claudetools/imported-conversations/auto-claude-variants/ac-plus/9a3122b3-253e-4cff-b84b-1757738fdb7f.jsonl

{"type":"queue-operation","operation":"dequeue","timestamp":"2026-01-12T18:53:42.593Z","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f"}
{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","type":"user","message":{"role":"user","content":"## YOUR ROLE - CODE IMPROVEMENTS IDEATION AGENT\n\nYou are the **Code Improvements Ideation Agent** in the Auto-Build framework. Your job is to discover code-revealed improvement opportunities by analyzing existing patterns, architecture, and infrastructure in the codebase.\n\n**Key Principle**: Find opportunities the code reveals. These are features and improvements that naturally emerge from understanding what patterns exist and how they can be extended, applied elsewhere, or scaled up.\n\n**Important**: This is NOT strategic product planning (that's Roadmap's job). Focus on what the CODE tells you is possible, not what users might want.\n\n---\n\n## YOUR CONTRACT\n\n**Input Files**:\n- `project_index.json` - Project structure and tech stack\n- `ideation_context.json` - Existing features, roadmap items, kanban tasks\n- `memory/codebase_map.json` (if exists) - Previously discovered file purposes\n- `memory/patterns.md` (if exists) - Established code patterns\n\n**Output**: `code_improvements_ideas.json` with code improvement ideas\n\nEach idea MUST have this structure:\n```json\n{\n  \"id\": \"ci-001\",\n  \"type\": \"code_improvements\",\n  \"title\": \"Short descriptive title\",\n  \"description\": \"What the feature/improvement does\",\n  \"rationale\": \"Why the code reveals this opportunity - what patterns enable it\",\n  \"builds_upon\": [\"Feature/pattern it extends\"],\n  \"estimated_effort\": \"trivial|small|medium|large|complex\",\n  \"affected_files\": [\"file1.ts\", \"file2.ts\"],\n  \"existing_patterns\": [\"Pattern to follow\"],\n  \"implementation_approach\": \"How to implement based on existing code\",\n  \"status\": \"draft\",\n  \"created_at\": \"ISO timestamp\"\n}\n```\n\n---\n\n## EFFORT LEVELS\n\nUnlike simple \"quick wins\", code improvements span all effort levels:\n\n| Level | Time | Description | Example |\n|-------|------|-------------|---------|\n| **trivial** | 1-2 hours | Direct copy with minor changes | Add search to list (search exists elsewhere) |\n| **small** | Half day | Clear pattern to follow, some new logic | Add new filter type using existing filter pattern |\n| **medium** | 1-3 days | Pattern exists but needs adaptation | New CRUD entity using existing CRUD patterns |\n| **large** | 3-7 days | Architectural pattern enables new capability | Plugin system using existing extension points |\n| **complex** | 1-2 weeks | Foundation supports major addition | Multi-tenant using existing data layer patterns |\n\n---\n\n## PHASE 0: LOAD CONTEXT\n\n```bash\n# Read project structure\ncat project_index.json\n\n# Read ideation context (existing features, planned items)\ncat ideation_context.json\n\n# Check for memory files\ncat memory/codebase_map.json 2>/dev/null || echo \"No codebase map yet\"\ncat memory/patterns.md 2>/dev/null || echo \"No patterns documented\"\n\n# Look at existing roadmap if available (to avoid duplicates)\ncat ../roadmap/roadmap.json 2>/dev/null | head -100 || echo \"No roadmap\"\n\n# Check for graph hints (historical insights from Graphiti)\ncat graph_hints.json 2>/dev/null || echo \"No graph hints available\"\n```\n\nUnderstand:\n- What is the project about?\n- What features already exist?\n- What patterns are established?\n- What is already planned (to avoid duplicates)?\n- What historical insights are available?\n\n### Graph Hints Integration\n\nIf `graph_hints.json` exists and contains hints for `code_improvements`, use them to:\n1. **Avoid duplicates**: Don't suggest ideas that have already been tried or rejected\n2. **Build on success**: Prioritize patterns that worked well in the past\n3. **Learn from failures**: Avoid approaches that previously caused issues\n4. **Leverage context**: Use historical file/pattern knowledge\n\n---\n\n## PHASE 1: DISCOVER EXISTING PATTERNS\n\nSearch for patterns that could be extended:\n\n```bash\n# Find similar components/modules that could be replicated\ngrep -r \"export function\\|export const\\|export class\" --include=\"*.ts\" --include=\"*.tsx\" . | head -40\n\n# Find existing API routes/endpoints\ngrep -r \"router\\.\\|app\\.\\|api/\\|/api\" --include=\"*.ts\" --include=\"*.py\" . | head -30\n\n# Find existing UI components\nls -la src/components/ 2>/dev/null || ls -la components/ 2>/dev/null\n\n# Find utility functions that could have more uses\ngrep -r \"export.*util\\|export.*helper\\|export.*format\" --include=\"*.ts\" . | head -20\n\n# Find existing CRUD operations\ngrep -r \"create\\|update\\|delete\\|get\\|list\" --include=\"*.ts\" --include=\"*.py\" . | head -30\n\n# Find existing hooks and reusable logic\ngrep -r \"use[A-Z]\" --include=\"*.ts\" --include=\"*.tsx\" . | head -20\n\n# Find existing middleware/interceptors\ngrep -r \"middleware\\|interceptor\\|handler\" --include=\"*.ts\" --include=\"*.py\" . | head -20\n```\n\nLook for:\n- Patterns that are repeated (could be extended)\n- Features that handle one case but could handle more\n- Utilities that could have additional methods\n- UI components that could have variants\n- Infrastructure that enables new capabilities\n\n---\n\n## PHASE 2: IDENTIFY OPPORTUNITY CATEGORIES\n\nThink about these opportunity types:\n\n### A. Pattern Extensions (trivial → medium)\n- Existing CRUD for one entity → CRUD for similar entity\n- Existing filter for one field → Filters for more fields\n- Existing sort by one column → Sort by multiple columns\n- Existing export to CSV → Export to JSON/Excel\n- Existing validation for one type → Validation for similar types\n\n### B. Architecture Opportunities (medium → complex)\n- Data model supports feature X with minimal changes\n- API structure enables new endpoint type\n- Component architecture supports new view/mode\n- State management pattern enables new features\n- Build system supports new output formats\n\n### C. Configuration/Settings (trivial → small)\n- Hard-coded values that could be user-configurable\n- Missing user preferences that follow existing preference patterns\n- Feature toggles that extend existing toggle patterns\n\n### D. Utility Additions (trivial → medium)\n- Existing validators that could validate more cases\n- Existing formatters that could handle more formats\n- Existing helpers that could have related helpers\n\n### E. UI Enhancements (trivial → medium)\n- Missing loading states that follow existing loading patterns\n- Missing empty states that follow existing empty state patterns\n- Missing error states that follow existing error patterns\n- Keyboard shortcuts that extend existing shortcut patterns\n\n### F. Data Handling (small → large)\n- Existing list views that could have pagination (if pattern exists)\n- Existing forms that could have auto-save (if pattern exists)\n- Existing data that could have search (if pattern exists)\n- Existing storage that could support new data types\n\n### G. Infrastructure Extensions (medium → complex)\n- Existing plugin points that aren't fully utilized\n- Existing event systems that could have new event types\n- Existing caching that could cache more data\n- Existing logging that could be extended\n\n---\n\n## PHASE 3: ANALYZE SPECIFIC OPPORTUNITIES\n\nFor each promising opportunity found:\n\n```bash\n# Examine the pattern file closely\ncat [file_path] | head -100\n\n# See how it's used\ngrep -r \"[function_name]\\|[component_name]\" --include=\"*.ts\" --include=\"*.tsx\" . | head -10\n\n# Check for related implementations\nls -la $(dirname [file_path])\n```\n\nFor each opportunity, deeply analyze:\n\n```\n<ultrathink>\nAnalyzing code improvement opportunity: [title]\n\nPATTERN DISCOVERY\n- Existing pattern found in: [file_path]\n- Pattern summary: [how it works]\n- Pattern maturity: [how well established, how many uses]\n\nEXTENSION OPPORTUNITY\n- What exactly would be added/changed?\n- What files would be affected?\n- What existing code can be reused?\n- What new code needs to be written?\n\nEFFORT ESTIMATION\n- Lines of code estimate: [number]\n- Test changes needed: [description]\n- Risk level: [low/medium/high]\n- Dependencies on other changes: [list]\n\nWHY THIS IS CODE-REVEALED\n- The pattern already exists in: [location]\n- The infrastructure is ready because: [reason]\n- Similar implementation exists for: [similar feature]\n\nEFFORT LEVEL: [trivial|small|medium|large|complex]\nJustification: [why this effort level]\n</ultrathink>\n```\n\n---\n\n## PHASE 4: FILTER AND PRIORITIZE\n\nFor each idea, verify:\n\n1. **Not Already Planned**: Check ideation_context.json for similar items\n2. **Pattern Exists**: The code pattern is already in the codebase\n3. **Infrastructure Ready**: Dependencies are already in place\n4. **Clear Implementation Path**: Can describe how to build it using existing patterns\n\nDiscard ideas that:\n- Require fundamentally new architectural patterns\n- Need significant research to understand approach\n- Are already in roadmap or kanban\n- Require strategic product decisions (those go to Roadmap)\n\n---\n\n## PHASE 5: GENERATE IDEAS (MANDATORY)\n\nGenerate 3-7 concrete code improvement ideas across different effort levels.\n\nAim for a mix:\n- 1-2 trivial/small (quick wins for momentum)\n- 2-3 medium (solid improvements)\n- 1-2 large/complex (bigger opportunities the code enables)\n\n---\n\n## PHASE 6: CREATE OUTPUT FILE (MANDATORY)\n\n**You MUST create code_improvements_ideas.json with your ideas.**\n\n```bash\ncat > code_improvements_ideas.json << 'EOF'\n{\n  \"code_improvements\": [\n    {\n      \"id\": \"ci-001\",\n      \"type\": \"code_improvements\",\n      \"title\": \"[Title]\",\n      \"description\": \"[What it does]\",\n      \"rationale\": \"[Why the code reveals this opportunity]\",\n      \"builds_upon\": [\"[Existing feature/pattern]\"],\n      \"estimated_effort\": \"[trivial|small|medium|large|complex]\",\n      \"affected_files\": [\"[file1.ts]\", \"[file2.ts]\"],\n      \"existing_patterns\": [\"[Pattern to follow]\"],\n      \"implementation_approach\": \"[How to implement using existing code]\",\n      \"status\": \"draft\",\n      \"created_at\": \"[ISO timestamp]\"\n    }\n  ]\n}\nEOF\n```\n\nVerify:\n```bash\ncat code_improvements_ideas.json\n```\n\n---\n\n## VALIDATION\n\nAfter creating ideas:\n\n1. Is it valid JSON?\n2. Does each idea have a unique id starting with \"ci-\"?\n3. Does each idea have builds_upon with at least one item?\n4. Does each idea have affected_files listing real files?\n5. Does each idea have existing_patterns?\n6. Is estimated_effort justified by the analysis?\n7. Does implementation_approach reference existing code?\n\n---\n\n## COMPLETION\n\nSignal completion:\n\n```\n=== CODE IMPROVEMENTS IDEATION COMPLETE ===\n\nIdeas Generated: [count]\n\nSummary by effort:\n- Trivial: [count]\n- Small: [count]\n- Medium: [count]\n- Large: [count]\n- Complex: [count]\n\nTop Opportunities:\n1. [title] - [effort] - extends [pattern]\n2. [title] - [effort] - extends [pattern]\n...\n\ncode_improvements_ideas.json created successfully.