mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
synced 2025-12-29 16:16:08 +00:00
feat: migrate research and index-repo to plugin, delete all slash commands
## Plugin Migration Added to pm-agent plugin: - /research: Deep web research with adaptive planning - /index-repo: Repository index (94% token reduction) - Total: 3 commands (pm, research, index-repo) ## Slash Commands Deleted Removed all 27 slash commands from ~/.claude/commands/sc/: - analyze, brainstorm, build, business-panel, cleanup - design, document, estimate, explain, git, help - implement, improve, index, load, pm, reflect - research, save, select-tool, spawn, spec-panel - task, test, troubleshoot, workflow ## Architecture Change Strategy: Minimal start with PM Agent orchestration - PM Agent = orchestrator (統括コマンダー) - Task tool (general-purpose, Explore) = execution - Plugin commands = specialized tasks when needed - Avoid reinventing the wheel (use official tools first) ## Files Changed - .claude-plugin/plugin.json: Added research + index-repo - .claude-plugin/commands/research.md: Copied from slash command - .claude-plugin/commands/index-repo.md: Copied from slash command - ~/.claude/commands/sc/: DELETED (all 27 commands) ## Benefits ✅ Minimal footprint (3 commands vs 27) ✅ Plugin-based distribution ✅ Version control ✅ Easy to extend when needed 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
kazuki
166
.claude-plugin/commands/index-repo.md
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166
.claude-plugin/commands/index-repo.md
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---
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name: index-repo
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description: "Create repository structure index for fast context loading (94% token reduction)"
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category: optimization
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complexity: simple
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mcp-servers: []
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personas: []
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---
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# Repository Indexing for Token Efficiency
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**Problem**: Loading全ファイルで毎回50,000トークン消費
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**Solution**: 最初だけインデックス作成、以降3,000トークンで済む (94%削減)
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## Auto-Execution
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**PM Mode Session Start**:
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```python
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index_path = Path("PROJECT_INDEX.md")
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if not index_path.exists() or is_stale(index_path, days=7):
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print("🔄 Creating repository index...")
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# Execute indexing automatically
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uv run python superclaude/indexing/parallel_repository_indexer.py
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```
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**Manual Trigger**:
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```bash
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/sc:index-repo # Full index
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/sc:index-repo --quick # Fast scan
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/sc:index-repo --update # Incremental
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```
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## What It Does
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### Parallel Analysis (5 concurrent tasks)
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1. **Code structure** (src/, lib/, superclaude/)
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2. **Documentation** (docs/, *.md)
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3. **Configuration** (.toml, .yaml, .json)
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4. **Tests** (tests/, **tests**)
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5. **Scripts** (scripts/, bin/, tools/)
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### Output Files
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- `PROJECT_INDEX.md` - Human-readable (3KB)
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- `PROJECT_INDEX.json` - Machine-readable (10KB)
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- `.superclaude/knowledge/agent_performance.json` - Learning data
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## Token Efficiency
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**Before** (毎セッション):
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```
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Read all .md files: 41,000 tokens
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Read all .py files: 15,000 tokens
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Glob searches: 2,000 tokens
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Total: 58,000 tokens
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```
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**After** (インデックス使用):
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```
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Read PROJECT_INDEX.md: 3,000 tokens
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Direct file access: 1,000 tokens
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Total: 4,000 tokens
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Savings: 93% (54,000 tokens)
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```
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## Usage in Sessions
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```python
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# Session start
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index = read_file("PROJECT_INDEX.md") # 3,000 tokens
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# Navigation
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"Where is the validator code?"
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→ Index says: superclaude/validators/
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→ Direct read, no glob needed
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# Understanding
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"What's the project structure?"
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→ Index has full overview
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→ No need to scan all files
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# Implementation
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"Add new validator"
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→ Index shows: tests/validators/ exists
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→ Index shows: 5 existing validators
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→ Follow established pattern
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```
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## Execution
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```bash
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$ /sc:index-repo
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================================================================================
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🚀 Parallel Repository Indexing
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================================================================================
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Repository: /Users/kazuki/github/SuperClaude_Framework
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Max workers: 5
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================================================================================
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📊 Executing parallel tasks...
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✅ code_structure: 847ms (system-architect)
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✅ documentation: 623ms (technical-writer)
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✅ configuration: 234ms (devops-architect)
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✅ tests: 512ms (quality-engineer)
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✅ scripts: 189ms (backend-architect)
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================================================================================
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✅ Indexing complete in 2.41s
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================================================================================
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💾 Index saved to: PROJECT_INDEX.md
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💾 JSON saved to: PROJECT_INDEX.json
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Files: 247 | Quality: 72/100
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```
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## Integration with Setup
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```python
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# setup/components/knowledge_base.py
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def install_knowledge_base():
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"""Install framework knowledge"""
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# ... existing installation ...
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# Auto-create repository index
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print("\n📊 Creating repository index...")
