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Mike Swanson 75ce1c2fd5 feat: Add Sequential Thinking to Code Review + Frontend Validation 
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>
2026-01-17 16:23:52 -07:00

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{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","type":"user","message":{"role":"user","content":"# Security Hardening Ideation Agent\n\nYou are a senior application security engineer. Your task is to analyze a codebase and identify security vulnerabilities, risks, and hardening opportunities.\n\n## Context\n\nYou have access to:\n- Project index with file structure and dependencies\n- Source code for security-sensitive areas\n- Package manifest (package.json, requirements.txt, etc.)\n- Configuration files\n- Memory context from previous sessions (if available)\n- Graph hints from Graphiti knowledge graph (if available)\n\n### Graph Hints Integration\n\nIf `graph_hints.json` exists and contains hints for your ideation type (`security_hardening`), use them to:\n1. **Avoid duplicates**: Don't suggest security fixes that have already been addressed\n2. **Build on success**: Prioritize security patterns that worked well in the past\n3. **Learn from incidents**: Use historical vulnerability knowledge to identify high-risk areas\n4. **Leverage context**: Use historical security audits to make better suggestions\n\n## Your Mission\n\nIdentify security issues across these categories:\n\n### 1. Authentication\n- Weak password policies\n- Missing MFA support\n- Session management issues\n- Token handling vulnerabilities\n- OAuth/OIDC misconfigurations\n\n### 2. Authorization\n- Missing access controls\n- Privilege escalation risks\n- IDOR vulnerabilities\n- Role-based access gaps\n- Resource permission issues\n\n### 3. Input Validation\n- SQL injection risks\n- XSS vulnerabilities\n- Command injection\n- Path traversal\n- Unsafe deserialization\n- Missing sanitization\n\n### 4. Data Protection\n- Sensitive data in logs\n- Missing encryption at rest\n- Weak encryption in transit\n- PII exposure risks\n- Insecure data storage\n\n### 5. Dependencies\n- Known CVEs in packages\n- Outdated dependencies\n- Unmaintained libraries\n- Supply chain risks\n- Missing lockfiles\n\n### 6. Configuration\n- Debug mode in production\n- Verbose error messages\n- Missing security headers\n- Insecure defaults\n- Exposed admin interfaces\n\n### 7. Secrets Management\n- Hardcoded credentials\n- Secrets in version control\n- Missing secret rotation\n- Insecure env handling\n- API keys in client code\n\n## Analysis Process\n\n1. **Dependency Audit**\n ```bash\n # Check for known vulnerabilities\n npm audit / pip-audit / cargo audit\n ```\n\n2. **Code Pattern Analysis**\n - Search for dangerous functions (eval, exec, system)\n - Find SQL query construction patterns\n - Identify user input handling\n - Check authentication flows\n\n3. **Configuration Review**\n - Environment variable usage\n - Security headers configuration\n - CORS settings\n - Cookie attributes\n\n4. **Data Flow Analysis**\n - Track sensitive data paths\n - Identify logging of PII\n - Check encryption boundaries\n\n## Output Format\n\nWrite your findings to `{output_dir}/security_hardening_ideas.json`:\n\n```json\n{\n \"security_hardening\": [\n {\n \"id\": \"sec-001\",\n \"type\": \"security_hardening\",\n \"title\": \"Fix SQL injection vulnerability in user search\",\n \"description\": \"The searchUsers() function in src/api/users.ts constructs SQL queries using string concatenation with user input, allowing SQL injection attacks.\",\n \"rationale\": \"SQL injection is a critical vulnerability that could allow attackers to read, modify, or delete database contents, potentially compromising all user data.\",\n \"category\": \"input_validation\",\n \"severity\": \"critical\",\n \"affectedFiles\": [\"src/api/users.ts\", \"src/db/queries.ts\"],\n \"vulnerability\": \"CWE-89: SQL Injection\",\n \"currentRisk\": \"Attacker can execute arbitrary SQL through the search parameter\",\n \"remediation\": \"Use parameterized queries with the database driver's prepared statement API. Replace string concatenation with bound parameters.\",\n \"references\": [\"https://owasp.org/www-community/attacks/SQL_Injection\", \"https://cwe.mitre.org/data/definitions/89.html\"],\n \"compliance\": [\"SOC2\", \"PCI-DSS\"]\n }\n ],\n \"metadata\": {\n \"dependenciesScanned\": 145,\n \"knownVulnerabilities\": 3,\n \"filesAnalyzed\": 89,\n \"criticalIssues\": 1,\n \"highIssues\": 4,\n \"generatedAt\": \"2024-12-11T10:00:00Z\"\n }\n}\n```\n\n## Severity Classification\n\n| Severity | Description | Examples |\n|----------|-------------|----------|\n| critical | Immediate exploitation risk, data breach potential | SQL injection, RCE, auth bypass |\n| high | Significant risk, requires prompt attention | XSS, CSRF, broken access control |\n| medium | Moderate risk, should be addressed | Information disclosure, weak crypto |\n| low | Minor risk, best practice improvements | Missing headers, verbose errors |\n\n## OWASP Top 10 Reference\n\n1. **A01 Broken Access Control** - Authorization checks\n2. **A02 Cryptographic Failures** - Encryption, hashing\n3. **A03 Injection** - SQL, NoSQL, OS, LDAP injection\n4. **A04 Insecure Design** - Architecture flaws\n5. **A05 Security Misconfiguration** - Defaults, headers\n6. **A06 Vulnerable Components** - Dependencies\n7. **A07 Auth Failures** - Session, credentials\n8. **A08 Data Integrity Failures** - Deserialization, CI/CD\n9. **A09 Logging Failures** - Audit, monitoring\n10. **A10 SSRF** - Server-side request forgery\n\n## Common Patterns to Check\n\n### Dangerous Code Patterns\n```javascript\n// BAD: Command injection risk\nexec(`ls ${userInput}`);\n\n// BAD: SQL injection risk\ndb.query(`SELECT * FROM users WHERE id = ${userId}`);\n\n// BAD: XSS risk\nelement.innerHTML = userInput;\n\n// BAD: Path traversal risk\nfs.readFile(`./uploads/${filename}`);\n```\n\n### Secrets Detection\n```\n# Patterns to flag\nAPI_KEY=sk-...\npassword = \"hardcoded\"\ntoken: \"eyJ...\"\naws_secret_access_key\n```\n\n## Guidelines\n\n- **Prioritize Exploitability**: Focus on issues that can be exploited, not theoretical risks\n- **Provide Clear Remediation**: Each finding should include how to fix it\n- **Reference Standards**: Link to OWASP, CWE, CVE where applicable\n- **Consider Context**: A \"vulnerability\" in a dev tool differs from production code\n- **Avoid False Positives**: Verify patterns before flagging\n\n## Categories Explained\n\n| Category | Focus | Common Issues |\n|----------|-------|---------------|\n| authentication | Identity verification | Weak passwords, missing MFA |\n| authorization | Access control | IDOR, privilege escalation |\n| input_validation | User input handling | Injection, XSS |\n| data_protection | Sensitive data | Encryption, PII |\n| dependencies | Third-party code | CVEs, outdated packages |\n| configuration | Settings & defaults | Headers, debug mode |\n| secrets_management | Credentials | Hardcoded secrets, rotation |\n\nRemember: Security is not about finding every possible issue, but identifying the most impactful risks that can be realistically exploited and providing actionable remediation.\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\\security_hardening_ideas.json\n**Max Ideas**: 5\n\nGenerate up to 5 Security Hardening ideas.\nAvoid duplicating features that are already planned (see ideation_context.json).\nOutput your ideas to security_hardening_ideas.json.\n\n"},"uuid":"4b86a154-6920-44e6-bdf2-1a1b451f7067","timestamp":"2026-01-12T18:53:44.402Z"}