\n\nNext phase: [UI/UX or Complete]\n```\n\n---\n\n## CRITICAL RULES\n\n1. **ONLY suggest ideas with existing patterns** - If the pattern doesn't exist, it's not a code improvement\n2. **Be specific about affected files** - List the actual files that would change\n3. **Reference real patterns** - Point to actual code in the codebase\n4. **Avoid duplicates** - Check ideation_context.json first\n5. **No strategic/PM thinking** - Focus on what code reveals, not user needs analysis\n6. **Justify effort levels** - Each level should have clear reasoning\n7. **Provide implementation approach** - Show how existing code enables the improvement\n\n---\n\n## EXAMPLES OF GOOD CODE IMPROVEMENTS\n\n**Trivial:**\n- \"Add search to user list\" (search pattern exists in product list)\n- \"Add keyboard shortcut for save\" (shortcut system exists)\n\n**Small:**\n- \"Add CSV export\" (JSON export pattern exists)\n- \"Add dark mode to settings modal\" (dark mode exists elsewhere)\n\n**Medium:**\n- \"Add pagination to comments\" (pagination pattern exists for posts)\n- \"Add new filter type to dashboard\" (filter system is established)\n\n**Large:**\n- \"Add webhook support\" (event system exists, HTTP handlers exist)\n- \"Add bulk operations to admin panel\" (single operations exist, batch patterns exist)\n\n**Complex:**\n- \"Add multi-tenant support\" (data layer supports tenant_id, auth system can scope)\n- \"Add plugin system\" (extension points exist, dynamic loading infrastructure exists)\n\n## EXAMPLES OF BAD CODE IMPROVEMENTS (NOT CODE-REVEALED)\n\n- \"Add real-time collaboration\" (no WebSocket infrastructure exists)\n- \"Add AI-powered suggestions\" (no ML integration exists)\n- \"Add multi-language support\" (no i18n architecture exists)\n- \"Add feature X because users want it\" (that's Roadmap's job)\n- \"Improve user onboarding\" (product decision, not code-revealed)\n\n---\n\n## BEGIN\n\nStart by reading project_index.json and ideation_context.json, then search for patterns and opportunities across all effort levels.\n\n\n---\n\n**Output Directory**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\n**Project Directory**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\n**Max Ideas**: 5\n\n\n**Ideation Context**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\ideation_context.json\n**Project Index**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\project_index.json\n**Output File**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\code_improvements_ideas.json\n**Max Ideas**: 5\n\nGenerate up to 5 Code Improvements ideas.\nAvoid duplicating features that are already planned (see ideation_context.json).\nOutput your ideas to code_improvements_ideas.json.\n\n"},"uuid":"2b1197d9-fdbc-430a-8dea-7fe5cabee8ff","timestamp":"2026-01-12T18:53:43.350Z"}
{"parentUuid":"2b1197d9-fdbc-430a-8dea-7fe5cabee8ff","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","message":{"model":"claude-opus-4-5-20251101","id":"msg_01LAFiz6mTp4XZWfJayErwW9","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I need to act as the Code Improvements Ideation Agent. Let me start by reading the required input files to understand the project structure, existing features, and patterns.\n\nLet me follow the phases outlined:\n1. First, read project_index.json and ideation_context.json\n2. Check for memory files\n3. Search for patterns\n4. Generate ideas\n5. Create output file","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":3998,"cache_read_input_tokens":15912,"cache_creation":{"ephemeral_5m_input_tokens":3998,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zQXtX4bBbr6rWukkLr","type":"assistant","uuid":"5e9e969c-1d36-4851-8359-0cf1bb926b43","timestamp":"2026-01-12T18:53:46.753Z"}
{"parentUuid":"5e9e969c-1d36-4851-8359-0cf1bb926b43","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","message":{"model":"claude-opus-4-5-20251101","id":"msg_01LAFiz6mTp4XZWfJayErwW9","type":"message","role":"assistant","content":[{"type":"text","text":"I'll start by reading the project context files to understand the codebase and identify code improvement opportunities.\n\n## PHASE 0: LOAD CONTEXT"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":3998,"cache_read_input_tokens":15912,"cache_creation":{"ephemeral_5m_input_tokens":3998,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zQXtX4bBbr6rWukkLr","type":"assistant","uuid":"c5279374-c184-4440-a9d0-f8adba036691","timestamp":"2026-01-12T18:53:47.742Z"}
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{"parentUuid":"d8c45d92-41b5-4f1b-bbba-aa2acc3cdfa3","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_01GHgD8xoP3XbEGooA4gLbfg","type":"message","role":"assistant","content":[{"type":"text","text":"Let me check for memory files and explore the actual codebase structure:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1094,"cache_read_input_tokens":19910,"cache_creation":{"ephemeral_5m_input_tokens":1094,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zTunEQo9tNDta8wp1T","type":"assistant","uuid":"53a956ce-f6a4-46bf-a1dc-2bda725fc968","timestamp":"2026-01-12T18:54:32.356Z"}
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{"parentUuid":"bfd468c9-1d5f-48d9-81e7-b2bb02df5c29","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Q3KCZVw8bEBDMFErp5gNtd","type":"tool_result","content":"     1→# Specification: Multi-LLM Agentic Development Platform\n     2→\n     3→## Overview\n     4→\n     5→Build an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n     6→\n     7→## Workflow Type\n     8→\n     9→**Type**: Feature (Greenfield Development)\n    10→\n    11→**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n    12→\n    13→## Task Scope\n    14→\n    15→### Services Involved\n    16→\n    17→- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n    18→- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n    19→- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n    20→- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n    21→- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n    22→- **frontend-ui** (integration) - React/TypeScript project management dashboard\n    23→\n    24→### This Task Will:\n    25→\n    26→- [ ] Create Python/FastAPI backend with modular service architecture\n    27→- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n    28→- [ ] Build task orchestration engine with model-specific routing\n    29→- [ ] Implement cross-model validation for quality assurance\n    30→- [ ] Create adaptive spec system with interview and re-interview workflows\n    31→- [ ] Build extensible skill/addon plugin architecture\n    32→- [ ] Integrate Git/Gitea for version control automation\n    33→- [ ] Implement checkpoint system for project steering and course correction\n    34→- [ ] Create React/TypeScript frontend for project management\n    35→- [ ] Design database schema for specs, decisions, task history, model interactions\n    36→- [ ] Build MCP server for feature graph and skill management\n    37→\n    38→### Out of Scope:\n    39→\n    40→- Production deployment infrastructure (Kubernetes, cloud deployment)\n    41→- Enterprise SSO/SAML authentication\n    42→- Multi-tenant user management\n    43→- Billing and subscription management\n    44→- Mobile application\n    45→- Integration with CI/CD pipelines (beyond basic Git operations)\n    46→\n    47→## Service Context\n    48→\n    49→### Backend Orchestration Service\n    50→\n    51→**Tech Stack:**\n    52→- Language: Python 3.11+\n    53→- Framework: FastAPI with uvicorn ASGI server\n    54→- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n    55→- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n    56→\n    57→**Entry Point:** `backend/main.py`\n    58→\n    59→**How to Run:**\n    60→```bash\n    61→cd backend\n    62→pip install -r requirements.txt\n    63→uvicorn main:app --reload --port 8000\n    64→```\n    65→\n    66→**Port:** 8000\n    67→\n    68→**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n    69→\n    70→---\n    71→\n    72→### LLM Wrapper Service\n    73→\n    74→**Tech Stack:**\n    75→- Language: Python 3.11+\n    76→- Framework: LiteLLM for unified interface\n    77→- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n    78→- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n    79→\n    80→**Entry Point:** `backend/llm/__init__.py`\n    81→\n    82→**Configuration:**\n    83→```bash\n    84→# Environment variables required\n    85→ANTHROPIC_API_KEY=your_anthropic_key\n    86→GEMINI_API_KEY=your_gemini_key\n    87→XAI_API_KEY=your_xai_key\n    88→# Ollama uses local server, no API key needed\n    89→```\n    90→\n    91→---\n    92→\n    93→### Frontend UI\n    94→\n    95→**Tech Stack:**\n    96→- Language: TypeScript\n    97→- Framework: React with Vite\n    98→- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n    99→- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n   100→\n   101→**Entry Point:** `frontend/src/main.tsx`\n   102→\n   103→**How to Run:**\n   104→```bash\n   105→cd frontend\n   106→npm install\n   107→npm run dev\n   108→```\n   109→\n   110→**Port:** 5173\n   111→\n   112→---\n   113→\n   114→### MCP Server\n   115→\n   116→**Tech Stack:**\n   117→- Language: Python 3.11+\n   118→- Framework: MCP SDK (FastMCP)\n   119→- Key packages: `mcp>=1.25,<2`\n   120→- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n   121→\n   122→**Entry Point:** `mcp-server/server.py`\n   123→\n   124→**How to Run:**\n   125→```bash\n   126→cd mcp-server\n   127→python server.py\n   128→```\n   129→\n   130→## Files to Create\n   131→\n   132→| File | Service | Purpose |\n   133→|------|---------|---------|\n   134→| `backend/main.py` | orchestration | FastAPI application entry point |\n   135→| `backend/requirements.txt` | orchestration | Python dependencies |\n   136→| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n   137→| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n   138→| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n   139→| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n   140→| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n   141→| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n   142→| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n   143→| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n   144→| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n   145→| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n   146→| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n   147→| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n   148→| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n   149→| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n   150→| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n   151→| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n   152→| `backend/models/spec.py` | orchestration | Spec data model |\n   153→| `backend/models/task.py` | orchestration | Task data model |\n   154→| `backend/models/decision.py` | orchestration | Decision tracking model |\n   155→| `backend/git/git_service.py` | version-control | GitPython wrapper |\n   156→| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n   157→| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n   158→| `mcp-server/server.py` | mcp | FastMCP server entry |\n   159→| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n   160→| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n   161→| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n   162→| `frontend/package.json` | frontend | NPM dependencies |\n   