mirror of
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* refactor(docs): move core docs into framework/business/research (move-only) - framework/: principles, rules, flags (思想・行動規範) - business/: symbols, examples (ビジネス領域) - research/: config (調査設定) - All files renamed to lowercase for consistency * docs: update references to new directory structure - Update ~/.claude/CLAUDE.md with new paths - Add migration notice in core/MOVED.md - Remove pm.md.backup - All @superclaude/ references now point to framework/business/research/ * fix(setup): update framework_docs to use new directory structure - Add validate_prerequisites() override for multi-directory validation - Add _get_source_dirs() for framework/business/research directories - Override _discover_component_files() for multi-directory discovery - Override get_files_to_install() for relative path handling - Fix get_size_estimate() to use get_files_to_install() - Fix uninstall/update/validate to use install_component_subdir Fixes installation validation errors for new directory structure. Tested: make dev installs successfully with new structure - framework/: flags.md, principles.md, rules.md - business/: examples.md, symbols.md - research/: config.md * refactor(modes): update component references for docs restructure * chore: remove redundant docs after PLANNING.md migration Cleanup after Self-Improvement Loop implementation: **Deleted (21 files, ~210KB)**: - docs/Development/ - All content migrated to PLANNING.md & TASK.md * ARCHITECTURE.md (15KB) → PLANNING.md * TASKS.md (3.7KB) → TASK.md * ROADMAP.md (11KB) → TASK.md * PROJECT_STATUS.md (4.2KB) → outdated * 13 PM Agent research files → archived in KNOWLEDGE.md - docs/PM_AGENT.md - Old implementation status - docs/pm-agent-implementation-status.md - Duplicate - docs/templates/ - Empty directory **Retained (valuable documentation)**: - docs/memory/ - Active session metrics & context - docs/patterns/ - Reusable patterns - docs/research/ - Research reports - docs/user-guide*/ - User documentation (4 languages) - docs/reference/ - Reference materials - docs/getting-started/ - Quick start guides - docs/agents/ - Agent-specific guides - docs/testing/ - Test procedures **Result**: - Eliminated redundancy after Root Documents consolidation - Preserved all valuable content in PLANNING.md, TASK.md, KNOWLEDGE.md - Maintained user-facing documentation structure 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * refactor: relocate PM modules to commands/modules - Move modules to superclaude/commands/modules/ - Organize command-specific modules under commands/ 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add self-improvement loop with 4 root documents Implements Self-Improvement Loop based on Cursor's proven patterns: **New Root Documents**: - PLANNING.md: Architecture, design principles, 10 absolute rules - TASK.md: Current tasks with priority (🔴🟡🟢⚪) - KNOWLEDGE.md: Accumulated insights, best practices, failures - README.md: Updated with developer documentation links **Key Features**: - Session Start Protocol: Read docs → Git status → Token budget → Ready - Evidence-Based Development: No guessing, always verify - Parallel Execution Default: Wave → Checkpoint → Wave pattern - Mac Environment Protection: Docker-first, no host pollution - Failure Pattern Learning: Past mistakes become prevention rules **Cleanup**: - Removed: docs/memory/checkpoint.json, current_plan.json (migrated to TASK.md) - Enhanced: setup/components/commands.py (module discovery) **Benefits**: - LLM reads rules at session start → consistent quality - Past failures documented → no repeats - Progressive knowledge accumulation → continuous improvement - 3.5x faster execution with parallel patterns 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * test: validate Self-Improvement Loop workflow Tested complete cycle: Read docs → Extract rules → Execute task → Update docs Test Results: - Session Start Protocol: ✅ All 6 steps successful - Rule Extraction: ✅ 10/10 absolute rules identified from PLANNING.md - Task Identification: ✅ Next tasks identified from TASK.md - Knowledge Application: ✅ Failure patterns accessed from KNOWLEDGE.md - Documentation Update: ✅ TASK.md and KNOWLEDGE.md updated with completed work - Confidence Score: 95% (exceeds 70% threshold) Proved Self-Improvement Loop closes: Execute → Learn → Update → Improve * refactor: responsibility-driven component architecture Rename components to reflect their responsibilities: - framework_docs.py → knowledge_base.py (KnowledgeBaseComponent) - modes.py → behavior_modes.py (BehaviorModesComponent) - agents.py → agent_personas.py (AgentPersonasComponent) - commands.py → slash_commands.py (SlashCommandsComponent) - mcp.py → mcp_integration.py (MCPIntegrationComponent) Each component now clearly documents its responsibility: - knowledge_base: Framework knowledge initialization - behavior_modes: Execution mode definitions - agent_personas: AI agent personality definitions - slash_commands: CLI command registration - mcp_integration: External tool integration Benefits: - Self-documenting architecture - Clear responsibility boundaries - Easy to navigate and extend - Scalable for future hierarchical organization 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * docs: add project-specific CLAUDE.md with UV rules - Document UV as required Python package manager - Add common operations and integration examples - Document project structure and component architecture - Provide development workflow guidelines 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: resolve installation failures after framework_docs rename ## Problems Fixed 1. **Syntax errors**: Duplicate docstrings in all component files (line 1) 2. **Dependency mismatch**: Stale framework_docs references after rename to knowledge_base ## Changes - Fix docstring format in all component files (behavior_modes, agent_personas, slash_commands, mcp_integration) - Update all dependency references: framework_docs → knowledge_base - Update component registration calls in knowledge_base.py (5 locations) - Update install.py files in both setup/ and superclaude/ (5 