Mike Swanson
75ce1c2fd5
Enhanced code review and frontend validation with intelligent triggers: Code Review Agent Enhancement: - Added Sequential Thinking MCP integration for complex issues - Triggers on 2+ rejections or 3+ critical issues - New escalation format with root cause analysis - Comprehensive solution strategies with trade-off evaluation - Educational feedback to break rejection cycles - Files: .claude/agents/code-review.md (+308 lines) - Docs: CODE_REVIEW_ST_ENHANCEMENT.md, CODE_REVIEW_ST_TESTING.md Frontend Design Skill Enhancement: - Automatic invocation for ANY UI change - Comprehensive validation checklist (200+ checkpoints) - 8 validation categories (visual, interactive, responsive, a11y, etc.) - 3 validation levels (quick, standard, comprehensive) - Integration with code review workflow - Files: .claude/skills/frontend-design/SKILL.md (+120 lines) - Docs: UI_VALIDATION_CHECKLIST.md (462 lines), AUTOMATIC_VALIDATION_ENHANCEMENT.md (587 lines) Settings Optimization: - Repaired .claude/settings.local.json (fixed m365 pattern) - Reduced permissions from 49 to 33 (33% reduction) - Removed duplicates, sorted alphabetically - Created SETTINGS_PERMISSIONS.md documentation Checkpoint Command Enhancement: - Dual checkpoint system (git + database) - Saves session context to API for cross-machine recall - Includes git metadata in database context - Files: .claude/commands/checkpoint.md (+139 lines) Decision Rationale: - Sequential Thinking MCP breaks rejection cycles by identifying root causes - Automatic frontend validation catches UI issues before code review - Dual checkpoints enable complete project memory across machines - Settings optimization improves maintainability Total: 1,200+ lines of documentation and enhancements Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
5 lines
19 KiB
JSON
5 lines
19 KiB
JSON
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-01-12T18:56:50.978Z","sessionId":"1fe2b4dd-4bb9-4312-af13-bc223700f9d2"}
|
|
{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"1fe2b4dd-4bb9-4312-af13-bc223700f9d2","version":"2.1.5","gitBranch":"","type":"user","message":{"role":"user","content":"Summarize the key findings from the \"spec_writing\" phase in 500 words or less.\n\nFocus on extracting ONLY the most critical information that subsequent phases need:\n- Key decisions made and their rationale\n- Critical files, components, or patterns identified\n- Important constraints or requirements discovered\n- Actionable insights for implementation\n\nBe concise and use bullet points. Skip boilerplate and meta-commentary.\n\n## Phase Output:\n**spec.md**:\n```\n# 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: \n\n[... file truncated ...]\n```\n\n## Summary:\n"},"uuid":"a6d1bb4b-4004-4950-948a-00cc03f14c98","timestamp":"2026-01-12T18:56:56.202Z"}
|
|
{"parentUuid":"a6d1bb4b-4004-4950-948a-00cc03f14c98","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"1fe2b4dd-4bb9-4312-af13-bc223700f9d2","version":"2.1.5","gitBranch":"","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_01LoLmnRTmVuwWo2tfkurs7B","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to summarize the key findings from the \"spec_writing\" phase output. They want:\n- Key decisions made and their rationale\n- Critical files, components, or patterns identified\n- Important constraints or requirements discovered\n- Actionable insights for implementation\n\nThey want this in 500 words or less, with bullet points, and no boilerplate.\n\nLet me extract the most critical information from the spec document:\n\n1. **Project Type & Scope**: Greenfield multi-LLM orchestration platform\n2. **Key Architecture Decisions**: \n - LiteLLM as unified interface\n - 5 main services (orchestration, llm-wrapper, spec-management, skill, version-control)\n - FastAPI backend, React frontend, MCP server\n3. **Model Strategy**: Claude, Gemini, Grok, Ollama with task-specific routing\n4. **Critical Patterns**: Async LLM calls, cross-model validation, cost tracking\n5. **Out of scope**: Production infra, enterprise features, multi-tenant\n\nLet me organize this concisely:","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":5152,"cache_read_input_tokens":13209,"cache_creation":{"ephemeral_5m_input_tokens":5152,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zekLWY259D1yYQDE1X","type":"assistant","uuid":"8eddf7cb-f40d-44a0-be5e-8f6b1b527168","timestamp":"2026-01-12T18:57:01.809Z"}
|
|
