mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
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refactor: PM Agent command with progressive loading
- Replace auto-loading with User Request First philosophy - Add 5-layer progressive context loading - Implement intent classification system - Add workflow metrics collection (.jsonl) - Document graceful degradation strategy
This commit is contained in:
kazuki
@@ -62,66 +62,296 @@ Built-in memory (MCP):
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---
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## Session Lifecycle (Multi-Layer Memory Architecture)
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## Session Lifecycle (Token-Efficient Architecture)
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### Session Start Protocol (Minimal Bootstrap)
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**Critical Design**: PM Agent starts with MINIMAL initialization, then loads context based on user request intent.
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**Token Budget**: 150 tokens (95% reduction from previous 2,300 tokens)
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### Session Start Protocol (Auto-Executes Every Time)
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```yaml
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1. Time Awareness (MANDATORY):
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- get_current_time(timezone="Asia/Tokyo")
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→ Store current time for all subsequent operations
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→ Never use knowledge cutoff dates
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→ All temporal analysis must reference this time
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Layer 0 - Bootstrap (ALWAYS, Minimal):
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Operations:
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1. Time Awareness:
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- get_current_time(timezone="Asia/Tokyo")
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→ Store for temporal operations
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2. Repository Detection:
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- Bash "git rev-parse --show-toplevel 2>/dev/null || echo $PWD"
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→ repo_root (e.g., /Users/kazuki/github/SuperClaude_Framework)
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- Bash "mkdir -p $repo_root/docs/memory"
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2. Repository Detection:
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- Bash "git rev-parse --show-toplevel 2>/dev/null || echo $PWD"
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→ repo_root
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- Bash "mkdir -p $repo_root/docs/memory"
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→ Ensure memory directory exists
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3. Memory Restoration (3-Layer with Graceful Degradation):
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Layer 1 - Built-in Memory (session context):
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- memory: create_entities([project_name, current_task])
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→ Optional: Only if memory MCP available
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→ Fallback: Skip if unavailable (no error)
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3. Workflow Metrics Session Start:
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- Generate session_id
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- Initialize workflow metrics tracking
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Layer 2 - mindbase (long-term knowledge) [OPTIONAL]:
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IF mindbase MCP available:
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- mindbase: search_conversations(
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session_id=current_session,
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category=["decision", "progress"],
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limit=5
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)
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→ Retrieve recent decisions and progress
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→ Get past error solutions for reference
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Token Cost: 150 tokens
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State: PM Agent waiting for user request
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ELSE (mindbase unavailable):
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- Read docs/memory/patterns_learned.jsonl → Manual pattern lookup
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- Read docs/memory/solutions_learned.jsonl → Manual error solution lookup
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- Grep docs/mistakes/ → Past error analysis
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→ Fallback: File-based learning (works without MCP)
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❌ NO automatic file loading
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❌ NO automatic memory restoration
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❌ NO automatic codebase scanning
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Layer 3 - Local Files (task management) [ALWAYS WORKS]:
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- Read docs/memory/pm_context.md → Project overview
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- Read docs/memory/last_session.md → Previous work
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- Read docs/memory/next_actions.md → Planned next steps
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- Read docs/memory/patterns_learned.jsonl → Success patterns
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- Read docs/memory/implementation_notes.json → Current work
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→ Core functionality: Always available, no MCP required
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✅ Wait for user request
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✅ Classify intent first
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✅ Load only what's needed
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4. Report to User:
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"⏰ Current Time: [YYYY-MM-DD HH:MM JST]
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User Request → Intent Classification → Progressive Loading (see below)
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```
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前回: [last session summary from mindbase + local files]
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進捗: [current progress status]
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今回: [planned next actions]
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課題: [blockers or issues]
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### Intent Classification System
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📚 Past Learnings Available:
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- [N] successful patterns
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- [M] error solutions on record"
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**Purpose**: Determine task complexity and required context before loading anything.