163→| `frontend/vite.config.ts` | frontend | Vite configuration |\n   164→| `frontend/src/main.tsx` | frontend | React entry point |\n   165→| `frontend/src/App.tsx` | frontend | Main application component |\n   166→| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n   167→| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n   168→| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n   169→| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n   170→| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n   171→| `docker-compose.yml` | infrastructure | Container orchestration |\n   172→| `.env.example` | infrastructure | Environment variable template |\n   173→\n   174→## Patterns to Follow\n   175→\n   176→### LiteLLM Unified Interface Pattern\n   177→\n   178→Use LiteLLM as the unified API wrapper for all LLM providers:\n   179→\n   180→```python\n   181→from litellm import completion, acompletion\n   182→\n   183→# Unified interface - same code for any provider\n   184→async def call_llm(model: str, messages: list, **kwargs) -> str:\n   185→    response = await acompletion(\n   186→        model=model,  # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n   187→        messages=messages,\n   188→        **kwargs\n   189→    )\n   190→    return response.choices[0].message.content\n   191→```\n   192→\n   193→**Key Points:**\n   194→- Model names follow `provider/model` format for non-OpenAI models\n   195→- Built-in cost tracking via `litellm.track_cost = True`\n   196→- Automatic retries and fallbacks configurable\n   197→- Same interface works across 100+ LLM providers\n   198→\n   199→---\n   200→\n   201→### FastAPI Service Pattern\n   202→\n   203→```python\n   204→from fastapi import FastAPI, Depends, HTTPException\n   205→from pydantic import BaseModel\n   206→\n   207→app = FastAPI(title=\"Multi-LLM Platform\")\n   208→\n   209→class TaskCreate(BaseModel):\n   210→    description: str\n   211→    priority: str = \"normal\"\n   212→\n   213→@app.post(\"/tasks/\", response_model=Task)\n   214→async def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n   215→    \"\"\"Create a new task with automatic model routing.\"\"\"\n   216→    return await orchestrator.create_task(task, db)\n   217→```\n   218→\n   219→**Key Points:**\n   220→- Use Pydantic models for request/response validation\n   221→- Dependency injection for database sessions\n   222→- Async endpoints for non-blocking LLM calls\n   223→\n   224→---\n   225→\n   226→### Async LLM Client Pattern\n   227→\n   228→```python\n   229→from anthropic import AsyncAnthropic\n   230→from google import genai\n   231→from xai_sdk import AsyncClient as XAIClient\n   232→from ollama import AsyncClient as OllamaClient\n   233→\n   234→# All providers support async clients for concurrent operations\n   235→async def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n   236→    tasks = [call_llm(model, prompt) for model in models]\n   237→    responses = await asyncio.gather(*tasks, return_exceptions=True)\n   238→    return [r for r in responses if not isinstance(r, Exception)]\n   239→```\n   240→\n   241→---\n   242→\n   243→### MCP Server Pattern\n   244→\n   245→```python\n   246→from mcp.server.fastmcp import FastMCP\n   247→\n   248→mcp = FastMCP(\"multi-llm-platform\")\n   249→\n   250→@mcp.tool()\n   251→async def create_spec(description: str) -> dict:\n   252→    \"\"\"Create a new project specification.\"\"\"\n   253→    return await spec_service.create(description)\n   254→\n   255→@mcp.resource(\"project://state\")\n   256→async def get_project_state() -> str:\n   257→    \"\"\"Get current project state.\"\"\"\n   258→    return json.dumps(state_manager.get_state())\n   259→```\n   260→\n   261→---\n   262→\n   263→### Git Integration Pattern\n   264→\n   265→```python\n   266→from git import Repo\n   267→\n   268→def create_feature_branch(repo_path: str, feature_name: str) -> str:\n   269→    \"\"\"Create a feature branch following conventional naming.\"\"\"\n   270→    repo = Repo(repo_path)\n   271→    branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n   272→    new_branch = repo.create_head(branch_name)\n   273→    new_branch.checkout()\n   274→    return branch_name\n   275→\n   276→def commit_with_message(repo_path: str, files: list, message: str):\n   277→    \"\"\"Commit with conventional commit message format.\"\"\"\n   278→    repo = Repo(repo_path)\n   279→    repo.index.add(files)\n   280→    repo.index.commit(message)\n   281→```\n   282→\n   283→**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n   284→\n   285→## Requirements\n   286→\n   287→### Functional Requirements\n   288→\n   289→1. **Multi-LLM Provider Integration**\n   290→   - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n   291→   - Acceptance: Can make requests to all 4 providers and receive responses\n   292→\n   293→2. **Strategic Model Routing**\n   294→   - Description: Route tasks to optimal models based on task characteristics\n   295→   - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n   296→\n   297→3. **Cross-Model Validation**\n   298→   - Description: Have multiple models review work for quality assurance\n   299→   - Acceptance: Critical decisions validated by 2+ models before acceptance\n   300→\n   301→4. **Cost Optimization**\n   302→   - Description: Track and minimize API costs through intelligent model selection\n   303→   - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n   304→\n   305→5. **Adaptive Spec System**\n   306→   - Description: Interview workflow for gathering requirements, with re-interview capability\n   307→   - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n   308→\n   309→6. **Version Control Integration**\n   310→   - Description: Automatic branching, commit generation, PR creation\n   311→   - Acceptance: Git operations automated, conventional commit messages generated\n   312→\n   313→7. **Project Steering**\n   314→   - Description: Mid-flight direction changes with context preservation\n   315→   - Acceptance: Can redirect project without losing history, impact analysis visible\n   316→\n   317→8. **Skill Plugin System**\n   318→   - Description: Extensible architecture for specialized agent capabilities\n   319→   - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n   320→\n   321→### Non-Functional Requirements\n   322→\n   323→1. **Performance**\n   324→   - Parallel LLM calls for independent operations\n   325→   - Response time < 30s for most operations\n   326→   - Support concurrent users\n   327→\n   328→2. **Security**\n   329→   - API keys stored securely (environment variables, not code)\n   330→   - No credential logging\n   331→   - Input validation on all endpoints\n   332→\n   333→3. **Reliability**\n   334→   - Fallback strategies when models fail\n   335→   - Graceful degradation when providers unavailable\n   336→   - State persistence across restarts\n   337→\n   338→### Edge Cases\n   339→\n   340→1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n   341→2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n   342→3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n   343→4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n   344→5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n   345→6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n   346→7. **Git Conflicts** - Detect and surface for manual resolution\n   347→8. **API Key Invalid** - Mark provider as unavailable, notify user\n   348→\n   349→## Implementation Notes\n   350→\n   351→### DO\n   352→\n   353→- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n   354→- Use `google-genai` package, NOT deprecated `google-generativeai`\n   355→- Use official `xai-sdk` package, not community alternatives\n   356→- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n   357→- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n   358→- Use async clients for all LLM operations to enable concurrency\n   359→- Use `fastapi[standard]` to get uvicorn bundled\n   360→- Start Ollama server separately before using Python client\n   361→- Pull Ollama models first: `ollama pull gemma3`\n   362→\n   363→### DON'T\n   364→\n   365→- Don't use GitPython in long-running daemon processes (resource leaks)\n   366→- Don't hardcode API keys - use environment variables\n   367→- Don't assume all models available - always check provider status\n   368→- Don't block on single LLM call when parallel is possible\n   369→- Don't generate code using deprecated google-generativeai patterns\n   370→- Don't use `--reload` flag in production uvicorn\n   371→\n   372→## Development Environment\n   373→\n   374→### Prerequisites\n   375→\n   376→```bash\n   377→# Python 3.11+\n   378→python --version  # Should be 3.11+\n   379→\n   380→# Node.js 18+\n   381→node --version  # Should be 18+\n   382→\n   383→# Ollama (for local LLM)\n   384→# Download from https://ollama.com/download\n   385→ollama --version\n   386→```\n   387→\n   388→### Start Services\n   389→\n   390→```bash\n   391→# Terminal 1: Start Ollama server\n   392→ollama serve\n   393→\n   394→# Terminal 2: Pull models\n   395→ollama pull gemma3\n   396→ollama pull codellama\n   397→\n   398→# Terminal 3: Start backend\n   399→cd backend\n   400→pip install -r requirements.txt\n   401→uvicorn main:app --reload --port 8000\n   402→\n   403→# Terminal 4: Start frontend\n   404→cd frontend\n   405→npm install\n   406→npm run dev\n   407→\n   408→# Terminal 5: Start MCP server (optional)\n   409→cd mcp-server\n   410→python server.py\n   411→```\n   412→\n   413→### Service URLs\n   414→\n   415→- Backend API: http://localhost:8000\n   416→- API Docs: http://localhost:8000/docs\n   417→- Frontend: http://localhost:5173\n   418→- Ollama: http://localhost:11434\n   419→\n   420→### Required Environment Variables\n   421→\n   422→Create `.env` file from `.env.example`:\n   423→\n   424→```bash\n   425→# LLM Provider Keys\n   426→ANTHROPIC_API_KEY=sk-ant-...\n   427→GEMINI_API_KEY=AI...\n   428→XAI_API_KEY=xai-...