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{"parentUuid":"1174a821-8e3d-498c-a404-5faf3aa0f824","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FQNtZtSjfpfB6T2A8jtUmi","type":"tool_result","content":" 1→{\n 2→ \"existing_features\": [],\n 3→ \"tech_stack\": [],\n 4→ \"target_audience\": null,\n 5→ \"planned_features\": [],\n 6→ \"graph_hints\": {},\n 7→ \"config\": {\n 8→ \"enabled_types\": [\n 9→ \"code_improvements\",\n 10→ \"ui_ux_improvements\",\n 11→ \"security_hardening\"\n 12→ ],\n 13→ \"include_roadmap_context\": true,\n 14→ \"include_kanban_context\": true,\n 15→ \"max_ideas_per_type\": 5\n 16→ },\n 17→ \"created_at\": \"2026-01-12T11:53:31.422574\"\n 18→}\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":"2e4c18c8-5e24-4ef3-96d4-c078a4e5788b","timestamp":"2026-01-12T18:54:15.321Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\ideation_context.json","content":"{\n \"existing_features\": [],\n \"tech_stack\": [],\n \"target_audience\": null,\n \"planned_features\": [],\n \"graph_hints\": {},\n \"config\": {\n \"enabled_types\": [\n \"code_improvements\",\n \"ui_ux_improvements\",\n \"security_hardening\"\n ],\n \"include_roadmap_context\": true,\n \"include_kanban_context\": true,\n \"max_ideas_per_type\": 5\n },\n \"created_at\": \"2026-01-12T11:53:31.422574\"\n}","numLines":18,"startLine":1,"totalLines":18}},"sourceToolAssistantUUID":"1174a821-8e3d-498c-a404-5faf3aa0f824"}
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{"parentUuid":"f83c2c47-2bfc-483d-820c-48e53d1ef6e2","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015QhnCDSA7ZPsqyB6wP6eF6","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":"5ecf9194-0c48-4025-9d39-5f8fc7cd3d97","timestamp":"2026-01-12T18:55:20.177Z","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":"f83c2c47-2bfc-483d-820c-48e53d1ef6e2"}
{"parentUuid":"be669ef3-bf61-480c-ae0d-52dc1baa4971","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XinLPaoA741B6suM4x8Bfb","type":"tool_result","content":" 1→# Auto Claude Framework Environment Variables\n 2→# Managed by Auto Claude UI\n 3→\n 4→# Claude Code OAuth Token (REQUIRED)\n 5→CLAUDE_CODE_OAUTH_TOKEN=\n 6→\n 7→# Model override (OPTIONAL)\n 8→# AUTO_BUILD_MODEL=claude-opus-4-5-20251101\n 9→\n 10→# =============================================================================\n 11→# LINEAR INTEGRATION (OPTIONAL)\n 12→# =============================================================================\n 13→# LINEAR_API_KEY=\n 14→# LINEAR_TEAM_ID=\n 15→# LINEAR_PROJECT_ID=\n 16→# LINEAR_REALTIME_SYNC=false\n 17→\n 18→# =============================================================================\n 19→# GITHUB INTEGRATION (OPTIONAL)\n 20→# =============================================================================\n 21→GITHUB_TOKEN=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\n 22→GITHUB_REPO=AZComputerGuru/auto-claude-plus\n 23→# GITHUB_AUTO_SYNC=false\n 24→\n 25→# =============================================================================\n 26→# GITLAB INTEGRATION (OPTIONAL)\n 27→# =============================================================================\n 28→# GITLAB_ENABLED=true\n 29→# GITLAB_INSTANCE_URL=https://gitlab.com\n 30→# GITLAB_TOKEN=\n 31→# GITLAB_PROJECT=group/project\n 32→# GITLAB_AUTO_SYNC=false\n 33→\n 34→# =============================================================================\n 35→# GIT/WORKTREE SETTINGS (OPTIONAL)\n 36→# =============================================================================\n 37→# Default base branch for worktree creation\n 38→# If not set, Auto Claude will auto-detect main/master, or fall back to current branch\n 39→# DEFAULT_BRANCH=main\n 40→\n 41→# =============================================================================\n 42→# UI SETTINGS (OPTIONAL)\n 43→# =============================================================================\n 44→# ENABLE_FANCY_UI=true\n 45→\n 46→# =============================================================================\n 47→# MCP SERVER CONFIGURATION (per-project overrides)\n 48→# =============================================================================\n 49→# Context7 documentation lookup (default: enabled)\n 50→# CONTEXT7_ENABLED=true\n 51→# Linear MCP integration (default: follows LINEAR_API_KEY)\n 52→# LINEAR_MCP_ENABLED=true\n 53→# Electron desktop automation - QA agents only (default: disabled)\n 54→# ELECTRON_MCP_ENABLED=false\n 55→# Puppeteer browser automation - QA agents only (default: disabled)\n 56→# PUPPETEER_MCP_ENABLED=false\n 57→\n 58→# =============================================================================\n 59→# PER-AGENT MCP OVERRIDES\n 60→# Add or remove MCP servers for specific agents\n 61→# Format: AGENT_MCP_<agent_type>_ADD=server1,server2\n 62→# Format: AGENT_MCP_<agent_type>_REMOVE=server1,server2\n 63→# =============================================================================\n 64→# No per-agent overrides configured\n 65→\n 66→# =============================================================================\n 67→# CUSTOM MCP SERVERS\n 68→# User-defined MCP servers (command-based or HTTP-based)\n 69→# JSON format: [{\"id\":\"...\",\"name\":\"...\",\"type\":\"command|http\",...}]\n 70→# =============================================================================\n 71→# CUSTOM_MCP_SERVERS=[]\n 72→\n 73→# =============================================================================\n 74→# MEMORY INTEGRATION\n 75→# Embedding providers: OpenAI, Google AI, Azure OpenAI, Ollama, Voyage\n 76→# =============================================================================\n 77→# GRAPHITI_ENABLED=true\n 78→\n 79→# Embedding Provider (for semantic search - optional, keyword search works without)\n 80→# GRAPHITI_EMBEDDER_PROVIDER=ollama\n 81→\n 82→# OpenAI Embeddings\n 83→# OPENAI_API_KEY=\n 84→# OPENAI_EMBEDDING_MODEL=text-embedding-3-small\n 85→\n 86→# Azure OpenAI Embeddings\n 87→# AZURE_OPENAI_API_KEY=\n 88→# AZURE_OPENAI_BASE_URL=\n 89→# AZURE_OPENAI_EMBEDDING_DEPLOYMENT=\n 90→\n 91→# Voyage AI Embeddings\n 92→# VOYAGE_API_KEY=\n 93→# VOYAGE_EMBEDDING_MODEL=voyage-3\n 94→\n 95→# Google AI Embeddings\n 96→# GOOGLE_API_KEY=\n 97→# GOOGLE_EMBEDDING_MODEL=text-embedding-004\n 98→\n 99→# Ollama Embeddings (Local - free)\n 100→# OLLAMA_BASE_URL=http://localhost:11434\n 101→# OLLAMA_EMBEDDING_MODEL=embeddinggemma\n 102→# OLLAMA_EMBEDDING_DIM=768\n 103→\n 104→# LadybugDB Database (embedded - no Docker required)\n 105→# GRAPHITI_DATABASE=auto_claude_memory\n 106→# GRAPHITI_DB_PATH=~/.auto-claude/memories\n 