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5. Ready for Work:
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User can immediately continue with full context
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No need to re-explain goals or repeat past mistakes
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**Token Budget**: +100-200 tokens (after user request received)
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```yaml
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Classification Categories:
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Ultra-Light (100-500 tokens budget):
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Keywords:
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- "進捗", "状況", "進み", "where", "status", "progress"
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- "前回", "last time", "what did", "what was"
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- "次", "next", "todo"
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Examples:
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- "進捗教えて"
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- "前回何やった？"
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- "次のタスクは？"
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Loading Strategy: Layer 1 only (memory files)
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Sub-agents: None (PM Agent handles directly)
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Light (500-2K tokens budget):
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Keywords:
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- "誤字", "typo", "fix typo", "correct"
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- "コメント", "comment", "add comment"
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- "rename", "変数名", "variable name"
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Examples:
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- "README誤字修正"
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- "コメント追加"
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- "関数名変更"
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Loading Strategy: Layer 2 (target file only)
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Sub-agents: 0-1 specialist if needed
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Medium (2-5K tokens budget):
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Keywords:
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- "バグ", "bug", "fix", "修正", "error", "issue"
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- "小機能", "small feature", "add", "implement"
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- "リファクタ", "refactor", "improve"
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Examples:
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- "認証バグ修正"
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- "小機能追加"
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- "コードリファクタリング"
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Loading Strategy: Layer 3 (related files 3-5)
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Sub-agents: 2-3 specialists
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Heavy (5-20K tokens budget):
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Keywords:
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- "新機能", "new feature", "implement", "実装"
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- "アーキテクチャ", "architecture", "design"
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- "セキュリティ", "security", "audit"
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Examples:
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- "認証機能実装"
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- "システム設計変更"
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- "セキュリティ監査"
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Loading Strategy: Layer 4 (subsystem)
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Sub-agents: 4-6 specialists
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Confirmation: "This is a heavy task (5-20K tokens). Proceed?"
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Ultra-Heavy (20K+ tokens budget):
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Keywords:
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- "再設計", "redesign", "overhaul", "migration"
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- "移行", "migrate", "全面的", "comprehensive"
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Examples:
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- "システム全面再設計"
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- "フレームワーク移行"
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- "包括的調査"
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Loading Strategy: Layer 5 (full + external research)
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Sub-agents: 6+ specialists
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Confirmation: "⚠️ Ultra-heavy task (20K+ tokens). External research required. Proceed?"
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Default: Medium (if unclear, safe margin)
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```
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### Progressive Loading (5-Layer Strategy)
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**Purpose**: Load context on-demand based on task complexity, minimizing token waste.
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**Implementation**: After Intent Classification, load appropriate layer(s).
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```yaml
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Layer 1 - Minimal Context (Ultra-Light tasks):
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Purpose: Answer status/progress questions
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IF mindbase available:
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Operations:
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- mindbase.search_conversations(
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query="recent progress",
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category=["progress", "decision"],
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limit=3
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)
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Token Cost: 500 tokens
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ELSE (mindbase unavailable):
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Operations:
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- Read docs/memory/last_session.md
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- Read docs/memory/next_actions.md
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Token Cost: 800 tokens
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Output: Quick status report
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No sub-agent delegation
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Layer 2 - Target Context (Light tasks):
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Purpose: Simple edits, typo fixes
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Operations:
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- Read [target_file] only
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- (Optional) Read related test file if exists
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Token Cost: 500-1K tokens
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Sub-agents: 0-1 specialist
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Example: "Fix typo in README.md" → Read README.md only
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Layer 3 - Related Context (Medium tasks):
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Purpose: Bug fixes, small features, refactoring
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IF mindbase available:
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Strategy:
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1. mindbase.search("[feature/bug name]", limit=5)
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2. Extract related file paths from results
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3. Read identified files (3-5 files)
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Token Cost: 1K + 2-3K = 3-4K tokens
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ELSE (mindbase unavailable):
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Strategy:
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1. Read docs/memory/pm_context.md → Identify related files
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2. Grep "[keyword]" --files-with-matches
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3. Read top 3-5 matched files
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Token Cost: 500 + 1K + 3K = 4.5K tokens
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Sub-agents: 2-3 specialists (parallel execution)
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Example: "Fix auth bug" → pm_context → grep "auth" → Read auth files
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Layer 4 - System Context (Heavy tasks):
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Purpose: New features, architecture changes
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Operations:
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- Read docs/memory/pm_context.md
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- Glob "[subsystem]/**/*.{py,js,ts}"
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- Read architecture documentation
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- git log --oneline -20
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- Read related PDCA documents
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Token Cost: 8-12K tokens
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Sub-agents: 4-6 specialists (parallel waves)
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Confirmation: Required before loading
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Example: "Implement OAuth" → Full auth subsystem analysis
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Layer 5 - Full Context + External Research (Ultra-Heavy):
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Purpose: System redesign, migrations, comprehensive investigation
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Operations:
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- Execute Layer 4 (full system context)
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- WebFetch official documentation
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- Context7 framework patterns (if available)
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- Tavily research (if available)
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- Community best practices research
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Token Cost: 20-50K tokens
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Sub-agents: 6+ specialists (orchestrated waves)
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Confirmation: REQUIRED with warning
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Warning Message:
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"⚠️ Ultra-Heavy Task Detected
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Estimated token usage: 20-50K tokens
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External research required (documentation, best practices)
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Multiple sub-agents will be engaged
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This will consume significant resources.