locations total) - Fix documentation links in README-ja.md and README-zh.md ## Verification ✅ All components load successfully without syntax errors ✅ Dependency resolution works correctly ✅ Installation completes in 0.5s with all validations passing ✅ make dev succeeds 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add automated README translation workflow ## New Features - **Auto-translation workflow** using GPT-Translate - Automatically translates README.md to Chinese (ZH) and Japanese (JA) - Triggers on README.md changes to master/main branches - Cost-effective: ~¥90/month for typical usage ## Implementation Details - Uses OpenAI GPT-4 for high-quality translations - GitHub Actions integration with gpt-translate@v1.1.11 - Secure API key management via GitHub Secrets - Automatic commit and PR creation on translation updates ## Files Added - `.github/workflows/translation-sync.yml` - Auto-translation workflow - `docs/Development/translation-workflow.md` - Setup guide and documentation ## Setup Required Add `OPENAI_API_KEY` to GitHub repository secrets to enable auto-translation. ## Benefits - 🤖 Automated translation on every README update - 💰 Low cost (~$0.06 per translation) - 🛡️ Secure API key storage - 🔄 Consistent translation quality across languages 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix(mcp): update airis-mcp-gateway URL to correct organization Fixes #440 ## Problem Code referenced non-existent `oraios/airis-mcp-gateway` repository, causing MCP installation to fail completely. ## Root Cause - Repository was moved to organization: `agiletec-inc/airis-mcp-gateway` - Old reference `oraios/airis-mcp-gateway` no longer exists - Users reported "not a python/uv module" error ## Changes - Update install_command URL: oraios → agiletec-inc - Update run_command URL: oraios → agiletec-inc - Location: setup/components/mcp_integration.py lines 37-38 ## Verification ✅ Correct URL now references active repository ✅ MCP installation will succeed with proper organization ✅ No other code references oraios/airis-mcp-gateway ## Related Issues - Fixes #440 (Airis-mcp-gateway url has changed) - Related to #442 (MCP update issues) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: replace cloud translation with local Neural CLI ## Changes ### Removed (OpenAI-dependent) - ❌ `.github/workflows/translation-sync.yml` - GPT-Translate workflow - ❌ `docs/Development/translation-workflow.md` - OpenAI setup docs ### Added (Local Ollama-based) - ✅ `Makefile`: New `make translate` target using Neural CLI - ✅ `docs/Development/translation-guide.md` - Neural CLI guide ## Benefits **Before (GPT-Translate)**: - 💰 Monthly cost: ~¥90 (OpenAI API) - 🔑 Requires API key setup - 🌐 Data sent to external API - ⏱️ Network latency **After (Neural CLI)**: - ✅ **$0 cost** - Fully local execution - ✅ **No API keys** - Zero setup friction - ✅ **Privacy** - No external data transfer - ✅ **Fast** - ~1-2 min per README - ✅ **Offline capable** - Works without internet ## Technical Details **Neural CLI**: - Built in Rust with Tauri - Uses Ollama + qwen2.5:3b model - Binary size: 4.0MB - Auto-installs to ~/.local/bin/ **Usage**: ```bash make translate # Translates README.md → README-zh.md, README-ja.md ``` ## Requirements - Ollama installed: `curl -fsSL https://ollama.com/install.sh | sh` - Model downloaded: `ollama pull qwen2.5:3b` - Neural CLI built: `cd ~/github/neural/src-tauri && cargo build --bin neural-cli --release` 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: kazuki <kazuki@kazukinoMacBook-Air.local> Co-authored-by: Claude <noreply@anthropic.com>
221 lines
5.1 KiB
Markdown
221 lines
5.1 KiB
Markdown
# PM Agent Task Management Workflow
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**Purpose**: Lightweight task tracking and progress documentation integrated with PM Agent's learning system.
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## Design Philosophy
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```yaml
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Storage: docs/memory/tasks/ (visible, searchable, Git-tracked)
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Format: Markdown (human-readable, grep-friendly)
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Lifecycle: Plan → Execute → Document → Learn
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Integration: PM Agent coordinates all phases
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```
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## Task Management Flow
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### 1. Planning Phase
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**Trigger**: Multi-step tasks (>3 steps), complex scope
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**PM Agent Actions**:
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```markdown
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1. Analyze user request
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2. Break down into steps
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3. Identify dependencies
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4. Map parallelization opportunities
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5. Create task plan in memory
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```
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**Output**: Mental model only (no file created yet)
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### 2. Execution Phase
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**During Implementation**:
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```markdown
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1. Execute steps systematically
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2. Track progress mentally
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3. Note blockers and decisions
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4. Adapt plan as needed
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```
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**No intermediate files** - keep execution fast and lightweight.
