项目文件夹

文件
Mithun Gowda B 116e9fc5f9 fix: fill implementation gaps across core modules (#544)
* fix: fill implementation gaps across core modules

- Replace ConfidenceChecker placeholder methods with real implementations
  that search the codebase for duplicates, verify architecture docs exist,
  check research references, and validate root cause specificity
- Fix intelligent_execute() error capture: collect actual errors from
  failed tasks instead of hardcoded None, format tracebacks as strings,
  and fix variable shadowing bug where loop var overwrote task parameter
- Implement ReflexionPattern mindbase integration via HTTP API with
  graceful fallback when service is unavailable
- Fix .gitignore: remove duplicate entries, add explicit !-rules for
  .claude/settings.json and .claude/skills/, remove Tests/ ignore
- Remove unnecessary sys.path hack in cli/main.py
- Fix FailureEntry.from_dict to not mutate input dict
- Add comprehensive execution module tests: 62 new tests covering
  ParallelExecutor, ReflectionEngine, SelfCorrectionEngine, and the
  intelligent_execute orchestrator (136 total, all passing)

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* chore: include test-generated reflexion artifacts

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* fix: address 5 open GitHub issues (#536, #537, #531, #517, #534)

Security fixes:
- #536: Remove shell=True and user-controlled $SHELL from _run_command()
  to prevent arbitrary code execution. Use direct list-based subprocess.run
  without passing full os.environ to child processes.
- #537: Add SHA-256 integrity verification for downloaded docker-compose
  and mcp-config files. Downloads are deleted on hash mismatch. Gateway
  config supports pinned hashes via docker_compose_sha256/mcp_config_sha256.

Bug fixes:
- #531: Add agent file installation to `superclaude install` and `update`
  commands. 20 agent markdown files are now copied to ~/.claude/agents/
  alongside command installation.
- #517: Fix MCP env var flag from --env to -e for API key passthrough,
  matching the Claude CLI's expected format.

Usability:
- #534: Replace Japanese trigger phrases and report labels in pm-agent.md
  and pm.md (both src/ and plugins/) with English equivalents for
  international accessibility.

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* docs: align documentation with Claude Code and fix version/count gaps

- Update CLAUDE.md project structure to include agents/ (20 agents),
  modes/ (7 modes), commands/ (30 commands), skills/, hooks/, mcp/,
  and core/ directories. Add Claude Code integration points section.
- Fix version references: 4.1.5 -> 4.2.0 in installation.md,
  quick-start.md, and package.json (was 4.1.7)
- Fix feature counts across all docs:
  - Commands: 21 -> 30
  - Agents: 14/16 -> 20
  - Modes: 6 -> 7
  - MCP Servers: 6 -> 8
- Update README.md agent count from 16 to 20
- Add docs/user-guide/claude-code-integration.md explaining how
  SuperClaude maps to Claude Code's native features (commands,
  agents, hooks, skills, settings, MCP servers, pytest plugin)

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* chore: update test-generated reflexion log

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* docs: comprehensive Claude Code gap analysis and integration guide

- Rewrite docs/user-guide/claude-code-integration.md with full feature
  mapping: all 28 hook events, skills system with YAML frontmatter,
  5 settings scopes, permission rules, plan mode, extended thinking,
  agent teams, voice, desktop features, and session management.
  Includes detailed gap table showing where SuperClaude under-uses
  Claude Code capabilities (skills migration, hooks integration,
  plan mode, settings profiles).
- Add Claude Code native features section to CLAUDE.md with extension
  points we use vs should use more (hooks, skills, plan mode, settings)
- Add Claude Code integration gap analysis to KNOWLEDGE.md with
  prioritized action items for skills migration, hooks leverage,
  plan mode integration, and settings profiles

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* chore: update test-generated reflexion log

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* chore: bump version to 4.3.0

Bump version across all 15 files:
- VERSION, pyproject.toml, package.json
- src/superclaude/__init__.py, src/superclaude/__version__.py
- CLAUDE.md, PLANNING.md, TASK.md, CHANGELOG.md
- README.md, README-zh.md, README-ja.md, README-kr.md
- docs/getting-started/installation.md, quick-start.md
- docs/Development/pm-agent-integration.md

Also fixes __version__.py which was out of sync at 0.4.0.
Adds comprehensive CHANGELOG entry for v4.3.0.