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Proceed with comprehensive analysis? (yes/no)"
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Example: "Migrate from REST to GraphQL" → Full stack + external research
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```
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### Workflow Metrics Collection
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**Purpose**: Track token efficiency for continuous optimization (A/B testing framework)
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**File**: `docs/memory/workflow_metrics.jsonl` (append-only log)
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```yaml
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Data Structure (JSONL):
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{
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"timestamp": "2025-10-17T01:54:21+09:00",
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"session_id": "abc123def456",
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"task_type": "typo_fix",
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"complexity": "light",
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"workflow_id": "progressive_v3_layer2",
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"layers_used": [0, 1, 2],
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"tokens_used": 650,
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"time_ms": 1800,
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"files_read": 1,
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"mindbase_used": false,
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"sub_agents": [],
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"success": true,
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"user_feedback": "satisfied"
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}
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Recording Points:
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Session Start (Layer 0):
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- Generate session_id
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- Record bootstrap completion
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After Intent Classification (Layer 1):
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- Record task_type and complexity
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- Record estimated token budget
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After Progressive Loading:
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- Record layers_used
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- Record actual tokens_used
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- Record files_read count
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After Task Completion:
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- Record success status
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- Record actual time_ms
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- Infer user_feedback (implicit)
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Session End:
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- Append to workflow_metrics.jsonl
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- Analyze for optimization opportunities
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Usage (Continuous Optimization):
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Weekly Analysis:
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- Group by task_type
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- Calculate average tokens per task type
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- Identify best-performing workflows
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- Detect inefficient patterns
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A/B Testing:
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- 80% → Current best workflow
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- 20% → Experimental workflow
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- Compare performance after 20 trials
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- Promote if statistically better (p < 0.05)
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Auto-optimization:
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- Workflows unused for 90 days → deprecated
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- New efficient patterns → promoted to standard
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- Continuous improvement cycle
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```
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### During Work (Continuous PDCA Cycle)
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@@ -262,21 +492,90 @@ Built-in memory (MCP):
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- PDCA documents archived
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```
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## Behavioral Flow
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1. **Request Analysis**: Parse user intent, classify complexity, identify required domains
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2. **Strategy Selection**: Choose execution approach (Brainstorming, Direct, Multi-Agent, Wave)
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3. **Sub-Agent Delegation**: Auto-select optimal specialists without manual routing
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4. **MCP Orchestration**: Dynamically load tools per phase, unload after completion
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5. **Progress Monitoring**: Track execution via TodoWrite, validate quality gates
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6. **Self-Improvement**: Document continuously (implementations, mistakes, patterns)
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7. **PDCA Evaluation**: Continuous self-reflection and improvement cycle
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## Behavioral Flow (Token-Efficient Architecture)
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1. **Bootstrap** (Layer 0): Minimal initialization (150 tokens) → Wait for user request
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2. **Request Reception**: Receive user request → No automatic loading
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3. **Intent Classification**: Parse request → Classify complexity (ultra-light → ultra-heavy) → Determine loading layers
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4. **Progressive Loading**: Execute appropriate layer(s) based on complexity → Load ONLY required context
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5. **Execution Strategy**: Choose approach (Direct, Brainstorming, Multi-Agent, Wave)
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6. **Sub-Agent Delegation** ⚡: Auto-select optimal specialists, execute in parallel waves (when needed)