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run_indexing()
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print("✅ Index created - 93% token savings enabled")
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```
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## When to Re-Index
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**Auto-triggers**:
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- セットアップ時 (初回のみ)
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- INDEX.mdが7日以上古い
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- PM Modeセッション開始時にチェック
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**Manual re-index**:
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- 大規模リファクタリング後 (>20 files)
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- 新機能追加後 (new directories)
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- 週1回 (active development)
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**Skip**:
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- 小規模編集 (<5 files)
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- ドキュメントのみ変更
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- INDEX.mdが24時間以内
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## Performance
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**Speed**:
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- Large repo (500+ files): 3-5 min
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- Medium repo (100-500 files): 1-2 min
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- Small repo (<100 files): 10-30 sec
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**Self-Learning**:
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- Tracks agent performance
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- Optimizes future runs
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- Stored in `.superclaude/knowledge/`
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---
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**Implementation**: `superclaude/indexing/parallel_repository_indexer.py`
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**Related**: `/sc:pm` (uses index), `/sc:save`, `/sc:load`
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103
.claude-plugin/commands/research.md
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103
.claude-plugin/commands/research.md
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---
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name: research
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description: Deep web research with adaptive planning and intelligent search
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category: command
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complexity: advanced
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mcp-servers: [tavily, sequential, playwright, serena]
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personas: [deep-research-agent]
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---
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# /sc:research - Deep Research Command
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> **Context Framework Note**: This command activates comprehensive research capabilities with adaptive planning, multi-hop reasoning, and evidence-based synthesis.
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## Triggers
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- Research questions beyond knowledge cutoff
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- Complex research questions
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- Current events and real-time information
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- Academic or technical research requirements
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- Market analysis and competitive intelligence
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## Context Trigger Pattern
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```
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/sc:research "[query]" [--depth quick|standard|deep|exhaustive] [--strategy planning|intent|unified]
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```
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## Behavioral Flow
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### 1. Understand (5-10% effort)
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- Assess query complexity and ambiguity
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- Identify required information types
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- Determine resource requirements
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- Define success criteria
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### 2. Plan (10-15% effort)
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- Select planning strategy based on complexity
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- Identify parallelization opportunities
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- Generate research question decomposition
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- Create investigation milestones
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### 3. TodoWrite (5% effort)
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- Create adaptive task hierarchy
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- Scale tasks to query complexity (3-15 tasks)
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- Establish task dependencies
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- Set progress tracking
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### 4. Execute (50-60% effort)
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- **Parallel-first searches**: Always batch similar queries
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- **Smart extraction**: Route by content complexity
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- **Multi-hop exploration**: Follow entity and concept chains
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- **Evidence collection**: Track sources and confidence
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### 5. Track (Continuous)
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- Monitor TodoWrite progress
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- Update confidence scores
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- Log successful patterns
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- Identify information gaps
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### 6. Validate (10-15% effort)
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- Verify evidence chains
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- Check source credibility
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- Resolve contradictions
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- Ensure completeness
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## Key Patterns
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### Parallel Execution
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- Batch all independent searches
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- Run concurrent extractions
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- Only sequential for dependencies
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### Evidence Management
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- Track search results
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- Provide clear citations when available
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- Note uncertainties explicitly
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### Adaptive Depth
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- **Quick**: Basic search, 1 hop, summary output
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- **Standard**: Extended search, 2-3 hops, structured report
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- **Deep**: Comprehensive search, 3-4 hops, detailed analysis
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- **Exhaustive**: Maximum depth, 5 hops, complete investigation
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## MCP Integration
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- **Tavily**: Primary search and extraction engine
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- **Sequential**: Complex reasoning and synthesis
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- **Playwright**: JavaScript-heavy content extraction
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- **Serena**: Research session persistence
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## Output Standards
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- Save reports to `docs/research/[topic]_[timestamp].md`
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- Include executive summary
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- Provide confidence levels
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- List all sources with citations
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## Examples
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```
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/sc:research "latest developments in quantum computing 2024"
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/sc:research "competitive analysis of AI coding assistants" --depth deep
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/sc:research "best practices for distributed systems" --strategy unified
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```
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## Boundaries
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**Will**: Current information, intelligent search, evidence-based analysis
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**Won't**: Make claims without sources, skip validation, access restricted content
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@@ -8,6 +8,16 @@
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"name": "pm",
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"path": "commands/pm.md",
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"description": "Activate PM Agent with confidence-driven workflow"
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},
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{
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"name": "research",
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"path": "commands/research.md",
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"description": "Deep web research with adaptive planning and intelligent search"
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},
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{
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"name": "index-repo",
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"path": "commands/index-repo.md",
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"description": "Create repository structure index for fast context loading (94% token reduction)"
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}
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],
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"skills": [
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@@ -12,12 +12,10 @@ __author__ = "Kazuki Nakai"
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from .pm_agent.confidence import ConfidenceChecker
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from .pm_agent.self_check import SelfCheckProtocol
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from .pm_agent.reflexion import ReflexionPattern
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from .pm_agent.token_budget import TokenBudgetManager
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__all__ = [
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"ConfidenceChecker",
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"SelfCheckProtocol",
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"ReflexionPattern",
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"TokenBudgetManager",
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"__version__",
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]
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