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

* i18n: replace all Japanese/Chinese text with English in source files

Replace CJK text with English across all non-translation files:

- src/superclaude/commands/pm.md: 38 Japanese strings in PDCA cycle,
  error handling patterns, anti-patterns, document templates
- src/superclaude/agents/pm-agent.md: 20 Japanese strings in PDCA
  phases, self-evaluation, documentation sections
- plugins/superclaude/: synced from src/ copies
- .github/workflows/readme-quality-check.yml: all Chinese comments,
  table headers, report strings, and PR comment text
- .github/workflows/pull-sync-framework.yml: Japanese comment
- .github/PULL_REQUEST_TEMPLATE.md: complete rewrite from Japanese

Translation files (README-ja.md, docs/user-guide-jp/, etc.) are
intentionally kept in their respective languages.

https://claude.ai/code/session_01AnGJMAA6Qp2j9WKKHHZfB9

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-03-22 22:57:15 +05:30

478 行
11 KiB
Markdown

# PM Agent Mode Integration Guide
**Last Updated**: 2025-10-14
**Target Version**: 4.3.0
**Status**: Implementation Guide
---
## 📋 Overview
This guide provides step-by-step procedures for integrating PM Agent mode as SuperClaude's always-active meta-layer with session lifecycle management, PDCA self-evaluation, and systematic knowledge management.
---
## 🎯 Integration Goals
1. **Session Lifecycle**: Auto-activation at session start with context restoration
2. **PDCA Engine**: Automated Plan-Do-Check-Act cycle execution
3. **Memory Operations**: Serena MCP integration for session persistence
4. **Documentation Strategy**: Systematic knowledge evolution
---
## 📐 Architecture Integration
### PM Agent Position
```
┌──────────────────────────────────────────┐
│ PM Agent Mode (Meta-Layer) │
│ • Always Active │
│ • Session Management │
│ • PDCA Self-Evaluation │
└──────────────┬───────────────────────────┘
[Specialist Agents Layer]
[Commands & Modes Layer]
[MCP Tool Layer]
```
See: [ARCHITECTURE.md](./ARCHITECTURE.md) for full system architecture
---
## 🔧 Phase 2: Core Implementation
### File Structure
```
superclaude/
├── Commands/
│ └── pm.md # ✅ Already updated
├── Agents/
│ └── pm-agent.md # ✅ Already updated
└── Core/
├── __init__.py # Module initialization
├── session_lifecycle.py # 🆕 Session management
├── pdca_engine.py # 🆕 PDCA automation
└── memory_ops.py # 🆕 Memory operations
```
### Implementation Order
1. `memory_ops.py` - Serena MCP wrapper (foundation)
2. `session_lifecycle.py` - Session management (depends on memory_ops)
3. `pdca_engine.py` - PDCA automation (depends on memory_ops)
---
## 1️⃣ memory_ops.py Implementation
### Purpose
Wrapper for Serena MCP memory operations with error handling and fallback.
### Key Functions
```python
# superclaude/Core/memory_ops.py
class MemoryOperations:
"""Serena MCP memory operations wrapper"""
def list_memories() -> List[str]:
"""List all available memories"""
def read_memory(key: str) -> Optional[Dict]:
"""Read memory by key"""
def write_memory(key: str, value: Dict) -> bool:
"""Write memory with key"""
def delete_memory(key: str) -> bool:
"""Delete memory by key"""
```
### Integration Points
- Connect to Serena MCP server
- Handle connection errors gracefully
- Provide fallback for offline mode
- Validate memory structure
### Testing
```bash
pytest tests/test_memory_ops.py -v
```
---
## 2️⃣ session_lifecycle.py Implementation
### Purpose
Auto-activation at session start, context restoration, user report generation.
### Key Functions
```python
# superclaude/Core/session_lifecycle.py
class SessionLifecycle:
"""Session lifecycle management"""
def on_session_start():
"""Hook for session start (auto-activation)"""