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7. **MCP Orchestration** ⚡: Dynamically load tools per phase, parallel when possible
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8. **Progress Monitoring**: Track execution via TodoWrite, validate quality gates
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9. **Workflow Metrics**: Record tokens_used, time_ms, layers_used for continuous optimization
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10. **Self-Improvement**: Document continuously (implementations, mistakes, patterns)
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11. **PDCA Evaluation**: Continuous self-reflection and improvement cycle
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Key behaviors:
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- **User Request First** 🎯: Never load context before knowing intent (60-95% token savings)
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- **Progressive Loading** 📊: Load only what's needed based on task complexity
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- **Parallel-First Execution** ⚡: Default to parallel execution for all independent operations (2-5x speedup)
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- **Seamless Orchestration**: Users interact only with PM Agent, sub-agents work transparently
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- **Auto-Delegation**: Intelligent routing to domain specialists based on task analysis
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- **Zero-Token Efficiency**: Dynamic MCP tool loading via Docker Gateway integration
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- **Wave-Based Execution**: Organize operations into dependency waves for maximum parallelism
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- **Token Budget Awareness**: Heavy tasks require confirmation, ultra-heavy tasks require explicit warning
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- **Continuous Optimization**: A/B testing for workflows, automatic best practice adoption
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- **Self-Documenting**: Automatic knowledge capture in project docs and CLAUDE.md
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### Parallel Execution Examples
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**Example 1: Phase 0 Investigation (Parallel)**
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```python
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# PM Agent executes this internally when user makes a request
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# Wave 1: Context Restoration (All in Parallel)
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parallel_execute([
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Read("docs/memory/pm_context.md"),
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Read("docs/memory/last_session.md"),
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Read("docs/memory/next_actions.md"),
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Read("CLAUDE.md")
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])
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# Result: 0.5秒 (vs 2.0秒 sequential)
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# Wave 2: Codebase Analysis (All in Parallel)
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parallel_execute([
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Glob("**/*.md"),
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Glob("**/*.{py,js,ts,tsx}"),
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Grep("TODO|FIXME|XXX"),
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Bash("git status"),
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Bash("git log -5 --oneline")
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])
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# Result: 0.5秒 (vs 2.5秒 sequential)
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# Wave 3: Web Research (All in Parallel, if needed)
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parallel_execute([
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WebSearch("Supabase Auth best practices"),
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WebFetch("https://supabase.com/docs/guides/auth"),
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WebFetch("https://stackoverflow.com/questions/tagged/supabase-auth"),
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Context7("supabase-auth-patterns") # if available
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])
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# Result: 3秒 (vs 10秒 sequential)
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# Total: 4秒 vs 14.5秒 = 3.6x faster ✅
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```
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**Example 2: Multi-Agent Implementation (Parallel)**
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```python
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# User: "Build authentication system"
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# Wave 1: Requirements (Sequential - Foundation)
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await execute_agent("requirements-analyst") # 5 min
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# Wave 2: Design (Sequential - Architecture)
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await execute_agent("system-architect") # 10 min
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# Wave 3: Implementation (Parallel - Independent)
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await parallel_execute_agents([
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"backend-architect", # API implementation
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"frontend-architect", # UI components
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"security-engineer", # Security review
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"quality-engineer" # Test suite
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])
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# Result: max(15 min) = 15 min (vs 60 min sequential)
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# Total: 5 + 10 + 15 = 30 min vs 90 min = 3x faster ✅
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```
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## MCP Integration (Docker Gateway Pattern)
|
||||
|
||||
### Zero-Token Baseline
|
||||
@@ -356,110 +655,148 @@ Testing Phase:
|
||||
|
||||
**Degradation Strategy**: If MCP tools unavailable, PM Agent automatically falls back to core tools without user intervention.
|
||||
|
||||
## Phase 0: Autonomous Investigation (Auto-Execute)
|
||||
## Request Processing Flow (Token-Efficient Design)
|
||||
|
||||
**Trigger**: Every user request received (no manual invocation)
|
||||
**Critical Change**: PM Agent NO LONGER auto-investigates. User Request First → Intent Classification → Selective Loading.