107→\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":"5c1af9b2-1072-4290-ba18-7128815b4287","timestamp":"2026-01-12T18:55:20.177Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\.env","content":"# Auto Claude Framework Environment Variables\n# Managed by Auto Claude UI\n\n# Claude Code OAuth Token (REQUIRED)\nCLAUDE_CODE_OAUTH_TOKEN=\n\n# Model override (OPTIONAL)\n# AUTO_BUILD_MODEL=claude-opus-4-5-20251101\n\n# =============================================================================\n# LINEAR INTEGRATION (OPTIONAL)\n# =============================================================================\n# LINEAR_API_KEY=\n# LINEAR_TEAM_ID=\n# LINEAR_PROJECT_ID=\n# LINEAR_REALTIME_SYNC=false\n\n# =============================================================================\n# GITHUB INTEGRATION (OPTIONAL)\n# =============================================================================\nGITHUB_TOKEN=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\nGITHUB_REPO=AZComputerGuru/auto-claude-plus\n# GITHUB_AUTO_SYNC=false\n\n# =============================================================================\n# GITLAB INTEGRATION (OPTIONAL)\n# =============================================================================\n# GITLAB_ENABLED=true\n# GITLAB_INSTANCE_URL=https://gitlab.com\n# GITLAB_TOKEN=\n# GITLAB_PROJECT=group/project\n# GITLAB_AUTO_SYNC=false\n\n# =============================================================================\n# GIT/WORKTREE SETTINGS (OPTIONAL)\n# =============================================================================\n# Default base branch for worktree creation\n# If not set, Auto Claude will auto-detect main/master, or fall back to current branch\n# DEFAULT_BRANCH=main\n\n# =============================================================================\n# UI SETTINGS (OPTIONAL)\n# =============================================================================\n# ENABLE_FANCY_UI=true\n\n# =============================================================================\n# MCP SERVER CONFIGURATION (per-project overrides)\n# =============================================================================\n# Context7 documentation lookup (default: enabled)\n# CONTEXT7_ENABLED=true\n# Linear MCP integration (default: follows LINEAR_API_KEY)\n# LINEAR_MCP_ENABLED=true\n# Electron desktop automation - QA agents only (default: disabled)\n# ELECTRON_MCP_ENABLED=false\n# Puppeteer browser automation - QA agents only (default: disabled)\n# PUPPETEER_MCP_ENABLED=false\n\n# =============================================================================\n# PER-AGENT MCP OVERRIDES\n# Add or remove MCP servers for specific agents\n# Format: AGENT_MCP_<agent_type>_ADD=server1,server2\n# Format: AGENT_MCP_<agent_type>_REMOVE=server1,server2\n# =============================================================================\n# No per-agent overrides configured\n\n# =============================================================================\n# CUSTOM MCP SERVERS\n# User-defined MCP servers (command-based or HTTP-based)\n# JSON format: [{\"id\":\"...\",\"name\":\"...\",\"type\":\"command|http\",...}]\n# =============================================================================\n# CUSTOM_MCP_SERVERS=[]\n\n# =============================================================================\n# MEMORY INTEGRATION\n# Embedding providers: OpenAI, Google AI, Azure OpenAI, Ollama, Voyage\n# =============================================================================\n# GRAPHITI_ENABLED=true\n\n# Embedding Provider (for semantic search - optional, keyword search works without)\n# GRAPHITI_EMBEDDER_PROVIDER=ollama\n\n# OpenAI Embeddings\n# OPENAI_API_KEY=\n# OPENAI_EMBEDDING_MODEL=text-embedding-3-small\n\n# Azure OpenAI Embeddings\n# AZURE_OPENAI_API_KEY=\n# AZURE_OPENAI_BASE_URL=\n# AZURE_OPENAI_EMBEDDING_DEPLOYMENT=\n\n# Voyage AI Embeddings\n# VOYAGE_API_KEY=\n# VOYAGE_EMBEDDING_MODEL=voyage-3\n\n# Google AI Embeddings\n# GOOGLE_API_KEY=\n# GOOGLE_EMBEDDING_MODEL=text-embedding-004\n\n# Ollama Embeddings (Local - free)\n# OLLAMA_BASE_URL=http://localhost:11434\n# OLLAMA_EMBEDDING_MODEL=embeddinggemma\n# OLLAMA_EMBEDDING_DIM=768\n\n# LadybugDB Database (embedded - no Docker required)\n# GRAPHITI_DATABASE=auto_claude_memory\n# GRAPHITI_DB_PATH=~/.auto-claude/memories\n","numLines":107,"startLine":1,"totalLines":107}},"sourceToolAssistantUUID":"be669ef3-bf61-480c-ae0d-52dc1baa4971"}
{"parentUuid":"59c98ce9-8549-4702-b991-9efabd4cf9c9","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CviLMJaqANDCTBx3tzjRKU","type":"tool_result","content":" 1→# Auto Claude data directory\n 2→.auto-claude/\n 3→\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":"ffa490e8-c0d8-4a3c-881d-c478b6047339","timestamp":"2026-01-12T18:55:20.178Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.gitignore","content":"# Auto Claude data directory\n.auto-claude/\n","numLines":3,"startLine":1,"totalLines":3}},"sourceToolAssistantUUID":"59c98ce9-8549-4702-b991-9efabd4cf9c9"}
{"parentUuid":"a3eec7be-15cd-48f2-96c7-0c843a68ede3","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01A3oPKJiqoRKaVzsS177ePA","type":"tool_result","content":" 1→{\n 2→ \"sandbox\": {\n 3→ \"enabled\": true,\n 4→ \"autoAllowBashIfSandboxed\": true\n 5→ },\n 6→ \"permissions\": {\n 7→ \"defaultMode\": \"acceptEdits\",\n 8→ \"allow\": [\n 9→ \"Read(./**)\",\n 10→ \"Write(./**)\",\n 11→ \"Edit(./**)\",\n 12→ \"Glob(./**)\",\n 13→ \"Grep(./**)\",\n 14→ \"Read(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n 15→ \"Write(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n 16→ \"Edit(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n 17→ \"Glob(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n 18→ \"Grep(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n 19→ \"Read(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\\\\.auto-claude\\\\ideation/**)\",\n 20→ \"Write(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\\\\.auto-claude\\\\ideation/**)\",\n 21→ \"Edit(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\\\\.auto-claude\\\\ideation/**)\",\n 22→ \"Bash(*)\",\n 23→ \"WebFetch(*)\",\n 24→ \"WebSearch(*)\",\n 25→ \"mcp__context7__resolve-library-id(*)\",\n 26→ \"mcp__context7__get-library-docs(*)\",\n 27→ \"mcp__graphiti-memory__search_nodes(*)\",\n 28→ \"mcp__graphiti-memory__search_facts(*)\",\n 29→ \"mcp__graphiti-memory__add_episode(*)\",\n 30→ \"mcp__graphiti-memory__get_episodes(*)\",\n 31→ \"mcp__graphiti-memory__get_entity_edge(*)\"\n 32→ ]\n 33→ }\n 34→}\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":"4df2c249-92ab-402f-8260-f1891d9421b6","timestamp":"2026-01-12T18:55:20.178Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.claude_settings.json","content":"{\n \"sandbox\": {\n \"enabled\": true,\n \"autoAllowBashIfSandboxed\": true\n },\n \"permissions\": {\n \"defaultMode\": \"acceptEdits\",\n \"allow\": [\n \"Read(./**)\",\n \"Write(./**)\",\n \"Edit(./**)\",\n \"Glob(./**)\",\n \"Grep(./**)\",\n \"Read(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n \"Write(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n \"Edit(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n \"Glob(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n \"Grep(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus/**)\",\n \"Read(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\\\\.auto-claude\\\\ideation/**)\",\n \"Write(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\\\\.auto-claude\\\\ideation/**)\",\n \"Edit(C:\\\\Users\\\\MikeSwanson\\\\claude-projects\\\\ACPlus\\\\auto-claude-plus\\\\.auto-claude\\\\ideation/**)\",\n \"Bash(*)\",\n \"WebFetch(*)\",\n \"WebSearch(*)\",\n \"mcp__context7__resolve-library-id(*)\",\n \"mcp__context7__get-library-docs(*)\",\n \"mcp__graphiti-memory__search_nodes(*)\",\n \"mcp__graphiti-memory__search_facts(*)\",\n \"mcp__graphiti-memory__add_episode(*)\",\n \"mcp__graphiti-memory__get_episodes(*)\",\n \"mcp__graphiti-memory__get_entity_edge(*)\"\n ]\n }\n}","numLines":34,"startLine":1,"totalLines":34}},"sourceToolAssistantUUID":"a3eec7be-15cd-48f2-96c7-0c843a68ede3"}