\n   429→\n   430→# Ollama (optional, defaults shown)\n   431→OLLAMA_HOST=http://localhost:11434\n   432→\n   433→# Database\n   434→DATABASE_URL=sqlite:///./multi_llm_platform.db\n   435→\n   436→# Git/Gitea (optional)\n   437→GITEA_URL=http://localhost:3000\n   438→GITEA_TOKEN=your_gitea_token\n   439→```\n   440→\n   441→## Success Criteria\n   442→\n   443→The task is complete when:\n   444→\n   445→1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n   446→2. [ ] Demonstrate intelligent model routing (correct model for task type)\n   447→3. [ ] Cross-model validation working for critical decisions\n   448→4. [ ] Cost tracking visible and accurate in UI\n   449→5. [ ] Spec interview workflow functional (create, modify, version)\n   450→6. [ ] Git integration working (branch, commit, PR creation)\n   451→7. [ ] Project steering possible mid-flight with context preservation\n   452→8. [ ] Skill plugin system loading and executing skills\n   453→9. [ ] Frontend dashboard displaying project state\n   454→10. [ ] No console errors in frontend or backend\n   455→11. [ ] All unit and integration tests passing\n   456→\n   457→## QA Acceptance Criteria\n   458→\n   459→**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n   460→\n   461→### Unit Tests\n   462→\n   463→| Test | File | What to Verify |\n   464→|------|------|----------------|\n   465→| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n   466→| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n   467→| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n   468→| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n   469→| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n   470→| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n   471→\n   472→### Integration Tests\n   473→\n   474→| Test | Services | What to Verify |\n   475→|------|----------|----------------|\n   476→| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n   477→| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n   478→| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n   479→| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n   480→| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n   481→\n   482→### End-to-End Tests\n   483→\n   484→| Flow | Steps | Expected Outcome |\n   485→|------|-------|------------------|\n   486→| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n   487→| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n   488→| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n   489→| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n   490→| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n   491→\n   492→### Browser Verification\n   493→\n   494→| Page/Component | URL | Checks |\n   495→|----------------|-----|--------|\n   496→| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n   497→| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n   498→| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n   499→| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n   500→| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n   501→\n   502→### API Verification\n   503→\n   504→| Endpoint | Method | Checks |\n   505→|----------|--------|--------|\n   506→| `/health` | GET | Returns 200, all services healthy |\n   507→| `/api/models/status` | GET | Returns status for all 4 providers |\n   508→| `/api/tasks` | POST | Creates task, returns task ID |\n   509→| `/api/specs` | POST | Creates spec from interview data |\n   510→| `/api/specs/{id}/versions` | GET | Returns version history |\n   511→\n   512→### Database Verification\n   513→\n   514→| Check | Query/Command | Expected |\n   515→|-------|---------------|----------|\n   516→| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n   517→| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n   518→| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n   519→\n   520→### Security Verification\n   521→\n   522→| Check | Method | Expected |\n   523→|-------|--------|----------|\n   524→| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n   525→| Env vars loaded | Check startup logs | Keys loaded from environment |\n   526→| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n   527→\n   528→### QA Sign-off Requirements\n   529→\n   530→- [ ] All unit tests pass (`pytest backend/tests/`)\n   531→- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n   532→- [ ] All E2E tests pass (Playwright or manual)\n   533→- [ ] Browser verification complete (all pages load, no console errors)\n   534→- [ ] API verification complete (all endpoints respond correctly)\n   535→- [ ] Database state verified (tables exist, data persists)\n   536→- [ ] Security verification complete (no credential exposure)\n   537→- [ ] No regressions in existing functionality\n   538→- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n   539→- [ ] No security vulnerabilities introduced\n   540→- [ ] Cost tracking accurate within 5% of actual API costs\n   541→- [ ] All 4 LLM providers successfully integrated and responding\n   542→\n   543→---\n   544→\n   545→## Appendix: Package Versions\n   546→\n   547→### Python Requirements (requirements.txt)\n   548→\n   549→```\n   550→# Core Framework\n   551→fastapi[standard]>=0.115.0\n   552→uvicorn>=0.30.0\n   553→pydantic>=2.0.0\n   554→\n   555→# LLM Providers\n   556→litellm>=1.0.0\n   557→anthropic>=0.39.0\n   558→google-genai>=1.0.0\n   559→xai-sdk>=0.1.0\n   560→ollama>=0.4.0\n   561→\n   562→# MCP\n   563→mcp>=1.25,<2\n   564→\n   565→# Database\n   566→sqlalchemy>=2.0.0\n   567→alembic>=1.13.0\n   568→\n   569→# Git\n   570→GitPython>=3.1.40\n   571→py-gitea>=0.2.10\n   572→\n   573→# Utilities\n   574→httpx>=0.27.0\n   575→python-dotenv>=1.0.0\n   576→```\n   577→\n   578→### Node Requirements (package.json)\n   579→\n   580→```json\n   581→{\n   582→  \"dependencies\": {\n   583→    \"react\": \"^18.0.0\",\n   584→    \"react-dom\": \"^18.0.0\",\n   585→    \"@tanstack/react-query\": \"^5.0.0\"\n   586→  },\n   587→  \"devDependencies\": {\n   588→    \"typescript\": \"^5.0.0\",\n   589→    \"vite\": \"^6.0.0\",\n   590→    \"@types/react\": \"^18.0.0\",\n   591→    \"@types/react-dom\": \"^18.0.0\",\n   592→    \"vitest\": \"^2.0.0\",\n   593→    \"@testing-library/react\": \"^16.0.0\"\n   594→  }\n   595→}\n   596→```\n   597→\n   598→---\n   599→\n   600→## Appendix: Phased Implementation Milestones\n   601→\n   602→Based on complexity assessment, implement in phases:\n   603→\n   604→1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n   605→2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n   606→3. **Phase 3**: Spec management system and version control integration\n   607→4. **Phase 4**: Skill plugin system and cross-model validation\n   608→5. **Phase 5**: Frontend UI and project steering capabilities\n   609→6. **Phase 6**: Quality assurance, security hardening, and production readiness\n   610→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"8b424b70-308b-437b-a3f7-a5caeeaf8a7b","timestamp":"2026-01-12T18:55:39.512Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","content":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n    response = await acompletion(\n        model=model,  # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n        messages=messages,\n        **kwargs\n    )\n    return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n    description: str\n    priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n    \"\"\"Create a new task with automatic model routing.\"\"\"\n    return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n    tasks = [call_llm(model, prompt) for model in models]\n    responses = await asyncio.gather(*tasks, return_exceptions=True)\n    return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n    \"\"\"Create a new project specification.\"\"\"\n    return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n    \"\"\"Get current project state.\"\"\"\n    return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n    \"\"\"Create a feature branch following conventional naming.\"\"\"\n    repo = Repo(repo_path)\n    branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n    new_branch = repo.create_head(branch_name)\n    new_branch.checkout()\n    return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n    \"\"\"Commit with conventional commit message format.\"\"\"\n    repo = Repo(repo_path)\n    repo.index.add(files)\n    repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n   - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n   - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n   - Description: Route tasks to optimal models based on task characteristics\n   - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n   - Description: Have multiple models review work for quality assurance\n   - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n   - Description: Track and minimize API costs through intelligent model selection\n   - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n   - Description: Interview workflow for gathering requirements, with re-interview capability\n   - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n   - Description: Automatic branching, commit generation, PR creation\n   - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n   - Description: Mid-flight direction changes with context preservation\n   - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n   - Description: Extensible architecture for specialized agent capabilities\n   - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n   - Parallel LLM calls for independent operations\n   - Response time < 30s for most operations\n   - Support concurrent users\n\n2. **Security**\n   - API keys stored securely (environment variables, not code)\n   - No credential logging\n   - Input validation on all endpoints\n\n3. **Reliability**\n   - Fallback strategies when models fail\n   - Graceful degradation when providers unavailable\n   - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version  # Should be 3.11+\n\n# Node.js 18+\nnode --version  # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n  \"dependencies\": {\n    \"react\": \"^18.0.0\",\n    \"react-dom\": \"^18.0.0\",\n    \"@tanstack/react-query\": \"^5.0.0\"\n  },\n  \"devDependencies\": {\n    \"typescript\": \"^5.0.0\",\n    \"vite\": \"^6.0.0\",\n    \"@types/react\": \"^18.0.0\",\n    \"@types/react-dom\": \"^18.0.0\",\n    \"vitest\": \"^2.0.0\",\n    \"@testing-library/react\": \"^16.0.0\"\n  }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","numLines":610,"startLine":1,"totalLines":610}},"sourceToolAssistantUUID":"bfd468c9-1d5f-48d9-81e7-b2bb02df5c29"}
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Version Control Integration\\n  - Native Git and Gitea support\\n  - Automatic branching strategies for features/tasks\\n  - Commit message generation aligned with conventional commits\\n  - PR creation with comprehensive descriptions\\n  - Support for reviewing and iterating on code in branches\\n\\n  3. Adaptive Spec System\\n  - Initial spec-app style interview to gather requirements\\n  - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n    - Clarify ambiguous requirements that emerged during development\\n    - Gather feedback on implemented features\\n    - Adjust scope based on discoveries\\n    - Validate assumptions with the user\\n  - Spec versioning: track how requirements evolve over time\\n  - Continuous alignment: ensure implementation stays true to user intent\\n\\n  4. 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Intelligent Orchestration\\n  - Task decomposition with model-specific assignments\\n  - Parallel execution where possible\\n  - Dependency management between tasks\\n  - Progressive refinement: iterative improvement cycles\\n  - Fallback strategies when a model fails or produces poor results\\n\\n  7. Quality Assurance\\n  - Multi-model consensus for critical decisions\\n  - Automated testing integration\\n  - Code quality metrics and reporting\\n  - Security vulnerability scanning\\n  - Performance profiling\\n\\n  Technical Architecture\\n\\n  - Backend: Python (FastAPI) for orchestration layer\\n  - Frontend: React/TypeScript UI for project management and monitoring\\n  - Model Integration: Unified API wrapper supporting all providers\\n  - State Management: Track project state, agent activities, and decisions\\n  - Database: Store specs, decisions, task history, and model interactions\\n  - MCP Server: Feature graph and skill management\\n\\n  Key Differentiators\\n\\n  1. 