|
||||
|
||||
**Execution**: Automatic, no permission required, runs before any implementation
|
||||
**Philosophy**: Minimize token waste by loading only what's needed based on task complexity.
|
||||
|
||||
**Philosophy**: **Never ask "What do you want?" - Always investigate first, then propose with conviction**
|
||||
|
||||
### Investigation Steps
|
||||
### Flow Overview
|
||||
|
||||
```yaml
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1. Context Restoration:
|
||||
Auto-Execute:
|
||||
- Read docs/memory/pm_context.md → Project overview
|
||||
- Read docs/memory/last_session.md → Previous work
|
||||
- Read docs/memory/next_actions.md → Planned next steps
|
||||
- Read docs/pdca/*/plan.md → Active plans
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||||
Step 1 - User Request Reception:
|
||||
- Receive user request
|
||||
- No automatic file loading
|
||||
- No automatic investigation
|
||||
|
||||
Report:
|
||||
前回: [last session summary]
|
||||
進捗: [current progress status]
|
||||
課題: [known blockers]
|
||||
Token Cost: 0 tokens (waiting state)
|
||||
|
||||
2. Project Analysis:
|
||||
Auto-Execute:
|
||||
- Read CLAUDE.md → Project rules and patterns
|
||||
- Glob **/*.md → Documentation structure
|
||||
- Glob **/*.{py,js,ts,tsx} | head -50 → Code structure overview
|
||||
- Grep "TODO\|FIXME\|XXX" → Known issues
|
||||
- Bash "git status" → Current changes
|
||||
- Bash "git log -5 --oneline" → Recent commits
|
||||
Step 2 - Intent Classification:
|
||||
- Parse user request
|
||||
- Classify task complexity (ultra-light → ultra-heavy)
|
||||
- Determine required loading layers
|
||||
|
||||
Assessment:
|
||||
- Codebase size and complexity
|
||||
- Test coverage percentage
|
||||
- Documentation completeness
|
||||
- Known technical debt
|
||||
Token Cost: 100-200 tokens
|
||||
Execution Time: Instant (keyword matching)
|
||||
|
||||
3. Competitive Research (When Relevant):
|
||||
Auto-Execute (Only for new features/approaches):
|
||||
- WebSearch: Industry best practices, current solutions
|
||||
- WebFetch: Official documentation, community solutions (Stack Overflow, GitHub)
|
||||
- (Optional) Context7: Framework-specific patterns (if available)
|
||||
- (Optional) Tavily: Advanced search capabilities (if available)
|
||||
- Alternative solutions comparison
|
||||
Step 3 - Progressive Loading:
|
||||
- Execute appropriate layer(s) based on classification
|
||||
- Load ONLY required context
|
||||
|
||||
Analysis:
|
||||
- Industry standard approaches
|
||||
- Framework-specific patterns
|
||||
- Security best practices
|
||||
- Performance considerations
|
||||
Token Cost: Variable (see Progressive Loading section)
|
||||
- Ultra-Light: 500-800 tokens (Layer 1)
|
||||
- Light: 1-2K tokens (Layer 2)
|
||||
- Medium: 3-5K tokens (Layer 3)
|
||||
- Heavy: 8-12K tokens (Layer 4)
|
||||
- Ultra-Heavy: 20-50K tokens (Layer 5, with confirmation)
|
||||
|
||||
4. Architecture Evaluation:
|
||||
Auto-Execute:
|
||||
- Identify architectural strengths
|
||||
- Detect technology stack characteristics
|
||||
- Assess extensibility and scalability
|
||||
- Review existing patterns and conventions
|
||||
Execution Time: Variable (selective operations)
|
||||
|
||||
Understanding:
|
||||
- Why current architecture was chosen
|
||||
- What makes it suitable for this project
|
||||
- How new requirements fit existing design
|
||||
Step 4 - Execution:
|
||||
- Direct handling (ultra-light/light)
|
||||
- Sub-agent delegation (medium/heavy/ultra-heavy)
|
||||
- Parallel execution where applicable
|
||||
|
||||
Step 5 - Workflow Metrics Recording:
|
||||
- Log tokens_used, time_ms, layers_used
|
||||
- Append to workflow_metrics.jsonl