{"parentUuid":"4df2c249-92ab-402f-8260-f1891d9421b6","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_011ffRZEp9D5YJqLHfx4Ard8","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now I have a comprehensive view of the project. Let me analyze what I've found:\n\n1. **Project Overview**: This is a Multi-LLM Agentic Development Platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama). The project is in early stages - it's mostly spec and planning files with no actual source code implemented yet.\n\n2. **Critical Security Finding - Exposed API Token**: \n - The `.auto-claude/.env` file contains a **hardcoded GitHub token**: `GITHUB_TOKEN=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`\n - This is a CRITICAL security vulnerability - tokens should never be committed to version control!\n\n3. **The `.gitignore` only ignores `.auto-claude/`** - so the `.env` file might be protected, but let me verify this.\n\n4. **Potential Security Issues I can identify for the planned project**:\n - API key management for multiple LLM providers\n - Database security (SQLite)\n - Input validation for API endpoints\n - CORS configuration needs\n - Authentication/authorization gaps (explicitly out of scope but creates risk)\n - Secrets handling in environment variables\n\nLet me also check if the graph_hints.json has any relevant security context:","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":11018,"cache_read_input_tokens":21383,"cache_creation":{"ephemeral_5m_input_tokens":11018,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zXsMzF7hbDrkbxJ45t","type":"assistant","uuid":"60f30971-2cb4-4263-bebd-89e1de7d3ea7","timestamp":"2026-01-12T18:55:37.987Z"}
{"parentUuid":"60f30971-2cb4-4263-bebd-89e1de7d3ea7","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_011ffRZEp9D5YJqLHfx4Ard8","type":"message","role":"assistant","content":[{"type":"text","text":"Let me check the graph hints and verify if any source code exists yet:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":11018,"cache_read_input_tokens":21383,"cache_creation":{"ephemeral_5m_input_tokens":11018,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zXsMzF7hbDrkbxJ45t","type":"assistant","uuid":"197e9ab3-3685-4f5e-b3a6-23f5388f969d","timestamp":"2026-01-12T18:55:38.719Z"}
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{"parentUuid":"3139454f-c5e2-428f-a546-a8c115581df1","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VzpjnsA68u4ad7jPA4e9rD","type":"tool_result","content":" 1→{\n 2→ \"enabled\": true,\n 3→ \"hints_by_type\": {\n 4→ \"code_improvements\": [],\n 5→ \"ui_ux_improvements\": [],\n 6→ \"security_hardening\": []\n 7→ },\n 8→ \"total_hints\": 0,\n 9→ \"created_at\": \"2026-01-12T11:53:31.431565\"\n 10→}\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":"9b8c3501-e7e3-414a-918f-499bee431580","timestamp":"2026-01-12T18:56:35.017Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\graph_hints.json","content":"{\n \"enabled\": true,\n \"hints_by_type\": {\n \"code_improvements\": [],\n \"ui_ux_improvements\": [],\n \"security_hardening\": []\n },\n \"total_hints\": 0,\n \"created_at\": \"2026-01-12T11:53:31.431565\"\n}","numLines":10,"startLine":1,"totalLines":10}},"sourceToolAssistantUUID":"3139454f-c5e2-428f-a546-a8c115581df1"}
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{"parentUuid":"adb46518-c242-4841-9e83-d0758f16ef76","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_01KEtFFqRJNxPpxrK2pnmavY","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"This is a greenfield project with no source code yet - only spec and planning files. The project hasn't been built yet. However, I can still identify security hardening opportunities based on:\n\n1. **The exposed GitHub token in .env file** - This is a CRITICAL security issue that needs immediate attention. Even though `.auto-claude/` is in `.gitignore`, the token is still present in a file that could be accidentally exposed.\n\n2. **The planned architecture** - Based on the spec, I can identify security considerations for:\n - API key management (multiple LLM providers)\n - Database security for SQLite\n - Input validation for FastAPI endpoints\n - CORS configuration\n - Authentication/authorization (not in scope but creates risk)\n - Secrets rotation\n - Security headers\n\nLet me write up 5 security hardening ideas focused on:\n1. The exposed GitHub token (CRITICAL)\n2. Secure API key management architecture\n3. Input validation for the planned FastAPI endpoints\n4. Security headers and CORS configuration\n5. Database security for SQLite\n\nLet me create the output file now.","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":971,"cache_read_input_tokens":32401,"cache_creation":{"ephemeral_5m_input_tokens":971,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zdQGjWcCLLR8np4HtR","type":"assistant","uuid":"0f6ac30e-5115-46c4-b85b-486f5bc6378b","timestamp":"2026-01-12T18:56:56.021Z"}
{"parentUuid":"0f6ac30e-5115-46c4-b85b-486f5bc6378b","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_01KEtFFqRJNxPpxrK2pnmavY","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have a complete picture. This is a **greenfield project** with specifications but no source code implemented yet. However, I've identified several critical security concerns:\n\n1. **CRITICAL**: There's a **GitHub token exposed** in the `.auto-claude/.env` file\n2. The planned architecture has multiple API key management challenges\n3. The spec mentions security requirements but lacks detailed implementation guidance\n\nLet me write the security hardening ideas:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":971,"cache_read_input_tokens":32401,"cache_creation":{"ephemeral_5m_input_tokens":971,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zdQGjWcCLLR8np4HtR","type":"assistant","uuid":"8c329c82-5a15-4563-bc9e-354c2a3c81da","timestamp":"2026-01-12T18:56:58.147Z"}