# 1. list_memories()
# 2. read_memory("pm_context")
# 3. read_memory("last_session")
# 4. read_memory("next_actions")
# 5. generate_user_report()
def generate_user_report() -> str:
"""Generate user report (前回/進捗/今回/課題)"""
def on_session_end():
"""Hook for session end (checkpoint save)"""
# 1. write_memory("last_session", summary)
# 2. write_memory("next_actions", todos)
# 3. write_memory("pm_context", complete_state)
```
### User Report Format
```
前回: [last session summary]
進捗: [current progress status]
今回: [planned next actions]
課題: [blockers or issues]
```
### Integration Points
- Hook into Claude Code session start
- Read memories using memory_ops
- Generate human-readable report
- Handle missing or corrupted memory
### Testing
```bash
pytest tests/test_session_lifecycle.py -v
```
---
## 3️⃣ pdca_engine.py Implementation
### Purpose
Automate PDCA cycle execution with documentation generation.
### Key Functions
```python
# superclaude/Core/pdca_engine.py
class PDCAEngine:
"""PDCA cycle automation"""
def plan_phase(goal: str):
"""Generate hypothesis (仮説)"""
# 1. write_memory("plan", goal)
# 2. Create docs/temp/hypothesis-YYYY-MM-DD.md
def do_phase():
"""Track experimentation (実験)"""
# 1. TodoWrite tracking
# 2. write_memory("checkpoint", progress) every 30min
# 3. Update docs/temp/experiment-YYYY-MM-DD.md
def check_phase():
"""Self-evaluation (評価)"""
# 1. think_about_task_adherence()
# 2. think_about_whether_you_are_done()
# 3. Create docs/temp/lessons-YYYY-MM-DD.md
def act_phase():
"""Knowledge extraction (改善)"""
# 1. Success → docs/patterns/[pattern-name].md
# 2. Failure → docs/mistakes/mistake-YYYY-MM-DD.md
# 3. Update CLAUDE.md if global pattern
```
### Documentation Templates
**hypothesis-template.md**:
```markdown
# Hypothesis: [Goal Description]
Date: YYYY-MM-DD
Status: Planning
## Goal
What are we trying to accomplish?
## Approach
How will we implement this?
## Success Criteria
How do we know when we're done?
## Potential Risks
What could go wrong?
```
**experiment-template.md**:
```markdown
# Experiment Log: [Implementation Name]
Date: YYYY-MM-DD
Status: In Progress
## Implementation Steps
- [ ] Step 1
- [ ] Step 2
## Errors Encountered
- Error 1: Description, solution
## Solutions Applied
- Solution 1: Description, result
## Checkpoint Saves
- 10:00: [progress snapshot]
- 10:30: [progress snapshot]
```
### Integration Points
- Create docs/ directory templates
- Integrate with TodoWrite
- Call Serena MCP think operations
- Generate documentation files
### Testing
```bash
pytest tests/test_pdca_engine.py -v
```
---
## 🔌 Phase 3: Serena MCP Integration
### Prerequisites
```bash
# Install Serena MCP server
# See: docs/troubleshooting/serena-installation.md
```
### Configuration
```json
// ~/.claude/.claude.json
{
"mcpServers": {
"serena": {
"command": "uv",
"args": ["run", "serena-mcp"]
}
}
}
```
### Memory Structure
```json
{
"pm_context": {
"project": "SuperClaude_Framework",
"current_phase": "Phase 2",
"architecture": "Context-Oriented Configuration",
"patterns": ["PDCA Cycle", "Session Lifecycle"]
},
"last_session": {
"date": "2025-10-14",
"accomplished": ["Phase 1 complete"],
"issues": ["Serena MCP not configured"],
"learned": ["Session Lifecycle pattern"]
},
"next_actions": [
"Implement session_lifecycle.py",
"Configure Serena MCP",
"Test memory operations"
]
}
```
### Testing Serena Connection
```bash
# Test memory operations
python -m SuperClaude.Core.memory_ops --test
```
---
## 📁 Phase 4: Documentation Strategy
### Directory Structure
```
docs/
├── temp/ # Temporary (7-day lifecycle)
│ ├── hypothesis-YYYY-MM-DD.md
│ ├── experiment-YYYY-MM-DD.md
│ └── lessons-YYYY-MM-DD.md
├── patterns/ # Formal patterns (永久保存)