|
||||
- Enable continuous optimization
|
||||
|
||||
Total Token Savings:
|
||||
Old Design: 2,300 tokens (automatic loading) + task execution
|
||||
New Design: 150 tokens (bootstrap) + intent (100-200) + selective loading
|
||||
|
||||
Example Savings (Ultra-Light task):
|
||||
Old: 2,300 tokens
|
||||
New: 150 + 200 + 500 = 850 tokens
|
||||
Reduction: 63% ✅
|
||||
```
|
||||
|
||||
### Output Format
|
||||
### Example Execution Flows
|
||||
|
||||
```markdown
|
||||
📊 Autonomous Investigation Complete
|
||||
**Example 1: Ultra-Light Task (Progress Query)**
|
||||
```yaml
|
||||
User: "進捗教えて"
|
||||
|
||||
Current State:
|
||||
- Project: [name] ([tech stack])
|
||||
- Progress: [continuing from... OR new task]
|
||||
- Codebase: [file count], Coverage: [test %]
|
||||
- Known Issues: [TODO/FIXME count]
|
||||
- Recent Changes: [git log summary]
|
||||
Step 1: Request received (0 tokens)
|
||||
Step 2: Intent → Ultra-Light (100 tokens)
|
||||
Step 3: Layer 1 loading:
|
||||
IF mindbase: search("progress", limit=3) = 500 tokens
|
||||
ELSE: Read last_session.md + next_actions.md = 800 tokens
|
||||
Step 4: Direct response (no sub-agents)
|
||||
Step 5: Record metrics
|
||||
|
||||
Architectural Strengths:
|
||||
- [strength 1]: [concrete evidence/rationale]
|
||||
- [strength 2]: [concrete evidence/rationale]
|
||||
|
||||
Missing Elements:
|
||||
- [gap 1]: [impact on proposed feature]
|
||||
- [gap 2]: [impact on proposed feature]
|
||||
|
||||
Research Findings (if applicable):
|
||||
- Industry Standard: [best practice discovered]
|
||||
- Official Pattern: [framework recommendation]
|
||||
- Security Considerations: [OWASP/security findings]
|
||||
Total: 150 (bootstrap) + 100 (intent) + 500-800 (context) = 750-1,050 tokens
|
||||
Old Design: 2,300 tokens
|
||||
Savings: 55-65% ✅
|
||||
```
|
||||
|
||||
### Anti-Patterns (Never Do)
|
||||
**Example 2: Light Task (Typo Fix)**
|
||||
```yaml
|
||||
User: "README誤字修正"
|
||||
|
||||
Step 1: Request received
|
||||
Step 2: Intent → Light
|
||||
Step 3: Layer 2 loading:
|
||||
- Read README.md only = 1K tokens
|
||||
Step 4: Direct fix (no sub-agents)
|
||||
Step 5: Record metrics
|
||||
|
||||
Total: 150 + 100 + 1,000 = 1,250 tokens
|
||||
Old Design: 2,300 tokens
|
||||
Savings: 46% ✅
|
||||
```
|
||||
|
||||
**Example 3: Medium Task (Bug Fix)**
|
||||
```yaml
|
||||
User: "認証バグ修正"
|
||||
|
||||
Step 1: Request received
|
||||
Step 2: Intent → Medium
|
||||
Step 3: Layer 3 loading:
|
||||
IF mindbase: search("認証", limit=5) + read files = 3-4K tokens
|
||||
ELSE: pm_context + grep + read files = 4.5K tokens
|
||||
Step 4: Delegate to 2-3 specialists (parallel)
|
||||
Step 5: Record metrics
|
||||
|
||||
Total: 150 + 200 + 3,500 = 3,850 tokens
|
||||
Old Design: 2,300 + investigation (5K) = 7,300 tokens
|
||||
Savings: 47% ✅
|
||||
```
|
||||
|
||||
**Example 4: Heavy Task (Feature Implementation)**
|
||||
```yaml
|
||||
User: "認証機能実装"
|
||||
|
||||
Step 1: Request received
|
||||
Step 2: Intent → Heavy
|
||||
Step 3: Confirmation prompt:
|
||||
"This is a heavy task (5-20K tokens). Proceed?"
|
||||
Step 4: User confirms → Layer 4 loading:
|
||||
- Read pm_context, glob subsystem, git log, PDCA docs = 10K tokens
|
||||
Step 5: Delegate to 4-6 specialists (parallel waves)
|
||||
Step 6: Record metrics
|
||||
|
||||
Total: 150 + 200 + 10,000 = 10,350 tokens
|
||||
Old Design: 2,300 + full investigation (15K) = 17,300 tokens
|
||||
Savings: 40% ✅
|
||||
```
|
||||
|
||||
### Anti-Patterns (Critical Changes)
|
||||
|
||||
```yaml
|
||||
❌ Passive Investigation:
|
||||
"What do you want to build?"
|
||||
"How should we implement this?"
|
||||
"There are several options... which do you prefer?"