{"parentUuid":"8c329c82-5a15-4563-bc9e-354c2a3c81da","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_01KEtFFqRJNxPpxrK2pnmavY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011nUpXSER1wr4tpLpzGsmgG","name":"Write","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\security_hardening_ideas.json","content":"{\n \"security_hardening\": [\n {\n \"id\": \"sec-001\",\n \"type\": \"security_hardening\",\n \"title\": \"Remove exposed GitHub token from .env file\",\n \"description\": \"The file .auto-claude/.env contains a hardcoded GitHub OAuth token (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). Even though .auto-claude/ is in .gitignore, this token should be revoked and regenerated immediately, as it may have been exposed in various ways (backup systems, local file sharing, etc.).\",\n \"rationale\": \"Exposed API tokens can allow attackers to perform actions on GitHub repositories, including reading private code, modifying repositories, or accessing organization data. The 'gho_' prefix indicates this is a GitHub OAuth token with potentially broad permissions.\",\n \"category\": \"secrets_management\",\n \"severity\": \"critical\",\n \"affectedFiles\": [\".auto-claude/.env\"],\n \"vulnerability\": \"CWE-798: Use of Hard-coded Credentials\",\n \"currentRisk\": \"Token may be used by attackers to access GitHub resources, modify code, or exfiltrate data from private repositories\",\n \"remediation\": \"1. Immediately revoke this token in GitHub settings (Settings > Developer settings > Personal access tokens). 2. Generate a new token with minimal required scopes. 3. Store the new token in a system environment variable or secure secret manager, not in files. 4. Add *.env to .gitignore at the root level as additional protection. 5. Consider using a .env.example file with placeholder values instead.\",\n \"references\": [\n \"https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/token-expiration-and-revocation\",\n \"https://cwe.mitre.org/data/definitions/798.html\"\n ],\n \"compliance\": [\"SOC2\", \"PCI-DSS\", \"OWASP A07:2021\"]\n },\n {\n \"id\": \"sec-002\",\n \"type\": \"security_hardening\",\n \"title\": \"Implement secure API key rotation and vault storage for LLM providers\",\n \"description\": \"The planned architecture requires API keys for 4+ LLM providers (Anthropic, Google/Gemini, xAI/Grok, Gitea). The spec shows these stored in environment variables, but lacks a secure key rotation strategy or centralized secret management.\",\n \"rationale\": \"Multiple high-value API keys without rotation increase the blast radius of any credential leak. LLM API keys often have significant financial implications if misused (token costs can accumulate rapidly).\",\n \"category\": \"secrets_management\",\n \"severity\": \"high\",\n \"affectedFiles\": [\".env.example\", \"backend/main.py\", \"backend/llm/unified_client.py\"],\n \"vulnerability\": \"CWE-522: Insufficiently Protected Credentials\",\n \"currentRisk\": \"Compromised API keys could result in: (1) Unauthorized use leading to high API costs, (2) Data exfiltration through LLM prompts, (3) No ability to quickly rotate credentials in incident response\",\n \"remediation\": \"1. Use a secrets manager (HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault) instead of .env files for production. 2. Implement automatic key rotation with at least 90-day rotation for all LLM provider keys. 3. Create a key_manager.py service that abstracts secret retrieval and supports both local dev (.env) and production (vault) modes. 4. Add runtime validation that keys are loaded at startup and log warnings (not keys themselves) if missing. 5. Implement API key usage monitoring to detect anomalous usage patterns.\",\n \"references\": [\n \"https://owasp.org/www-project-web-security-testing-guide/latest/4-Web_Application_Security_Testing/04-Authentication_Testing/01-Testing_for_Credentials_Transported_over_an_Encrypted_Channel\",\n \"https://cheatsheetseries.owasp.org/cheatsheets/Secrets_Management_Cheat_Sheet.html\"\n ],\n \"compliance\": [\"SOC2\", \"ISO 27001\", \"OWASP A07:2021\"]\n },\n {\n \"id\": \"sec-003\",\n \"type\": \"security_hardening\",\n \"title\": \"Add comprehensive input validation and sanitization for FastAPI endpoints\",\n \"description\": \"The spec outlines multiple API endpoints (/api/tasks, /api/specs, /api/models) that will accept user input including task descriptions, spec content, and model parameters. Without proper input validation, these endpoints are vulnerable to injection attacks.\",\n \"rationale\": \"User input flows to multiple high-risk destinations: LLM prompts (prompt injection), database queries (SQL injection), Git operations (command injection), and file operations (path traversal). The multi-model architecture increases the attack surface.\",\n \"category\": \"input_validation\",\n \"severity\": \"high\",\n \"affectedFiles\": [\n \"backend/api/routes/tasks.py\",\n \"backend/api/routes/specs.py\",\n \"backend/services/orchestrator.py\",\n \"backend/git/git_service.py\"\n ],\n \"vulnerability\": \"CWE-20: Improper Input Validation, CWE-89: SQL Injection, CWE-78: OS Command Injection\",\n \"currentRisk\": \"Without validation: (1) Prompt injection could manipulate LLM behavior, (2) SQL injection could compromise the database, (3) Command injection in git operations could execute arbitrary code, (4) Path traversal could access/modify unauthorized files\",\n \"remediation\": \"1. Use Pydantic models with strict validators for all API inputs (already planned, ensure implementation). 2. Create a sanitization module with functions for: SQL (use parameterized queries only), Shell (reject special characters in branch names/commit messages), File paths (validate against allowed directories). 3. Implement content-length limits on all text inputs (task descriptions, spec content). 4. Add rate limiting per-endpoint using slowapi or similar. 5. Create an input validation middleware that logs and blocks suspicious patterns. 6. For LLM prompts, implement prompt injection detection using pattern matching and semantic analysis.\",\n \"references\": [\n \"https://owasp.org/www-community/attacks/SQL_Injection\",\n \"https://owasp.org/www-community/attacks/Command_Injection\",\n \"https://cwe.mitre.org/data/definitions/20.html\"\n ],\n \"compliance\": [\"SOC2\", \"PCI-DSS\", \"OWASP A03:2021\"]\n },\n {\n \"id\": \"sec-004\",\n \"type\": \"security_hardening\",\n \"title\": \"Configure security headers and CORS policy for FastAPI backend\",\n \"description\": \"The planned FastAPI backend will expose APIs consumed by the React frontend. Without proper security headers and CORS configuration, the application is vulnerable to XSS, clickjacking, and cross-origin attacks.\",\n \"rationale\": \"The spec mentions the frontend at localhost:5173 consuming the backend at localhost:8000. Without CORS restrictions, any malicious website could make requests to the backend. Missing security headers expose users to various client-side attacks.