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Transparency: Full visibility into which models did what and why\\n\\n  Use Cases\\n\\n  - Large greenfield projects with evolving requirements\\n  - Migrating/refactoring existing codebases with uncertain scope\\n  - Research projects where requirements emerge through exploration\\n  - Cost-sensitive projects that need to optimize token usage\\n  - High-stakes projects requiring multi-model validation\\n\\n  Success Criteria\\n\\n  - Successfully complete a full project lifecycle: spec → implementation → steering → completion\\n  - Demonstrate cost savings through intelligent model routing\\n  - Show quality improvements through cross-model validation\\n  - Handle mid-project requirement changes gracefully\\n  - Support at least 3 different LLM providers working in concert\",\n     4→  \"created_at\": \"2026-01-12T18:38:27.736Z\",\n     5→  \"updated_at\": \"2026-01-12T18:40:41.854Z\",\n     6→  \"status\": \"in_progress\",\n     7→  \"phases\": [],\n     8→  \"planStatus\": \"in_progress\"\n     9→}\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"aa3c96de-f24e-4b2b-9db5-7e835004790e","timestamp":"2026-01-12T18:55:39.513Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\implementation_plan.json","content":"{\n  \"feature\": \"Multi-LLM Agentic Development Platform\",\n  \"description\": \"Multi-LLM Agentic Development Platform\\n\\n  I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n  Core Features\\n\\n  1. 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Intelligent Orchestration\\n  - Task decomposition with model-specific assignments\\n  - Parallel execution where possible\\n  - Dependency management between tasks\\n  - Progressive refinement: iterative improvement cycles\\n  - Fallback strategies when a model fails or produces poor results\\n\\n  7. Quality Assurance\\n  - Multi-model consensus for critical decisions\\n  - Automated testing integration\\n  - Code quality metrics and reporting\\n  - Security vulnerability scanning\\n  - Performance profiling\\n\\n  Technical Architecture\\n\\n  - Backend: Python (FastAPI) for orchestration layer\\n  - Frontend: React/TypeScript UI for project management and monitoring\\n  - Model Integration: Unified API wrapper supporting all providers\\n  - State Management: Track project state, agent activities, and decisions\\n  - Database: Store specs, decisions, task history, and model interactions\\n  - MCP Server: Feature graph and skill management\\n\\n  Key Differentiators\\n\\n  1. 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Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n  2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n  3. Local-first option: Can run meaningful work without API costs via Ollama\\n  4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n  5. Transparency: Full visibility into which models did what and why\\n\\n  Use Cases\\n\\n  - Large greenfield projects with evolving requirements\\n  - Migrating/refactoring existing codebases with uncertain scope\\n  - Research projects where requirements emerge through exploration\\n  - Cost-sensitive projects that need to optimize token usage\\n  - High-stakes projects requiring multi-model validation\\n\\n  Success Criteria\\n\\n  - Successfully complete a full project lifecycle: spec → implementation → steering → completion\\n  - Demonstrate cost savings through intelligent model routing\\n  - Show quality improvements through cross-model validation\\n  - Handle mid-project requirement changes gracefully\\n  - Support at least 3 different LLM providers working in concert\",\n  \"workflow_type\": \"refactoring\"\n}","numLines":4,"startLine":1,"totalLines":4}},"sourceToolAssistantUUID":"159e3859-0235-410e-881b-c32d3f35a761"}
{"parentUuid":"081a9c6a-ec72-45e2-9792-2eba448be1c8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_01NgLjDPbriqYjNi1nZVhx9f","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"This is a greenfield project for building a Multi-LLM Agentic Development Platform. The spec.md outlines a comprehensive architecture with:\n\n1. Backend: Python/FastAPI\n2. Frontend: React/TypeScript\n3. LLM Wrapper: LiteLLM with multiple providers\n4. MCP Server\n\nHowever, the actual codebase is essentially empty - no Python or TypeScript files exist yet. The spec.md describes the PLANNED architecture and patterns.\n\nThis is a challenging situation for \"code improvements\" ideation because:\n- Code improvements should be based on existing patterns that can be extended\n- This project hasn't been implemented yet - it's a greenfield spec\n\nGiven this situation, I need to think carefully about what \"code improvements\" means in this context:\n\n1. The spec defines many patterns that WILL exist - I could identify improvements to the planned patterns before implementation\n2. The .auto-claude framework itself may have patterns to analyze\n\nLet me check the .auto-claude directory structure more closely - this might be where patterns exist that can be extended.","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":11219,"cache_read_input_tokens":23762,"cache_creation":{"ephemeral_5m_input_tokens":11219,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zZCDauu8VCyPrcFQBF","type":"assistant","uuid":"1a6d0a62-17b4-47e3-b4ed-34a4c25875fc","timestamp":"2026-01-12T18:55:50.236Z"}
{"parentUuid":"1a6d0a62-17b4-47e3-b4ed-34a4c25875fc","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_01NgLjDPbriqYjNi1nZVhx9f","type":"message","role":"assistant","content":[{"type":"text","text":"Let me examine the `.auto-claude` structure to understand what patterns may already exist in the framework:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":11219,"cache_read_input_tokens":23762,"cache_creation":{"ephemeral_5m_input_tokens":11219,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zZCDauu8VCyPrcFQBF","type":"assistant","uuid":"65cde099-d9a0-42e8-bc66-3d9744367bff","timestamp":"2026-01-12T18:55:50.857Z"}
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{"parentUuid":"6f5549cb-cfe4-4818-845d-937a61e42d9f","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_01NgLjDPbriqYjNi1nZVhx9f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012JJ8AG82Lk49J1q8VcZxgX","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\complexity_assessment.json"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":11219,"cache_read_input_tokens":23762,"cache_creation":{"ephemeral_5m_input_tokens":11219,"ephemeral_1h_input_tokens":0},"output_tokens":521,"service_tier":"standard"}},"requestId":"req_011CX3zZCDauu8VCyPrcFQBF","type":"assistant","uuid":"793da32e-60d3-4bfd-b10a-94d0c3c57d22","timestamp":"2026-01-12T18:55:54.427Z"}
{"parentUuid":"51fedab2-1fd8-4d57-81ed-eb7e384cbdbc","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Es7WBihrvR4BV1ph485Jt4","type":"tool_result","content":"     1→{\n     2→  \"project_root\": \"C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\",\n     3→  \"project_type\": \"single\",\n     4→  \"services\": {},\n     5→  \"infrastructure\": {},\n     6→  \"conventions\": {}\n     7→}\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"c9cb8a5b-05a3-4715-b32a-26a072ab00e8","timestamp":"2026-01-12T18:56:39.850Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\project_index.json","content":"{\n  \"project_root\": \"C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\",\n  \"project_type\": \"single\",\n  \"services\": {},\n  \"infrastructure\": {},\n  \"conventions\": {}\n}","numLines":7,"startLine":1,"totalLines":7}},"sourceToolAssistantUUID":"51fedab2-1fd8-4d57-81ed-eb7e384cbdbc"}
{"parentUuid":"6f5549cb-cfe4-4818-845d-937a61e42d9f","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UvYVaXMCcwXQTuWMmgVbpZ","type":"tool_result","content":"     1→{\n     2→  \"task_description\": \"Multi-LLM Agentic Development Platform\\n\\n  I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n  Core Features\\n\\n  1. Multi-LLM Provider Support\\n  - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\\n  - Strategic model assignment - route tasks to models best suited for them:\\n    - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\\n    - Claude Sonnet/Opus for complex reasoning and architecture\\n    - Gemini for multimodal analysis and large context windows\\n    - Grok for real-time information and alternative perspectives\\n  - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\\n  - Cost optimization: intelligently balance between cloud and local models\\n\\n  2. Version Control Integration\\n  - Native Git and Gitea support\\n  - Automatic branching strategies for features/tasks\\n  - Commit message generation aligned with conventional commits\\n  - PR creation with comprehensive descriptions\\n  - Support for reviewing and iterating on code in branches\\n\\n  3. Adaptive Spec System\\n  - Initial spec-app style interview to gather requirements\\n  - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n    - Clarify ambiguous requirements that emerged during development\\n    - Gather feedback on implemented features\\n    - Adjust scope based on discoveries\\n    - Validate assumptions with the user\\n  - Spec versioning: track how requirements evolve over time\\n  - Continuous alignment: ensure implementation stays true to user intent\\n\\n  4. Agent Skill System\\n  - Extensible skill/addon architecture for specialized agent capabilities\\n  - Skills can be model-specific or model-agnostic\\n  - Examples: code review, security scanning, performance optimization, documentation generation\\n  - Custom skill creation framework\\n  - Skill composition: chain skills together for complex workflows\\n\\n  5. Project Steering & Course Correction\\n  - Mid-flight adjustments: ability to redirect project direction after work has begun\\n  - Checkpoint system: review progress at milestones and adjust plans\\n  - Context preservation: maintain full project history when steering changes\\n  - Impact analysis: understand implications of direction changes\\n  - Graceful pivoting: refactor or adapt existing work rather than starting over\\n\\n  6. Intelligent Orchestration\\n  - Task decomposition with model-specific assignments\\n  - Parallel execution where possible\\n  - Dependency management between tasks\\n  - Progressive refinement: iterative improvement cycles\\n  - Fallback strategies when a model fails or produces poor results\\n\\n  7. Quality Assurance\\n  - Multi-model consensus for critical decisions\\n  - Automated testing integration\\n  - Code quality metrics and reporting\\n  - Security vulnerability scanning\\n  - Performance profiling\\n\\n  Technical Architecture\\n\\n  - Backend: Python (FastAPI) for orchestration layer\\n  - Frontend: React/TypeScript UI for project management and monitoring\\n  - Model Integration: Unified API wrapper supporting all providers\\n  - State Management: Track project state, agent activities, and decisions\\n  - Database: Store specs, decisions, task history, and model interactions\\n  - MCP Server: Feature graph and skill management\\n\\n  Key Differentiators\\n\\n  1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n  2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n  3. Local-first option: Can run meaningful work without API costs via Ollama\\n  4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n  5. Transparency: Full visibility into which models did what and why\\n\\n  Use Cases\\n\\n  - Large greenfield projects with evolving requirements\\n  - Migrating/refactoring existing codebases with uncertain scope\\n  - Research projects where requirements emerge through exploration\\n  - Cost-sensitive projects that need to optimize token usage\\n  - High-stakes projects requiring multi-model validation\\n\\n  Success Criteria\\n\\n  - Successfully complete a full project lifecycle: spec \\u2192 implementation \\u2192 steering \\u2192 completion\\n  - Demonstrate cost savings through intelligent model routing\\n  - Show quality improvements through cross-model validation\\n  - Handle mid-project requirement changes gracefully\\n  - Support at least 3 different LLM providers working in concert\",\n     3→  \"scoped_services\": [],\n     4→  \"files_to_modify\": [],\n     5→  \"files_to_reference\": [],\n     6→  \"created_at\": \"2026-01-12T11:50:41.978005\"\n     7→}\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"1b54fbe2-480e-4c1b-bf76-66f6d215cc5d","timestamp":"2026-01-12T18:56:39.850Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\context.json","content":"{\n  \"task_description\": \"Multi-LLM Agentic Development Platform\\n\\n  I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n  Core Features\\n\\n  1. Multi-LLM Provider Support\\n  - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\\n  - Strategic model assignment - route tasks to models best suited for them:\\n    - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\\n    - Claude Sonnet/Opus for complex reasoning and architecture\\n    - Gemini for multimodal analysis and large context windows\\n    - Grok for real-time information and alternative perspectives\\n  - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\\n  - Cost optimization: intelligently balance between cloud and local models\\n\\n  2. Version Control Integration\\n  - Native Git and Gitea support\\n  - Automatic branching strategies for features/tasks\\n  - Commit message generation aligned with conventional commits\\n  - PR creation with comprehensive descriptions\\n  - Support for reviewing and iterating on code in branches\\n\\n  3. Adaptive Spec System\\n  - Initial spec-app style interview to gather requirements\\n  - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n    - Clarify ambiguous requirements that emerged during development\\n    - Gather feedback on implemented features\\n    - Adjust scope based on discoveries\\n    - Validate assumptions with the user\\n  - Spec versioning: track how requirements evolve over time\\n  - Continuous alignment: ensure implementation stays true to user intent\\n\\n  4. Agent Skill System\\n  - Extensible skill/addon architecture for specialized agent capabilities\\n  - Skills can be model-specific or model-agnostic\\n  - Examples: code review, security scanning, performance optimization, documentation generation\\n  - Custom skill creation framework\\n  - Skill composition: chain skills together for complex workflows\\n\\n  5. Project Steering & Course Correction\\n  - Mid-flight adjustments: ability to redirect project direction after work has begun\\n  - Checkpoint system: review progress at milestones and adjust plans\\n  - Context preservation: maintain full project history when steering changes\\n  - Impact analysis: understand implications of direction changes\\n  - Graceful pivoting: refactor or adapt existing work rather than starting over\\n\\n  6. Intelligent Orchestration\\n  - Task decomposition with model-specific assignments\\n  - Parallel execution where possible\\n  - Dependency management between tasks\\n  - Progressive refinement: iterative improvement cycles\\n  - Fallback strategies when a model fails or produces poor results\\n\\n  7. Quality Assurance\\n  - Multi-model consensus for critical decisions\\n  - Automated testing integration\\n  - Code quality metrics and reporting\\n  - Security vulnerability scanning\\n  - Performance profiling\\n\\n  Technical Architecture\\n\\n  - Backend: Python (FastAPI) for orchestration layer\\n  - Frontend: React/TypeScript UI for project management and monitoring\\n  - Model Integration: Unified API wrapper supporting all providers\\n  - State Management: Track project state, agent activities, and decisions\\n  - Database: Store specs, decisions, task history, and model interactions\\n  - MCP Server: Feature graph and skill management\\n\\n  Key Differentiators\\n\\n  1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n  2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n  3. Local-first option: Can run meaningful work without API costs via Ollama\\n  4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n  5. Transparency: Full visibility into which models did what and why\\n\\n  Use Cases\\n\\n  - Large greenfield projects with evolving requirements\\n  - Migrating/refactoring existing codebases with uncertain scope\\n  - Research projects where requirements emerge through exploration\\n  - Cost-sensitive projects that need to optimize token usage\\n  - High-stakes projects requiring multi-model validation\\n\\n  Success Criteria\\n\\n  - Successfully complete a full project lifecycle: spec \\u2192 implementation \\u2192 steering \\u2192 completion\\n  - Demonstrate cost savings through intelligent model routing\\n  - Show quality improvements through cross-model validation\\n  - Handle mid-project requirement changes gracefully\\n  - Support at least 3 different LLM providers working in concert\",\n  \"scoped_services\": [],\n  \"files_to_modify\": [],\n  \"files_to_reference\": [],\n  \"created_at\": \"2026-01-12T11:50:41.978005\"\n}","numLines":7,"startLine":1,"totalLines":7}},"sourceToolAssistantUUID":"6f5549cb-cfe4-4818-845d-937a61e42d9f"}
{"parentUuid":"793da32e-60d3-4bfd-b10a-94d0c3c57d22","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012JJ8AG82Lk49J1q8VcZxgX","type":"tool_result","content":"     1→{\n     2→  \"complexity\": \"complex\",\n     3→  \"workflow_type\": \"feature\",\n     4→  \"confidence\": 0.95,\n     5→  \"reasoning\": \"This is a large greenfield multi-LLM platform requiring 4+ external LLM provider integrations (Claude, Gemini, Grok, Ollama), Git/Gitea integration, full-stack development (FastAPI backend + React frontend), database schema design, and new architectural patterns for multi-model orchestration. The project index shows no existing patterns to leverage - this is building from scratch.\",\n     6→\n     7→  \"analysis\": {\n     8→    \"scope\": {\n     9→      \"estimated_files\": 75,\n    10→      \"estimated_services\": 5,\n    11→      \"is_cross_cutting\": true,\n    12→      \"notes\": \"Core services: orchestration layer, unified LLM wrapper, spec management, skill system, version control integration. Plus frontend components, database models, API routes, utilities, and configuration.\"\n    13→    },\n    14→    \"integrations\": {\n    15→      \"external_services\": [\"Claude API\", \"Gemini API\", \"Grok API\", \"Ollama\", \"Git\", \"Gitea\"],\n    16→      \"new_dependencies\": [\"fastapi\", \"litellm\", \"sqlalchemy\", \"react\", \"typescript\", \"gitpython\", \"pydantic\", \"httpx\"],\n    17→      \"research_needed\": true,\n    18→      \"notes\": \"Each LLM provider has different API patterns. Multi-model orchestration and cross-validation are novel patterns requiring careful design. Git/Gitea automation needs research.\"\n    19→    },\n    20→    \"infrastructure\": {\n    21→      \"docker_changes\": true,\n    22→      \"database_changes\": true,\n    23→      \"config_changes\": true,\n    24→      \"notes\": \"Need Docker for backend/frontend/database containerization. Database schema for specs, decisions, task history, model interactions. Multiple .env variables for API keys and model configurations.\"\n    25→    },\n    26→    \"knowledge\": {\n    27→      \"patterns_exist\": false,\n    28→      \"research_required\": true,\n    29→      \"unfamiliar_tech\": [\"multi-model orchestration\", \"cross-model validation\", \"litellm or unified LLM wrapper\", \"MCP server development\", \"adaptive spec systems\"],\n    30→      \"notes\": \"Project index shows empty project - no existing codebase patterns. Multi-model orchestration is a novel architectural pattern. Need to research each provider's API, rate limits, and capabilities.\"\n    31→    },\n    32→    \"risk\": {\n    33→      \"level\": \"high\",\n    34→      \"concerns\": [\n    35→        \"API key management for 4+ providers\",\n    36→        \"Cost control across multiple paid APIs\",\n    37→        \"Concurrent execution and race conditions in orchestration\",\n    38→        \"State management complexity across parallel model calls\",\n    39→        \"Cross-model validation logic and consensus mechanisms\",\n    40→        \"Fallback strategies when models fail\",\n    41→        \"Security of credential storage\",\n    42→        \"Performance optimization for parallel API calls\"\n    43→      ],\n    44→      \"notes\": \"Complex distributed system with multiple external dependencies. Need careful error handling, retry logic, and graceful degradation.\"\n    45→    }\n    46→  },\n    47→\n    48→  \"recommended_phases\": [\n    49→    \"discovery\",\n    50→    \"requirements\",\n    51→    \"research\",\n    52→    \"context\",\n    53→    \"spec_writing\",\n    54→    \"self_critique\",\n    55→    \"planning\",\n    56→    \"validation\"\n    57→  ],\n    58→\n    59→  \"flags\": {\n    60→    \"needs_research\": true,\n    61→    \"needs_self_critique\": true,\n    62→    \"needs_infrastructure_setup\": true\n    63→  },\n    64→\n    65→  \"validation_recommendations\": {\n    66→    \"risk_level\": \"high\",\n    67→    \"skip_validation\": false,\n    68→    \"minimal_mode\": false,\n    69→    \"test_types_required\": [\"unit\", \"integration\", \"e2e\"],\n    70→    \"security_scan_required\": true,\n    71→    \"staging_deployment_required\": false,\n    72→    \"reasoning\": \"Multi-provider API integration with credential handling requires comprehensive testing. Security scan needed for API key management and authentication flows. No staging deployment since this is greenfield - local Docker environment sufficient for initial validation.\"\n    73→  },\n    74→\n    75→  \"additional_notes\": {\n    76→    \"workflow_type_clarification\": \"Marked as 'feature' rather than 'refactoring' because the project index shows an empty project with no existing code to refactor. This is greenfield development building new functionality from scratch, even though it's inspired by existing Autocoder 2.0/Auto-Claude concepts.\",\n    77→    \"phased_implementation_recommended\": true,\n    78→    \"suggested_milestones\": [\n    79→      \"Phase 1: Core orchestration layer with single LLM provider\",\n    80→      \"Phase 2: Add additional LLM providers with unified wrapper\",\n    81→      \"Phase 3: Spec management and version control integration\",\n    82→      \"Phase 4: Skill system and cross-model validation\",\n    83→      \"Phase 5: Frontend UI and project steering\",\n    84→      \"Phase 6: Quality assurance and production hardening\"\n    85→    ]\n    86→  },\n    87→\n    88→  \"created_at\": \"2025-01-12T12:00:00.000Z\"\n    89→}\n    90→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"11e40d41-f0e1-40ed-a369-8205be644130","timestamp":"2026-01-12T18:56:39.850Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\complexity_assessment.json","content":"{\n  \"complexity\": \"complex\",\n  \"workflow_type\": \"feature\",\n  \"confidence\": 0.95,\n  \"reasoning\": \"This is a large greenfield multi-LLM platform requiring 4+ external LLM provider integrations (Claude, Gemini, Grok, Ollama), Git/Gitea integration, full-stack development (FastAPI backend + React frontend), database schema design, and new architectural patterns for multi-model orchestration. The project index shows no existing patterns to leverage - this is building from scratch.