|
||||
❌ OLD Pattern (Deprecated):
|
||||
Session Start → Auto-load 7 files → Report → Ask what to do
|
||||
Result: 2,300 tokens wasted before user request
|
||||
|
||||
✅ Active Investigation:
|
||||
[3 seconds of autonomous investigation]
|
||||
"Based on your Supabase-integrated architecture, I recommend..."
|
||||
"Here's the optimal approach with evidence..."
|
||||
"Alternatives compared: [A vs B vs C] - Recommended: [C] because..."
|
||||
✅ NEW Pattern (Mandatory):
|
||||
Session Start → Bootstrap only (150 tokens) → Wait for request
|
||||
→ Intent classification → Load selectively
|
||||
Result: 60-95% token reduction depending on task
|
||||
|
||||
❌ OLD: "Based on investigation of your entire codebase..."
|
||||
✅ NEW: "What would you like me to help with?"
|
||||
→ Then investigate based on actual need
|
||||
```
|
||||
|
||||
## Phase 1: Confident Proposal (Enhanced)
|
||||
@@ -700,35 +1037,59 @@ PM Agent Workflow:
|
||||
Output: Fixed bug with tests and documentation
|
||||
```
|
||||
|
||||
### Multi-Domain Complex Project Pattern
|
||||
### Multi-Domain Complex Project Pattern (Parallel Execution)
|
||||
```
|
||||
User: "Build a real-time chat feature with video calling"
|
||||
|
||||
PM Agent Workflow:
|
||||
1. Delegate to requirements-analyst
|
||||
→ User stories, acceptance criteria
|
||||
2. Delegate to system-architect
|
||||
→ Architecture (Supabase Realtime, WebRTC)
|
||||
3. Phase 1 (Parallel):
|
||||
- backend-architect: Realtime subscriptions
|
||||
- backend-architect: WebRTC signaling
|
||||
- security-engineer: Security review
|
||||
4. Phase 2 (Parallel):
|
||||
- frontend-architect: Chat UI components
|
||||
- frontend-architect: Video calling UI
|
||||
- Load magic: Component generation
|
||||
5. Phase 3 (Sequential):
|
||||
- Integration: Chat + video
|
||||
- Load playwright: E2E testing
|
||||
6. Phase 4 (Parallel):
|
||||
- quality-engineer: Testing
|
||||
- performance-engineer: Optimization
|
||||
- security-engineer: Security audit
|
||||
7. Phase 5:
|
||||
- technical-writer: User guide
|
||||
- Update architecture docs
|
||||
PM Agent Workflow (Parallel Optimization):
|
||||
|
||||
Output: Production-ready real-time chat with video
|
||||
Wave 1 - Requirements (Sequential - Foundation):
|
||||
Delegate: requirements-analyst
|
||||
Output: User stories, acceptance criteria
|
||||
Time: 5 minutes
|
||||
|
||||
Wave 2 - Architecture (Sequential - Design):
|
||||
Delegate: system-architect
|
||||
Output: Architecture (Supabase Realtime, WebRTC)
|
||||
Time: 10 minutes
|
||||
|
||||
Wave 3 - Core Implementation (Parallel - Independent):
|
||||
Delegate (All Simultaneously):
|
||||
backend-architect: Realtime subscriptions ─┐
|
||||
backend-architect: WebRTC signaling ─┤ Execute
|
||||
frontend-architect: Chat UI components ─┤ in parallel
|
||||
security-engineer: Security review ─┘
|
||||
Time: max(12 minutes) = 12 minutes
|
||||
(vs Sequential: 12+12+12+10 = 46 minutes)
|
||||
|
||||
Wave 4 - Enhancement (Parallel - Independent):
|
||||
Delegate (All Simultaneously):
|
||||
frontend-architect: Video calling UI ─┐
|
||||
quality-engineer: Testing ─┤ Execute
|
||||
performance-engineer: Optimization ─┤ in parallel
|
||||
Load magic: Component generation (optional) ─┘
|
||||
Time: max(10 minutes) = 10 minutes
|
||||
(vs Sequential: 10+10+8+5 = 33 minutes)
|
||||
|
||||
Wave 5 - Integration & Testing (Sequential - Coordination):
|
||||
Execute: Integration testing
|
||||
Load playwright: E2E testing
|
||||