\",\n \"category\": \"configuration\",\n \"severity\": \"medium\",\n \"affectedFiles\": [\n \"backend/main.py\",\n \"frontend/vite.config.ts\"\n ],\n \"vulnerability\": \"CWE-346: Origin Validation Error, CWE-1021: Improper Restriction of Rendered UI Layers\",\n \"currentRisk\": \"Without configuration: (1) CSRF attacks could perform unauthorized actions, (2) XSS could steal session data or API keys temporarily stored in browser, (3) Clickjacking could trick users into unintended actions\",\n \"remediation\": \"1. Add CORS middleware with explicit allowed origins (not '*' in production):\\n```python\\nfrom fastapi.middleware.cors import CORSMiddleware\\napp.add_middleware(\\n CORSMiddleware,\\n allow_origins=['http://localhost:5173'], # Production: specific domains only\\n allow_credentials=True,\\n allow_methods=['GET', 'POST', 'PUT', 'DELETE'],\\n allow_headers=['*'],\\n)\\n```\\n2. Add security headers middleware:\\n```python\\nfrom starlette.middleware import Middleware\\n# X-Content-Type-Options: nosniff\\n# X-Frame-Options: DENY\\n# Strict-Transport-Security: max-age=31536000\\n# Content-Security-Policy: default-src 'self'\\n```\\n3. Use httpOnly and secure flags for any cookies. 4. Implement CSRF tokens for state-changing operations.\",\n \"references\": [\n \"https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS\",\n \"https://owasp.org/www-project-secure-headers/\",\n \"https://cheatsheetseries.owasp.org/cheatsheets/Cross-Site_Request_Forgery_Prevention_Cheat_Sheet.html\"\n ],\n \"compliance\": [\"SOC2\", \"OWASP A05:2021\"]\n },\n {\n \"id\": \"sec-005\",\n \"type\": \"security_hardening\",\n \"title\": \"Implement secure logging practices to prevent credential and PII exposure\",\n \"description\": \"The spec requires tracking LLM interactions, costs, and task history. Without careful logging practices, sensitive data (API keys, user inputs, LLM responses) could be exposed in logs, console output, or error messages.\",\n \"rationale\": \"The system will process: (1) Multiple API keys for LLM providers, (2) User task descriptions that may contain sensitive business data, (3) LLM responses that could contain generated secrets or PII. Logs are often stored insecurely and accessed by multiple parties.\",\n \"category\": \"data_protection\",\n \"severity\": \"medium\",\n \"affectedFiles\": [\n \"backend/main.py\",\n \"backend/llm/unified_client.py\",\n \"backend/llm/routing/cost_tracker.py\",\n \"backend/services/orchestrator.py\"\n ],\n \"vulnerability\": \"CWE-532: Insertion of Sensitive Information into Log File\",\n \"currentRisk\": \"Without secure logging: (1) API keys could appear in debug logs, (2) PII in user prompts could be logged, (3) Error tracebacks could expose internal paths and configurations, (4) LLM responses with generated credentials could be persisted\",\n \"remediation\": \"1. Create a logging configuration that sanitizes sensitive patterns:\\n```python\\nimport re\\nclass SensitiveDataFilter(logging.Filter):\\n PATTERNS = [\\n (r'sk-ant-[a-zA-Z0-9-]+', '[ANTHROPIC_KEY_REDACTED]'),\\n (r'AIza[a-zA-Z0-9-]+', '[GOOGLE_KEY_REDACTED]'),\\n (r'xai-[a-zA-Z0-9-]+', '[XAI_KEY_REDACTED]'),\\n (r'gh[ops]_[a-zA-Z0-9]+', '[GITHUB_TOKEN_REDACTED]'),\\n ]\\n```\\n2. Set DEBUG mode to False in production and use structured logging. 3. Implement log rotation and secure storage with restricted access. 4. Create a separate audit log for security events (auth failures, rate limit hits). 5. Never log full LLM prompts/responses in production - log only metadata (token counts, model used, latency). 6. Use exception handlers that return generic error messages to clients while logging full details server-side.\",\n \"references\": [\n \"https://owasp.org/www-project-web-security-testing-guide/latest/4-Web_Application_Security_Testing/02-Configuration_and_Deployment_Management_Testing/09-Test_for_Sensitive_Information_in_Log_Files\",\n \"https://cwe.mitre.org/data/definitions/532.html\",\n \"https://cheatsheetseries.owasp.org/cheatsheets/Logging_Cheat_Sheet.html\"\n ],\n \"compliance\": [\"SOC2\", \"GDPR\", \"OWASP A09:2021\"]\n }\n ],\n \"metadata\": {\n \"dependenciesScanned\": 0,\n \"knownVulnerabilities\": 1,\n \"filesAnalyzed\": 8,\n \"criticalIssues\": 1,\n \"highIssues\": 2,\n \"mediumIssues\": 2,\n \"lowIssues\": 0,\n \"generatedAt\": \"2026-01-12T12:00:00Z\",\n \"analysisNotes\": \"Project is in greenfield/spec phase with no source code yet. Security recommendations focus on: (1) Critical exposed credential in .env, (2) Architectural security patterns that should be implemented from the start. The multi-LLM architecture with 4+ API providers creates a significant attack surface that requires careful secrets management.\",\n \"projectPhase\": \"specification\",\n \"riskSummary\": {\n \"immediateAction\": \"Revoke and rotate exposed GitHub token in .auto-claude/.env\",\n \"architecturalPriority\": \"Implement secure secrets management before adding more API integrations\",\n \"owaspCoverage\": [\"A03:2021 Injection\", \"A05:2021 Security Misconfiguration\", \"A07:2021 Identification and Authentication Failures\", \"A09:2021 Security Logging and Monitoring Failures\"]\n }\n }\n}\n"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":971,"cache_read_input_tokens":32401,"cache_creation":{"ephemeral_5m_input_tokens":971,"ephemeral_1h_input_tokens":0},"output_tokens":3828,"service_tier":"standard"}},"requestId":"req_011CX3zdQGjWcCLLR8np4HtR","type":"assistant","uuid":"ab5387e4-b35c-4336-b1b9-f0734ab01af6","timestamp":"2026-01-12T18:57:52.753Z"}
{"parentUuid":"ab5387e4-b35c-4336-b1b9-f0734ab01af6","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011nUpXSER1wr4tpLpzGsmgG","type":"tool_result","content":"File created successfully at: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\security_hardening_ideas.json"}]},"uuid":"b8184d86-e9c1-450b-b33d-ba27b1d8d992","timestamp":"2026-01-12T18:58:02.028Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\ideation\\security_hardening_ideas.json","content":"{\n \"security_hardening\": [\n {\n \"id\": \"sec-001\",\n \"type\": \"security_hardening\",\n \"title\": \"Remove exposed GitHub token from .env file\",\n \"description\": \"The file .auto-claude/.env contains a hardcoded GitHub OAuth token (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). Even though .auto-claude/ is in .gitignore, this token should be revoked and regenerated immediately, as it may have been exposed in various ways (backup systems, local file sharing, etc.).\",\n \"rationale\": \"Exposed API tokens can allow attackers to perform actions on GitHub repositories, including reading private code, modifying repositories, or accessing organization data. The 'gho_' prefix indicates this is a GitHub OAuth token with potentially broad permissions.\",\n \"category\": \"secrets_management\",\n \"severity\": \"critical\",\n \"affectedFiles\": [\".auto-claude/.env\"],\n \"vulnerability\": \"CWE-798: Use of Hard-coded Credentials\",\n \"currentRisk\": \"Token may be used by attackers to access GitHub resources, modify code, or exfiltrate data from private repositories\",\n \"remediation\": \"1. Immediately revoke this token in GitHub settings (Settings > Developer settings > Personal access tokens). 2. Generate a new token with minimal required scopes. 3. Store the new token in a system environment variable or secure secret manager, not in files. 