\",\n\n  \"analysis\": {\n    \"scope\": {\n      \"estimated_files\": 75,\n      \"estimated_services\": 5,\n      \"is_cross_cutting\": true,\n      \"notes\": \"Core services: orchestration layer, unified LLM wrapper, spec management, skill system, version control integration. Plus frontend components, database models, API routes, utilities, and configuration.\"\n    },\n    \"integrations\": {\n      \"external_services\": [\"Claude API\", \"Gemini API\", \"Grok API\", \"Ollama\", \"Git\", \"Gitea\"],\n      \"new_dependencies\": [\"fastapi\", \"litellm\", \"sqlalchemy\", \"react\", \"typescript\", \"gitpython\", \"pydantic\", \"httpx\"],\n      \"research_needed\": true,\n      \"notes\": \"Each LLM provider has different API patterns. Multi-model orchestration and cross-validation are novel patterns requiring careful design. Git/Gitea automation needs research.\"\n    },\n    \"infrastructure\": {\n      \"docker_changes\": true,\n      \"database_changes\": true,\n      \"config_changes\": true,\n      \"notes\": \"Need Docker for backend/frontend/database containerization. Database schema for specs, decisions, task history, model interactions. Multiple .env variables for API keys and model configurations.\"\n    },\n    \"knowledge\": {\n      \"patterns_exist\": false,\n      \"research_required\": true,\n      \"unfamiliar_tech\": [\"multi-model orchestration\", \"cross-model validation\", \"litellm or unified LLM wrapper\", \"MCP server development\", \"adaptive spec systems\"],\n      \"notes\": \"Project index shows empty project - no existing codebase patterns. Multi-model orchestration is a novel architectural pattern. Need to research each provider's API, rate limits, and capabilities.\"\n    },\n    \"risk\": {\n      \"level\": \"high\",\n      \"concerns\": [\n        \"API key management for 4+ providers\",\n        \"Cost control across multiple paid APIs\",\n        \"Concurrent execution and race conditions in orchestration\",\n        \"State management complexity across parallel model calls\",\n        \"Cross-model validation logic and consensus mechanisms\",\n        \"Fallback strategies when models fail\",\n        \"Security of credential storage\",\n        \"Performance optimization for parallel API calls\"\n      ],\n      \"notes\": \"Complex distributed system with multiple external dependencies. Need careful error handling, retry logic, and graceful degradation.\"\n    }\n  },\n\n  \"recommended_phases\": [\n    \"discovery\",\n    \"requirements\",\n    \"research\",\n    \"context\",\n    \"spec_writing\",\n    \"self_critique\",\n    \"planning\",\n    \"validation\"\n  ],\n\n  \"flags\": {\n    \"needs_research\": true,\n    \"needs_self_critique\": true,\n    \"needs_infrastructure_setup\": true\n  },\n\n  \"validation_recommendations\": {\n    \"risk_level\": \"high\",\n    \"skip_validation\": false,\n    \"minimal_mode\": false,\n    \"test_types_required\": [\"unit\", \"integration\", \"e2e\"],\n    \"security_scan_required\": true,\n    \"staging_deployment_required\": false,\n    \"reasoning\": \"Multi-provider API integration with credential handling requires comprehensive testing. Security scan needed for API key management and authentication flows. No staging deployment since this is greenfield - local Docker environment sufficient for initial validation.\"\n  },\n\n  \"additional_notes\": {\n    \"workflow_type_clarification\": \"Marked as 'feature' rather than 'refactoring' because the project index shows an empty project with no existing code to refactor. This is greenfield development building new functionality from scratch, even though it's inspired by existing Autocoder 2.0/Auto-Claude concepts.\",\n    \"phased_implementation_recommended\": true,\n    \"suggested_milestones\": [\n      \"Phase 1: Core orchestration layer with single LLM provider\",\n      \"Phase 2: Add additional LLM providers with unified wrapper\",\n      \"Phase 3: Spec management and version control integration\",\n      \"Phase 4: Skill system and cross-model validation\",\n      \"Phase 5: Frontend UI and project steering\",\n      \"Phase 6: Quality assurance and production hardening\"\n    ]\n  },\n\n  \"created_at\": \"2025-01-12T12:00:00.000Z\"\n}\n","numLines":90,"startLine":1,"totalLines":90}},"sourceToolAssistantUUID":"793da32e-60d3-4bfd-b10a-94d0c3c57d22"}
{"parentUuid":"11e40d41-f0e1-40ed-a369-8205be644130","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_014vafc5NBm7yxCQbkAMWfpW","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear picture. This is a GREENFIELD project with:\n- No existing code - it's empty\n- A detailed spec defining patterns to be implemented\n- Complexity assessment showing it's a \"complex\" project\n\nFor code improvements, I'm in an unusual situation: there's no code yet to discover patterns from. The spec.md describes FUTURE patterns, but nothing is implemented.\n\nHowever, I can look at this from a different angle:\n1. The `.auto-claude` framework files themselves may have patterns\n2. The spec describes planned patterns that could be analyzed for improvement opportunities\n\nGiven the nature of this project (greenfield, no code), I should:\n1. Acknowledge that code improvements traditionally require existing code patterns\n2. Look at the spec patterns and identify foundational improvements that should be built into the initial implementation\n\nActually, re-reading my instructions:\n- \"Code improvements\" should be based on what the CODE reveals\n- Without code, I cannot suggest code improvements\n\nBut wait - the spec describes many patterns that will exist. I can identify:\n1. Pattern improvements to build into the initial implementation\n2. Architecture decisions that the spec enables\n\nLet me frame this as: \"Given the specified architecture and patterns, what improvements should be built in from day one?\"\n\nThis is legitimate because:\n- The spec defines patterns (LiteLLM, FastAPI patterns, MCP patterns)\n- These patterns exist in the broader ecosystem\n- I can identify improvements based on how these patterns are typically extended\n\nLet me create code improvement ideas based on the PLANNED patterns that the spec reveals:","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3539,"cache_read_input_tokens":34981,"cache_creation":{"ephemeral_5m_input_tokens":3539,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard"}},"requestId":"req_011CX3zdeENeeQxMTX1FPk9G","type":"assistant","uuid":"5a342340-95a4-4d1e-9e05-6b1cbc198142","timestamp":"2026-01-12T18:56:52.981Z"}
{"parentUuid":"5a342340-95a4-4d1e-9e05-6b1cbc198142","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_014vafc5NBm7yxCQbkAMWfpW","type":"message","role":"assistant","content":[{"type":"text","text":"Based on my analysis, I can see this is a **greenfield project** with a comprehensive spec but no implemented code yet. The spec.md describes planned patterns that will exist (LiteLLM wrapper, FastAPI services, MCP server, etc.).\n\nFor code improvements ideation on a greenfield project, I'll focus on **improvements to build into the initial implementation** based on the specified patterns and architecture. Let me examine the planned patterns more closely:\n\n## PHASE 1-3: PATTERN ANALYSIS\n\nBased on the spec, I can identify these established patterns that will exist:\n1. **LiteLLM Unified Interface** - Pattern for multi-provider LLM calls\n2. **FastAPI Service Pattern** - REST API with Pydantic models\n3. **Async LLM Client Pattern** - Concurrent model calls\n4. **MCP Server Pattern** - FastMCP for tools/resources\n5. **Git Integration Pattern** - GitPython for version control\n\nNow let me create the code improvements ideas file based on what the architectural patterns reveal as opportunities:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3539,"cache_read_input_tokens":34981,"cache_creation":{"ephemeral_5m_input_tokens":3539,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard"}},"requestId":"req_011CX3zdeENeeQxMTX1FPk9G","type":"assistant","uuid":"0da33b0b-e2be-4440-9b82-38feb318e076","timestamp":"2026-01-12T18:56:58.540Z"}
{"parentUuid":"0da33b0b-e2be-4440-9b82-38feb318e076","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_014vafc5NBm7yxCQbkAMWfpW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SridS9ssQgom1sKpJVCW6T","name":"Write","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\code_improvements_ideas.json","content":"{\n  \"code_improvements\": [\n    {\n      \"id\": \"ci-001\",\n      \"type\": \"code_improvements\",\n      \"title\": \"LLM Response Caching Layer\",\n      \"description\": \"Add a response caching layer to the unified LLM client that caches identical prompts to reduce API costs and improve response times. Use content-addressable hashing for cache keys.\",\n      \"rationale\": \"The LiteLLM unified interface pattern supports middleware/interceptors. Since cost optimization is a core requirement and identical prompts to the same model will return similar responses, caching is a natural extension of the existing unified client pattern.\",\n      \"builds_upon\": [\"LiteLLM Unified Interface Pattern\", \"Cost Tracking System\"],\n      \"estimated_effort\": \"small\",\n      \"affected_files\": [\"backend/llm/unified_client.py\", \"backend/llm/cache/response_cache.py\", \"backend/llm/routing/cost_tracker.py\"],\n      \"existing_patterns\": [\"LiteLLM completion wrapper\", \"Cost tracking middleware\"],\n      \"implementation_approach\": \"Add a caching decorator/middleware to the acompletion wrapper function. Use hashlib to create cache keys from model+messages+key params. Store in SQLite or Redis. Check cache before API call, store response after. Integrate with cost_tracker to show cache hit savings.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    },\n    {\n      \"id\": \"ci-002\",\n      \"type\": \"code_improvements\",\n      \"title\": \"Provider Health Check Endpoint with Auto-Routing Fallback\",\n      \"description\": \"Extend the /api/models/status endpoint to include latency metrics and automatically update routing weights. When a provider is slow or unhealthy, automatically route tasks to alternatives.\",\n      \"rationale\": \"The spec defines strategic model routing and fallback strategies. The provider status endpoint pattern can be extended to feed health data back into the router, creating a self-healing system that follows the existing routing pattern.\",\n      \"builds_upon\": [\"Model Status Endpoint\", \"Task-to-Model Router\"],\n      \"estimated_effort\": \"medium\",\n      \"affected_files\": [\"backend/api/routes/models.py\", \"backend/llm/routing/router.py\", \"backend/llm/providers/health_monitor.py\"],\n      \"existing_patterns\": [\"FastAPI dependency injection\", \"Async provider clients\", \"Router task assignment\"],\n      \"implementation_approach\": \"Create HealthMonitor service that periodically pings each provider with test prompts. Store latency/success metrics. Expose via /api/models/status with response times. Inject health data into router to adjust routing weights dynamically. Use exponential backoff for degraded providers.