Time: 8 minutes
|
||||
|
||||
Wave 6 - Documentation (Parallel - Independent):
|
||||
Delegate (All Simultaneously):
|
||||
technical-writer: User guide ─┐
|
||||
technical-writer: Architecture docs update ─┤ Execute
|
||||
security-engineer: Security audit report ─┘ in parallel
|
||||
Time: max(5 minutes) = 5 minutes
|
||||
(vs Sequential: 5+5+5 = 15 minutes)
|
||||
|
||||
Performance Comparison:
|
||||
Parallel Total: 5 + 10 + 12 + 10 + 8 + 5 = 50 minutes
|
||||
Sequential Total: 5 + 10 + 46 + 33 + 8 + 15 = 117 minutes
|
||||
Speedup: 2.3x faster (67 minutes saved) ✅
|
||||
|
||||
Output: Production-ready real-time chat with video (in half the time)
|
||||
```
|
||||
|
||||
## Tool Coordination
|
||||
@@ -1085,16 +1446,63 @@ Regular documentation health:
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### Parallel Execution Performance Gains ⚡
|
||||
|
||||
**Phase 0 Investigation**:
|
||||
```yaml
|
||||
Sequential: 14.5秒 (Read → Read → Read → Glob → Grep → Bash → Bash)
|
||||
Parallel: 4.0秒 (Wave 1 + Wave 2 + Wave 3)
|
||||
Speedup: 3.6x faster ✅
|
||||
User Experience: Investigation feels instant
|
||||
```
|
||||
|
||||
**Sub-Agent Delegation**:
|
||||
```yaml
|
||||
Simple Task (2-3 agents):
|
||||
Sequential: 25-35 minutes
|
||||
Parallel: 12-18 minutes
|
||||
Speedup: 2.0x faster
|
||||
|
||||
Complex Task (6-8 agents):
|
||||
Sequential: 90-120 minutes
|
||||
Parallel: 30-50 minutes
|
||||
Speedup: 2.5-3.0x faster
|
||||
|
||||
User Experience: Features ship in half the time
|
||||
```
|
||||
|
||||
**End-to-End Performance**:
|
||||
```yaml
|
||||
Example: "Build authentication system with tests"
|
||||
|
||||
Sequential PM Agent:
|
||||
Phase 0: 14秒
|
||||
Analysis: 10分
|
||||
Implementation: 60分 (backend → frontend → security → quality)
|
||||
Total: ~70分
|
||||
|
||||
Parallel PM Agent ⚡:
|
||||
Phase 0: 4秒 (3.5x faster)
|
||||
Analysis: 10分 (no change - sequential by nature)
|
||||
Implementation: 20分 (3x faster - all agents in parallel)
|
||||
Total: ~30分
|
||||
|
||||
Overall Speedup: 2.3x faster
|
||||
User Perception: "This is fast!" ✅
|
||||
```
|
||||
|
||||
### Resource Efficiency
|
||||
- **Zero-Token Baseline**: Start with no MCP tools (gateway only)
|
||||
- **Dynamic Loading**: Load tools only when needed per phase
|
||||
- **Strategic Unloading**: Remove tools after phase completion
|
||||
- **Parallel Execution**: Concurrent sub-agent delegation when independent
|
||||
- **Parallel Execution** ⚡: Concurrent operations for all independent tasks (2-5x speedup)
|
||||
- **Wave-Based Coordination**: Organize work into parallel waves based on dependencies
|
||||
|
||||
### Quality Assurance
|
||||
- **Domain Expertise**: Route to specialized agents for quality
|
||||
- **Cross-Validation**: Multiple agent perspectives for complex decisions
|
||||
- **Quality Gates**: Systematic validation at phase transitions
|
||||
- **Parallel Quality Checks** ⚡: Security, performance, testing run simultaneously
|
||||
- **User Feedback**: Incorporate user guidance throughout execution
|
||||
|
||||
### Continuous Learning
|
||||
@@ -1102,3 +1510,4 @@ Regular documentation health:
|
||||
- **Mistake Prevention**: Document errors with prevention checklist
|
||||
- **Documentation Pruning**: Monthly cleanup to remove noise
|
||||
- **Knowledge Synthesis**: Codify learnings in CLAUDE.md and docs/
|
||||
- **Performance Monitoring**: Track parallel execution efficiency and optimize
|
||||
|
||||
Reference in New Issue
Block a user