4. Add *.env to .gitignore at the root level as additional protection. 5. Consider using a .env.example file with placeholder values instead.\",\n \"references\": [\n \"https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/token-expiration-and-revocation\",\n \"https://cwe.mitre.org/data/definitions/798.html\"\n ],\n \"compliance\": [\"SOC2\", \"PCI-DSS\", \"OWASP A07:2021\"]\n },\n {\n \"id\": \"sec-002\",\n \"type\": \"security_hardening\",\n \"title\": \"Implement secure API key rotation and vault storage for LLM providers\",\n \"description\": \"The planned architecture requires API keys for 4+ LLM providers (Anthropic, Google/Gemini, xAI/Grok, Gitea). The spec shows these stored in environment variables, but lacks a secure key rotation strategy or centralized secret management.\",\n \"rationale\": \"Multiple high-value API keys without rotation increase the blast radius of any credential leak. LLM API keys often have significant financial implications if misused (token costs can accumulate rapidly).\",\n \"category\": \"secrets_management\",\n \"severity\": \"high\",\n \"affectedFiles\": [\".env.example\", \"backend/main.py\", \"backend/llm/unified_client.py\"],\n \"vulnerability\": \"CWE-522: Insufficiently Protected Credentials\",\n \"currentRisk\": \"Compromised API keys could result in: (1) Unauthorized use leading to high API costs, (2) Data exfiltration through LLM prompts, (3) No ability to quickly rotate credentials in incident response\",\n \"remediation\": \"1. Use a secrets manager (HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault) instead of .env files for production. 2. Implement automatic key rotation with at least 90-day rotation for all LLM provider keys. 3. Create a key_manager.py service that abstracts secret retrieval and supports both local dev (.env) and production (vault) modes. 4. Add runtime validation that keys are loaded at startup and log warnings (not keys themselves) if missing. 5. Implement API key usage monitoring to detect anomalous usage patterns.\",\n \"references\": [\n \"https://owasp.org/www-project-web-security-testing-guide/latest/4-Web_Application_Security_Testing/04-Authentication_Testing/01-Testing_for_Credentials_Transported_over_an_Encrypted_Channel\",\n \"https://cheatsheetseries.owasp.org/cheatsheets/Secrets_Management_Cheat_Sheet.html\"\n ],\n \"compliance\": [\"SOC2\", \"ISO 27001\", \"OWASP A07:2021\"]\n },\n {\n \"id\": \"sec-003\",\n \"type\": \"security_hardening\",\n \"title\": \"Add comprehensive input validation and sanitization for FastAPI endpoints\",\n \"description\": \"The spec outlines multiple API endpoints (/api/tasks, /api/specs, /api/models) that will accept user input including task descriptions, spec content, and model parameters. Without proper input validation, these endpoints are vulnerable to injection attacks.\",\n \"rationale\": \"User input flows to multiple high-risk destinations: LLM prompts (prompt injection), database queries (SQL injection), Git operations (command injection), and file operations (path traversal). The multi-model architecture increases the attack surface.\",\n \"category\": \"input_validation\",\n \"severity\": \"high\",\n \"affectedFiles\": [\n \"backend/api/routes/tasks.py\",\n \"backend/api/routes/specs.py\",\n \"backend/services/orchestrator.py\",\n \"backend/git/git_service.py\"\n ],\n \"vulnerability\": \"CWE-20: Improper Input Validation, CWE-89: SQL Injection, CWE-78: OS Command Injection\",\n \"currentRisk\": \"Without validation: (1) Prompt injection could manipulate LLM behavior, (2) SQL injection could compromise the database, (3) Command injection in git operations could execute arbitrary code, (4) Path traversal could access/modify unauthorized files\",\n \"remediation\": \"1. Use Pydantic models with strict validators for all API inputs (already planned, ensure implementation). 2. Create a sanitization module with functions for: SQL (use parameterized queries only), Shell (reject special characters in branch names/commit messages), File paths (validate against allowed directories). 3. Implement content-length limits on all text inputs (task descriptions, spec content). 4. Add rate limiting per-endpoint using slowapi or similar. 5. Create an input validation middleware that logs and blocks suspicious patterns. 6. For LLM prompts, implement prompt injection detection using pattern matching and semantic analysis.\",\n \"references\": [\n \"https://owasp.org/www-community/attacks/SQL_Injection\",\n \"https://owasp.org/www-community/attacks/Command_Injection\",\n \"https://cwe.mitre.org/data/definitions/20.html\"\n ],\n \"compliance\": [\"SOC2\", \"PCI-DSS\", \"OWASP A03:2021\"]\n },\n {\n \"id\": \"sec-004\",\n \"type\": \"security_hardening\",\n \"title\": \"Configure security headers and CORS policy for FastAPI backend\",\n \"description\": \"The planned FastAPI backend will expose APIs consumed by the React frontend. Without proper security headers and CORS configuration, the application is vulnerable to XSS, clickjacking, and cross-origin attacks.\",\n \"rationale\": \"The spec mentions the frontend at localhost:5173 consuming the backend at localhost:8000. Without CORS restrictions, any malicious website could make requests to the backend. Missing security headers expose users to various client-side attacks.\",\n \"category\": \"configuration\",\n \"severity\": \"medium\",\n \"affectedFiles\": [\n \"backend/main.py\",\n \"frontend/vite.config.ts\"\n ],\n \"vulnerability\": \"CWE-346: Origin Validation Error, CWE-1021: Improper Restriction of Rendered UI Layers\",\n \"currentRisk\": \"Without configuration: (1) CSRF attacks could perform unauthorized actions, (2) XSS could steal session data or API keys temporarily stored in browser, (3) Clickjacking could trick users into unintended actions\",\n \"remediation\": \"1. Add CORS middleware with explicit allowed origins (not '*' in production):\\n```python\\nfrom fastapi.middleware.cors import CORSMiddleware\\napp.add_middleware(\\n CORSMiddleware,\\n allow_origins=['http://localhost:5173'], # Production: specific domains only\\n allow_credentials=True,\\n allow_methods=['GET', 'POST', 'PUT', 'DELETE'],\\n allow_headers=['*'],\\n)\\n```\\n2. Add security headers middleware:\\n```python\\nfrom starlette.middleware import Middleware\\n# X-Content-Type-Options: nosniff\\n# X-Frame-Options: DENY\\n# Strict-Transport-Security: max-age=31536000\\n# Content-Security-Policy: default-src 'self'\\n```\\n3. Use httpOnly and secure flags for any cookies. 4. Implement CSRF tokens for state-changing operations.\",\n \"references\": [\n \"https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS\",\n \"https://owasp.org/www-project-secure-headers/\",\n \"https://cheatsheetseries.owasp.org/cheatsheets/Cross-Site_Request_Forgery_Prevention_Cheat_Sheet.html\"\n ],\n \"compliance\": [\"SOC2\", \"OWASP A05:2021\"]\n },\n {\n \"id\": \"sec-005\",\n \"type\": \"security_hardening\",\n \"title\": \"Implement secure logging practices to prevent credential and PII exposure\",\n \"description\": \"The spec requires tracking LLM interactions, costs, and task history. Without careful logging practices, sensitive data (API keys, user inputs, LLM responses) could be exposed in logs, console output, or error messages.