{"parentUuid":"8eddf7cb-f40d-44a0-be5e-8f6b1b527168","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"1fe2b4dd-4bb9-4312-af13-bc223700f9d2","version":"2.1.5","gitBranch":"","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_01LoLmnRTmVuwWo2tfkurs7B","type":"message","role":"assistant","content":[{"type":"text","text":"# Spec Writing Phase - Key Findings\n\n## Project Classification\n- **Workflow Type**: Feature (Greenfield Development)\n- **Rationale**: Empty codebase despite referencing Autocoder 2.0/Auto-Claude patterns. Building from scratch, not refactoring.\n\n## Core Architecture Decisions\n\n### Multi-Service Design (5 services)\n1. **orchestration-service**: Task decomposition, model routing, parallel execution\n2. **llm-wrapper-service**: Unified LLM API via LiteLLM abstraction\n3. **spec-management-service**: Adaptive specs with interview/re-interview workflows\n4. **skill-service**: Extensible plugin architecture\n5. **version-control-service**: Git/Gitea automation\n\n### Technology Stack\n- **Backend**: Python 3.11+, FastAPI, SQLAlchemy, uvicorn (port 8000)\n- **Frontend**: TypeScript, React, Vite (port 5173)\n- **MCP Server**: FastMCP SDK, Python\n- **LLM Integration**: LiteLLM as unified interface layer\n\n## Critical Technical Decisions\n\n### LiteLLM as Unified Interface\n- **Why**: Single API for 100+ providers vs. managing 4+ separate SDKs\n- **Models**: Claude (`claude-3-opus`), Gemini (`gemini/gemini-pro`), Grok (`xai/grok`), Ollama (`ollama/llama3`)\n- **Built-in features**: Cost tracking, automatic retries, fallback logic\n- **Pattern**: `await acompletion(model=model, messages=messages)` works universally\n\n### Async-First Architecture\n- All LLM calls use async clients (`AsyncAnthropic`, async `genai`, etc.)\n- Enables parallel validation across models\n- FastAPI async endpoints prevent blocking\n\n### Database Schema Requirements\nFour core models identified:\n- `Spec`: Version-tracked project specifications\n- `Task`: Task history with model assignments\n- `Decision`: Decision tracking for course correction\n- Model interaction logs (via LiteLLM)\n\n## Critical Files & Components\n\n### Backend Core (33 files total)\n- **Entry**: `backend/main.py` (FastAPI app)\n- **Orchestration**: `backend/services/orchestrator.py` (routing logic)\n- **LLM Layer**: `backend/llm/unified_client.py` + 4 provider files\n- **Routing**: `backend/llm/routing/router.py` (task-to-model matching)\n- **Validation**: `backend/llm/validation/cross_validate.py`\n- **Git**: `backend/git/git_service.py`, `gitea_service.py`\n\n### Frontend (8 files)\n- `Dashboard.tsx`, `SpecEditor.tsx`, `TaskViewer.tsx`\n- `ModelStatus.tsx`, `CostTracker.tsx` (monitoring)\n\n### MCP Server (4 files)\n- `server.py`, `spec_tools.py`, `task_tools.py`, `project_state.py`\n\n## Key Constraints & Requirements\n\n### Environment Dependencies\n```bash\nANTHROPIC_API_KEY, GEMINI_API_KEY, XAI_API_KEY required\nOllama needs local server (no API key)\n```\n\n### Explicitly Out of Scope\n- Production deployment (K8s, cloud)\n- Enterprise auth (SSO/SAML)\n- Multi-tenant architecture\n- Billing/subscriptions\n- Mobile apps\n- CI/CD integration beyond Git ops\n\n## Actionable Implementation Insights\n\n### Phase 1 Priority\n1. Set up FastAPI skeleton + LiteLLM wrapper first (enables testing with real models early)\n2. Build basic orchestrator with single-model routing before adding cross-validation\n3. SQLAlchemy models before API routes (data layer foundation)\n\n### Integration Strategy\n- LiteLLM handles provider differences automatically\n- Use `asyncio.gather()` for parallel model calls\n- Cost tracking via `litellm.track_cost = True` (built-in)\n\n### Git Workflow Pattern\n- Feature branches per task\n- Conventional commit message generation\n- Gitea API for PR automation\n\n### Cross-Model Validation Pattern\n```python\nasync def parallel_validation(prompt, models):\n tasks = [call_llm(model, prompt) for model in models]\n return await asyncio.gather(*tasks, return_exceptions=True)\n```\n\n## Risk Mitigation\n- LiteLLM abstracts provider API changes\n- Async architecture prevents blocking on slow model responses\n- Local Ollama provides cost-free fallback for high-volume tasks"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":5152,"cache_read_input_tokens":13209,"cache_creation":{"ephemeral_5m_input_tokens":5152,"ephemeral_1h_input_tokens":0},"output_tokens":1347,"service_tier":"standard"}},"requestId":"req_011CX3zekLWY259D1yYQDE1X","type":"assistant","uuid":"5ecd7797-a7cf-4799-92f4-88f9643bc353","timestamp":"2026-01-12T18:57:20.499Z"}
|