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### 3. Documentation Phase
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**After Completion** (PM Agent auto-activates):
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```markdown
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1. Extract implementation patterns
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2. Document key decisions
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3. Record learnings
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4. Save to docs/memory/tasks/[date]-[task-name].md
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```
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**Template**:
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```markdown
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# Task: [Name]
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Date: YYYY-MM-DD
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Status: Completed
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## Request
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[Original user request]
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## Implementation Steps
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1. Step 1 - [outcome]
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2. Step 2 - [outcome]
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3. Step 3 - [outcome]
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## Key Decisions
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- Decision 1: [rationale]
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- Decision 2: [rationale]
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## Patterns Discovered
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- Pattern 1: [description]
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- Pattern 2: [description]
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## Learnings
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- Learning 1
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- Learning 2
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## Files Modified
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- file1.ts: [changes]
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- file2.py: [changes]
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```
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### 4. Learning Phase
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**PM Agent Knowledge Extraction**:
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```markdown
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1. Identify reusable patterns
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2. Extract to docs/patterns/ if applicable
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3. Update PM Agent knowledge base
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4. Prune outdated patterns
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```
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## When to Use Task Management
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**Use When**:
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- Complex multi-step operations (>3 steps)
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- Cross-file refactoring
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- Learning-worthy implementations
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- Need to track decisions
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**Skip When**:
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- Simple single-file edits
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- Trivial bug fixes
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- Routine operations
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- Quick experiments
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## Storage Structure
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```
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docs/
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└── memory/
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└── tasks/
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├── 2025-10-17-auth-implementation.md
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├── 2025-10-17-api-redesign.md
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└── README.md (index of all tasks)
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```
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## Integration with PM Agent
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```yaml
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PM Agent Activation Points:
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1. Task Planning: Analyze and break down
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2. Mid-Task: Note blockers and pivots
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3. Post-Task: Extract patterns and document
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4. Monthly: Review and prune task history
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PM Agent Responsibilities:
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- Task complexity assessment
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- Step breakdown and dependency mapping
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- Pattern extraction and knowledge capture
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- Documentation quality and pruning
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```
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## Comparison: Old vs New
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```yaml
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Old Design (Serena + TodoWrite):
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Storage: ~/.claude/todos/*.json (invisible)
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Format: JSON (machine-only)
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Lifecycle: Created → Abandoned → Garbage
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Result: Empty files, wasted tokens
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New Design (PM Agent + Markdown):
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Storage: docs/memory/tasks/*.md (visible)
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Format: Markdown (human-readable)
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Lifecycle: Plan → Execute → Document → Learn
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Result: Knowledge accumulation, no garbage
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```
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## Example Workflow
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**User**: "Implement JWT authentication"
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**PM Agent Planning**:
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```markdown
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Mental breakdown:
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1. Install dependencies (parallel: jwt lib + types)
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2. Create middleware (sequential: after deps)
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3. Add route protection (parallel: multiple routes)
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4. Write tests (sequential: after implementation)
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Estimated: 4 main steps, 2 parallelizable
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```
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**Execution**: PM Agent coordinates, no files created
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**Documentation** (after completion):
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```markdown
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File: docs/memory/tasks/2025-10-17-jwt-auth.md
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# Task: JWT Authentication Implementation
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Date: 2025-10-17
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Status: Completed
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## Request
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Implement JWT authentication for API routes
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## Implementation Steps
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1. Dependencies - Installed jsonwebtoken + @types/jsonwebtoken
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2. Middleware - Created auth.middleware.ts with token validation
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3. Route Protection - Applied to /api/user/* routes
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4. Tests - Added 8 test cases (auth.test.ts)
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## Key Decisions
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- Used RS256 (not HS256) for better security
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- 15min access token, 7day refresh token
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- Stored keys in environment variables
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## Patterns Discovered
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- Middleware composition pattern for auth chains
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- Error handling with custom AuthError class
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## Files Modified
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- src/middleware/auth.ts: New auth middleware
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- src/routes/user.ts: Applied middleware
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- tests/auth.test.ts: New test suite
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```
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## Benefits
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```yaml
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Visibility: All tasks visible in docs/memory/
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Searchability: grep-friendly markdown
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Git History: Task evolution tracked
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Learning: Patterns extracted automatically
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No Garbage: Only completed, valuable tasks saved
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```
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## Anti-Patterns
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❌ **Don't**: Create task file before completion
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❌ **Don't**: Document trivial operations
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❌ **Don't**: Create TODO comments in code
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❌ **Don't**: Use for session management (separate concern)
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✅ **Do**: Let PM Agent decide when to document
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✅ **Do**: Focus on learning and patterns
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✅ **Do**: Keep task files concise
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✅ **Do**: Review and prune old tasks monthly
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