\",\n \"rationale\": \"The system will process: (1) Multiple API keys for LLM providers, (2) User task descriptions that may contain sensitive business data, (3) LLM responses that could contain generated secrets or PII. Logs are often stored insecurely and accessed by multiple parties.\",\n \"category\": \"data_protection\",\n \"severity\": \"medium\",\n \"affectedFiles\": [\n \"backend/main.py\",\n \"backend/llm/unified_client.py\",\n \"backend/llm/routing/cost_tracker.py\",\n \"backend/services/orchestrator.py\"\n ],\n \"vulnerability\": \"CWE-532: Insertion of Sensitive Information into Log File\",\n \"currentRisk\": \"Without secure logging: (1) API keys could appear in debug logs, (2) PII in user prompts could be logged, (3) Error tracebacks could expose internal paths and configurations, (4) LLM responses with generated credentials could be persisted\",\n \"remediation\": \"1. Create a logging configuration that sanitizes sensitive patterns:\\n```python\\nimport re\\nclass SensitiveDataFilter(logging.Filter):\\n PATTERNS = [\\n (r'sk-ant-[a-zA-Z0-9-]+', '[ANTHROPIC_KEY_REDACTED]'),\\n (r'AIza[a-zA-Z0-9-]+', '[GOOGLE_KEY_REDACTED]'),\\n (r'xai-[a-zA-Z0-9-]+', '[XAI_KEY_REDACTED]'),\\n (r'gh[ops]_[a-zA-Z0-9]+', '[GITHUB_TOKEN_REDACTED]'),\\n ]\\n```\\n2. Set DEBUG mode to False in production and use structured logging. 3. Implement log rotation and secure storage with restricted access. 4. Create a separate audit log for security events (auth failures, rate limit hits). 5. Never log full LLM prompts/responses in production - log only metadata (token counts, model used, latency). 6. Use exception handlers that return generic error messages to clients while logging full details server-side.\",\n \"references\": [\n \"https://owasp.org/www-project-web-security-testing-guide/latest/4-Web_Application_Security_Testing/02-Configuration_and_Deployment_Management_Testing/09-Test_for_Sensitive_Information_in_Log_Files\",\n \"https://cwe.mitre.org/data/definitions/532.html\",\n \"https://cheatsheetseries.owasp.org/cheatsheets/Logging_Cheat_Sheet.html\"\n ],\n \"compliance\": [\"SOC2\", \"GDPR\", \"OWASP A09:2021\"]\n }\n ],\n \"metadata\": {\n \"dependenciesScanned\": 0,\n \"knownVulnerabilities\": 1,\n \"filesAnalyzed\": 8,\n \"criticalIssues\": 1,\n \"highIssues\": 2,\n \"mediumIssues\": 2,\n \"lowIssues\": 0,\n \"generatedAt\": \"2026-01-12T12:00:00Z\",\n \"analysisNotes\": \"Project is in greenfield/spec phase with no source code yet. Security recommendations focus on: (1) Critical exposed credential in .env, (2) Architectural security patterns that should be implemented from the start. The multi-LLM architecture with 4+ API providers creates a significant attack surface that requires careful secrets management.\",\n \"projectPhase\": \"specification\",\n \"riskSummary\": {\n \"immediateAction\": \"Revoke and rotate exposed GitHub token in .auto-claude/.env\",\n \"architecturalPriority\": \"Implement secure secrets management before adding more API integrations\",\n \"owaspCoverage\": [\"A03:2021 Injection\", \"A05:2021 Security Misconfiguration\", \"A07:2021 Identification and Authentication Failures\", \"A09:2021 Security Logging and Monitoring Failures\"]\n }\n }\n}\n","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"ab5387e4-b35c-4336-b1b9-f0734ab01af6"}
{"parentUuid":"b8184d86-e9c1-450b-b33d-ba27b1d8d992","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_01QkgWWLazzNkZvZzFUzAtsk","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I've written the security hardening ideas to the output file. Let me summarize what I found for the user.","signature":"EpECCkYICxgCKkCkAJXSJeS+Y2vu9xHSx1ilrk36qW1li3tdRiMamJP0aENVdz7qr6oVzUYeplP/k2kJU4gFxorhf3k6R/FLdmfDEgw8R6fy+yhiL8cPKIcaDL+ea0OOyBPFbF+tmyIwDaWr/N6uBjJ7WeB7/M6JvYdMWdFLJ2XNXagpAgmdF3PGl8A5Ryeo3lDh1Yft0cVOKnm3xFTij6a8HNZFcz2TQ/UFGMZ+kza1yHvmEPHoa+Zpb5aLwV9KzDSCqgN6BBbSZXStO7v9YbJg32jfjuTjG4+L7rd1QTSL1dPq4btD1y28WMjWJiB56iONry28e2WcBZHbsxqu2OqAdZQYjnmzpBGsNKt59BLqwqEhGAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3885,"cache_read_input_tokens":33372,"cache_creation":{"ephemeral_5m_input_tokens":3885,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard"}},"requestId":"req_011CX3zjfu1D9hvdeTQQD7nm","type":"assistant","uuid":"f80f4e7b-1deb-4e0a-8e44-1a67b24f0003","timestamp":"2026-01-12T18:58:20.962Z"}
{"parentUuid":"f80f4e7b-1deb-4e0a-8e44-1a67b24f0003","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"a36a31e0-d5c2-409b-a499-dbcc3f2b9c8b","version":"2.1.5","gitBranch":"master","slug":"sleepy-forging-dewdrop","message":{"model":"claude-opus-4-5-20251101","id":"msg_01QkgWWLazzNkZvZzFUzAtsk","type":"message","role":"assistant","content":[{"type":"text","text":"I've completed the security analysis and written 5 security hardening ideas to `security_hardening_ideas.json`. Here's a summary:\n\n## Security Analysis Summary\n\n### 🚨 Critical Finding\n**Exposed GitHub Token** - The `.auto-claude/.env` file contains a hardcoded GitHub OAuth token (`gho_4V66...`). This token should be **revoked immediately** in GitHub settings and regenerated.\n\n### Security Hardening Ideas Generated\n\n| ID | Severity | Title | Category |\n|----|----------|-------|----------|\n| sec-001 | **CRITICAL** | Remove exposed GitHub token from .env file | Secrets Management |\n| sec-002 | **HIGH** | Implement secure API key rotation and vault storage for LLM providers | Secrets Management |\n| sec-003 | **HIGH** | Add comprehensive input validation and sanitization for FastAPI endpoints | Input Validation |\n| sec-004 | **MEDIUM** | Configure security headers and CORS policy for FastAPI backend | Configuration |\n| sec-005 | **MEDIUM** | Implement secure logging practices to prevent credential and PII exposure | Data Protection |\n\n### Key Observations\n\n1. **Project Phase**: This is a greenfield project with specifications but no implemented code yet - an ideal time to establish secure patterns.\n\n2. **Attack Surface**: The multi-LLM architecture with 4+ API providers (Anthropic, Google, xAI, Ollama) creates a significant attack surface requiring careful secrets management.\n\n3. **OWASP Coverage**: The recommendations address:\n - A03:2021 Injection\n - A05:2021 Security Misconfiguration \n - A07:2021 Identification and Authentication Failures\n - A09:2021 Security Logging and Monitoring Failures\n\n### Immediate Action Required\n**Revoke the exposed GitHub token** at: GitHub Settings → Developer settings → Personal access tokens"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3885,"cache_read_input_tokens":33372,"cache_creation":{"ephemeral_5m_input_tokens":3885,"ephemeral_1h_input_tokens":0},"output_tokens":473,"service_tier":"standard"}},"requestId":"req_011CX3zjfu1D9hvdeTQQD7nm","type":"assistant","uuid":"4f7b97db-ba97-4382-bdb2-66c48ae2ab4a","timestamp":"2026-01-12T18:58:26.517Z"}
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