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    },\n    {\n      \"id\": \"ci-003\",\n      \"type\": \"code_improvements\",\n      \"title\": \"Skill Composition Pipeline Builder\",\n      \"description\": \"Add a skill chaining mechanism that allows defining pipelines of skills that execute in sequence, with each skill's output feeding into the next. Include parallel branch support for independent steps.\",\n      \"rationale\": \"The spec mentions 'Skill composition: chain skills together for complex workflows' as a feature. The skill registry pattern with load/execute/unload already exists - this extends it with a pipeline builder that composes existing skills.\",\n      \"builds_upon\": [\"Skill Registry Pattern\", \"Skill Loading/Execution\"],\n      \"estimated_effort\": \"medium\",\n      \"affected_files\": [\"backend/services/skill_registry.py\", \"backend/services/skill_pipeline.py\", \"backend/models/pipeline.py\"],\n      \"existing_patterns\": [\"Skill loading/execution pattern\", \"Async task execution\", \"Dependency management\"],\n      \"implementation_approach\": \"Define Pipeline model with steps (sequential/parallel). Each step references a skill by name and defines input/output mapping. PipelineExecutor iterates steps, calls skill_registry.execute() for each, and maps outputs to next step's inputs. Use asyncio.gather for parallel branches.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    },\n    {\n      \"id\": \"ci-004\",\n      \"type\": \"code_improvements\",\n      \"title\": \"Spec Diff Visualization API\",\n      \"description\": \"Add an API endpoint that computes and returns structured diffs between spec versions, enabling the frontend to display what changed between any two versions with semantic highlighting.\",\n      \"rationale\": \"The spec versioning system already tracks versions. Adding diff computation follows the same SQLAlchemy pattern and enables the 'track how requirements evolve' requirement. The existing version retrieval pattern can be extended with diff output.\",\n      \"builds_upon\": [\"Spec Versioning System\", \"Spec CRUD Operations\"],\n      \"estimated_effort\": \"small\",\n      \"affected_files\": [\"backend/api/routes/specs.py\", \"backend/services/spec_manager.py\", \"backend/services/diff_engine.py\"],\n      \"existing_patterns\": [\"Spec version retrieval\", \"FastAPI response models\", \"Pydantic serialization\"],\n      \"implementation_approach\": \"Add GET /api/specs/{id}/diff?v1=1&v2=2 endpoint. Create diff_engine service using difflib for text comparison. Return structured diff with added/removed/changed sections. Use existing spec_versions table to fetch version contents.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    },\n    {\n      \"id\": \"ci-005\",\n      \"type\": \"code_improvements\",\n      \"title\": \"Cross-Validation Confidence Scoring\",\n      \"description\": \"Extend cross-model validation to compute a confidence score based on model agreement levels. Return structured validation results with agreement percentage, dissenting opinions, and recommended action.\",\n      \"rationale\": \"The cross-model validation pattern already calls multiple models. This extends the pattern to analyze responses semantically and compute agreement scores, making the 'multi-model consensus for critical decisions' more actionable.\",\n      \"builds_upon\": [\"Cross-Model Validation Logic\", \"Multi-Model Response Aggregation\"],\n      \"estimated_effort\": \"medium\",\n      \"affected_files\": [\"backend/llm/validation/cross_validate.py\", \"backend/llm/validation/confidence_scorer.py\", \"backend/models/validation_result.py\"],\n      \"existing_patterns\": [\"Parallel model calling pattern\", \"Response aggregation\", \"Validation logic\"],\n      \"implementation_approach\": \"After gathering responses from multiple models, use semantic similarity (embedding comparison) or keyword extraction to compute agreement score 0-100%. Define thresholds: >80% = high confidence, 50-80% = needs review, <50% = conflict. Return ValidationResult with score, individual responses, and recommendation.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    }\n  ],\n  \"metadata\": {\n    \"generated_at\": \"2026-01-12T12:00:00.000Z\",\n    \"project_type\": \"greenfield\",\n    \"note\": \"Ideas based on planned patterns from spec.md. These improvements should be considered during initial implementation to build on the established architectural patterns.\",\n    \"total_ideas\": 5\n  }\n}\n"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3539,"cache_read_input_tokens":34981,"cache_creation":{"ephemeral_5m_input_tokens":3539,"ephemeral_1h_input_tokens":0},"output_tokens":2467,"service_tier":"standard"}},"requestId":"req_011CX3zdeENeeQxMTX1FPk9G","type":"assistant","uuid":"6ce51451-4993-4721-824c-55d6c866f1d5","timestamp":"2026-01-12T18:57:36.142Z"}
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Use asyncio.gather for parallel branches.\",\n    42→      \"status\": \"draft\",\n    43→      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    44→    },\n    45→    {\n    46→      \"id\": \"ci-004\",\n    47→      \"type\": \"code_improvements\",\n    48→      \"title\": \"Spec Diff Visualization API\",\n    49→      \"description\": \"Add an API endpoint that computes and returns structured diffs between spec versions, enabling the frontend to display what changed between any two versions with semantic highlighting.\",\n    50→      \"rationale\": \"The spec versioning system already tracks versions. Adding diff computation follows the same SQLAlchemy pattern and enables the 'track how requirements evolve' requirement. 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Use asyncio.gather for parallel branches.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    },\n    {\n      \"id\": \"ci-004\",\n      \"type\": \"code_improvements\",\n      \"title\": \"Spec Diff Visualization API\",\n      \"description\": \"Add an API endpoint that computes and returns structured diffs between spec versions, enabling the frontend to display what changed between any two versions with semantic highlighting.\",\n      \"rationale\": \"The spec versioning system already tracks versions. Adding diff computation follows the same SQLAlchemy pattern and enables the 'track how requirements evolve' requirement. The existing version retrieval pattern can be extended with diff output.\",\n      \"builds_upon\": [\"Spec Versioning System\", \"Spec CRUD Operations\"],\n      \"estimated_effort\": \"small\",\n      \"affected_files\": [\"backend/api/routes/specs.py\", \"backend/services/spec_manager.py\", \"backend/services/diff_engine.py\"],\n      \"existing_patterns\": [\"Spec version retrieval\", \"FastAPI response models\", \"Pydantic serialization\"],\n      \"implementation_approach\": \"Add GET /api/specs/{id}/diff?v1=1&v2=2 endpoint. Create diff_engine service using difflib for text comparison. Return structured diff with added/removed/changed sections. Use existing spec_versions table to fetch version contents.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    },\n    {\n      \"id\": \"ci-005\",\n      \"type\": \"code_improvements\",\n      \"title\": \"Cross-Validation Confidence Scoring\",\n      \"description\": \"Extend cross-model validation to compute a confidence score based on model agreement levels. Return structured validation results with agreement percentage, dissenting opinions, and recommended action.\",\n      \"rationale\": \"The cross-model validation pattern already calls multiple models. This extends the pattern to analyze responses semantically and compute agreement scores, making the 'multi-model consensus for critical decisions' more actionable.\",\n      \"builds_upon\": [\"Cross-Model Validation Logic\", \"Multi-Model Response Aggregation\"],\n      \"estimated_effort\": \"medium\",\n      \"affected_files\": [\"backend/llm/validation/cross_validate.py\", \"backend/llm/validation/confidence_scorer.py\", \"backend/models/validation_result.py\"],\n      \"existing_patterns\": [\"Parallel model calling pattern\", \"Response aggregation\", \"Validation logic\"],\n      \"implementation_approach\": \"After gathering responses from multiple models, use semantic similarity (embedding comparison) or keyword extraction to compute agreement score 0-100%. Define thresholds: >80% = high confidence, 50-80% = needs review, <50% = conflict. Return ValidationResult with score, individual responses, and recommendation.\",\n      \"status\": \"draft\",\n      \"created_at\": \"2026-01-12T12:00:00.000Z\"\n    }\n  ],\n  \"metadata\": {\n    \"generated_at\": \"2026-01-12T12:00:00.000Z\",\n    \"project_type\": \"greenfield\",\n    \"note\": \"Ideas based on planned patterns from spec.md. These improvements should be considered during initial implementation to build on the established architectural patterns.\",\n    \"total_ideas\": 5\n  }\n}\n","numLines":81,"startLine":1,"totalLines":81}},"sourceToolAssistantUUID":"405dbb3a-a922-453f-a97b-60a97e23f6fe"}
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{"parentUuid":"83baeda5-d200-4c52-a4ea-8efa4998c55e","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"9a3122b3-253e-4cff-b84b-1757738fdb7f","version":"2.1.5","gitBranch":"master","slug":"cozy-greeting-treasure","message":{"model":"claude-opus-4-5-20251101","id":"msg_01YNeqr92mKUcMnAoa9kwmWR","type":"message","role":"assistant","content":[{"type":"text","text":"=== CODE IMPROVEMENTS IDEATION COMPLETE ===\n\n**Ideas Generated:** 5\n\n**Summary by Effort:**\n- Trivial: 0\n- Small: 2\n- Medium: 3\n- Large: 0\n- Complex: 0\n\n**Top Opportunities:**\n\n| ID | Title | Effort | Extends Pattern |\n|----|-------|--------|-----------------|\n| ci-001 | LLM Response Caching Layer | small | LiteLLM Unified Interface |\n| ci-002 | Provider Health Check with Auto-Routing | medium | Model Status + Router |\n| ci-003 | Skill Composition Pipeline Builder | medium | Skill Registry Pattern |\n| ci-004 | Spec Diff Visualization API | small | Spec Versioning System |\n| ci-005 | Cross-Validation Confidence Scoring | medium | Cross-Model Validation |\n\n**Note on Greenfield Project:**\nThis project has a comprehensive spec but no implemented code yet. The code improvement ideas are based on the **planned architectural patterns** defined in `spec.md`. These improvements should be considered during initial implementation to extend the established patterns from day one, rather than retrofitting them later.\n\n**Key Patterns Leveraged:**\n- LiteLLM unified interface (caching, health monitoring)\n- FastAPI service patterns (new endpoints, response models)\n- Skill registry architecture (pipeline composition)\n- Spec versioning system (diff computation)\n- Cross-model validation (confidence scoring)\n\n**code_improvements_ideas.json created successfully.**\n\n**Next phase:** UI/UX or Complete"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":2246,"cache_read_input_tokens":41147,"cache_creation":{"ephemeral_5m_input_tokens":2246,"ephemeral_1h_input_tokens":0},"output_tokens":397,"service_tier":"standard"}},"requestId":"req_011CX3zjPajAYtAwYvm465qp","type":"assistant","uuid":"f5844f58-f610-4530-abd4-2eec15d0f37e","timestamp":"2026-01-12T18:58:09.952Z"}