│ └── [pattern-name].md
└── mistakes/ # Mistake records (永久保存)
└── mistake-YYYY-MM-DD.md
```
### Lifecycle Automation
```bash
# Create cleanup script
scripts/cleanup_temp_docs.sh
# Run daily via cron
0 0 * * * /path/to/scripts/cleanup_temp_docs.sh
```
### Migration Scripts
```bash
# Migrate successful experiments to patterns
python scripts/migrate_to_patterns.py
# Migrate failures to mistakes
python scripts/migrate_to_mistakes.py
```
---
## 🚀 Phase 5: Auto-Activation (Research Needed)
### Research Questions
1. How does Claude Code handle initialization?
2. Are there plugin hooks available?
3. Can we intercept session start events?
### Implementation Plan (TBD)
Once research complete, implement auto-activation hooks:
```python
# superclaude/Core/auto_activation.py (future)
def on_claude_code_start():
"""Auto-activate PM Agent at session start"""
session_lifecycle.on_session_start()
```
---
## ✅ Implementation Checklist
### Phase 2: Core Implementation
- [ ] Implement `memory_ops.py`
- [ ] Write unit tests for memory_ops
- [ ] Implement `session_lifecycle.py`
- [ ] Write unit tests for session_lifecycle
- [ ] Implement `pdca_engine.py`
- [ ] Write unit tests for pdca_engine
- [ ] Integration testing
### Phase 3: Serena MCP
- [ ] Install Serena MCP server
- [ ] Configure `.claude.json`
- [ ] Test memory operations
- [ ] Test think operations
- [ ] Test cross-session persistence
### Phase 4: Documentation Strategy
- [ ] Create `docs/temp/` template
- [ ] Create `docs/patterns/` template
- [ ] Create `docs/mistakes/` template
- [ ] Implement lifecycle automation
- [ ] Create migration scripts
### Phase 5: Auto-Activation
- [ ] Research Claude Code hooks
- [ ] Design auto-activation system
- [ ] Implement auto-activation
- [ ] Test session start behavior
---
## 🧪 Testing Strategy
### Unit Tests
```bash
tests/
├── test_memory_ops.py # Memory operations
├── test_session_lifecycle.py # Session management
└── test_pdca_engine.py # PDCA automation
```
### Integration Tests
```bash
tests/integration/
├── test_pm_agent_flow.py # End-to-end PM Agent
├── test_serena_integration.py # Serena MCP integration
└── test_cross_session.py # Session persistence
```
### Manual Testing
1. Start new session → Verify context restoration
2. Work on task → Verify checkpoint saves
3. End session → Verify state preservation
4. Restart → Verify seamless resumption
---
## 📊 Success Criteria
### Functional
- [ ] PM Agent activates at session start
- [ ] Context restores from memory
- [ ] User report generates correctly
- [ ] PDCA cycle executes automatically
- [ ] Documentation strategy works
### Performance
- [ ] Session start delay <500ms
- [ ] Memory operations <100ms
- [ ] Context restoration reliable (>99%)
### Quality
- [ ] Test coverage >90%
- [ ] No regression in existing features
- [ ] Documentation complete
---
## 🔧 Troubleshooting
### Common Issues
**"Serena MCP not connecting"**
- Check server installation
- Verify `.claude.json` configuration
- Test connection: `claude mcp list`
**"Memory operations failing"**
- Check network connection
- Verify Serena server running
- Check error logs
**"Context not restoring"**
- Verify memory structure
- Check `pm_context` exists
- Test with fresh memory
---
## 📚 References
- [ARCHITECTURE.md](./ARCHITECTURE.md) - System architecture
- [ROADMAP.md](./ROADMAP.md) - Development roadmap
- [pm-agent-implementation-status.md](../pm-agent-implementation-status.md) - Status tracking
- [Commands/pm.md](../../superclaude/Commands/pm.md) - PM Agent command
- [Agents/pm-agent.md](../../superclaude/Agents/pm-agent.md) - PM Agent persona
---
**Last Verified**: 2025-10-14
**Next Review**: 2025-10-21 (1 week)
**Version**: 4.1.5