SuperClaude/docs/memory/pm_context.md

PM Agent Context

Project: SuperClaude_Framework Type: AI Agent Framework Tech Stack: Claude Code, MCP Servers, Markdown-based configuration Current Focus: Token-efficient architecture with progressive context loading

Project Overview

SuperClaude is a comprehensive framework for Claude Code that provides:

• Persona-based specialized agents (frontend, backend, security, etc.)
• MCP server integrations (Context7, Magic, Morphllm, Sequential, etc.)
• Slash command system for workflow automation
• Self-improvement workflow with PDCA cycle
• NEW: Token-optimized PM Agent with progressive loading

Architecture

• superclaude/agents/ - Agent persona definitions
• superclaude/commands/ - Slash command definitions (pm.md: token-efficient redesign)
• docs/ - Documentation and patterns
• docs/memory/ - PM Agent session state (local files)
• docs/pdca/ - PDCA cycle documentation per feature
• docs/research/ - Research reports (llm-agent-token-efficiency-2025.md)

Token Efficiency Architecture (2025-10-17 Redesign)

Layer 0: Bootstrap (Always Active)

• Token Cost: 150 tokens (95% reduction from old 2,300 tokens)
• Operations: Time awareness + repo detection + session initialization
• Philosophy: User Request First - NO auto-loading before understanding intent

Intent Classification System

Ultra-Light (100-500 tokens):   "進捗", "progress", "status" → Layer 1 only
Light (500-2K tokens):          "typo", "rename", "comment" → Layer 2 (target file)
Medium (2-5K tokens):           "bug", "fix", "refactor" → Layer 3 (related files)
Heavy (5-20K tokens):           "feature", "architecture" → Layer 4 (subsystem)
Ultra-Heavy (20K+ tokens):      "redesign", "migration" → Layer 5 (full + research)

Progressive Loading (5-Layer Strategy)

• Layer 1: Minimal context (mindbase: 500 tokens | fallback: 800 tokens)
• Layer 2: Target context (500-1K tokens)
• Layer 3: Related context (mindbase: 3-4K | fallback: 4.5K)
• Layer 4: System context (8-12K tokens, user confirmation)
• Layer 5: External research (20-50K tokens, WARNING required)

Workflow Metrics Collection

• File: docs/memory/workflow_metrics.jsonl
• Purpose: Continuous A/B testing for workflow optimization
• Data: task_type, complexity, workflow_id, tokens_used, time_ms, success
• Strategy: ε-greedy (80% best workflow, 20% experimental)

mindbase Integration Incentive

• Layer 1: 500 tokens (mindbase) vs 800 tokens (fallback) = 38% savings
• Layer 3: 3-4K tokens (mindbase) vs 4.5K tokens (fallback) = 20% savings
• Total Potential: Up to 90% token reduction with semantic search (industry benchmark)

Active Patterns

• Repository-Scoped Memory: Local file-based memory in docs/memory/
• PDCA Cycle: Plan → Do → Check → Act documentation workflow
• Self-Evaluation Checklists: Replace Serena MCP think_about_* functions
• User Request First: Bootstrap → Wait → Intent → Progressive Load → Execute
• Continuous Optimization: A/B testing via workflow_metrics.jsonl

Recent Changes (2025-10-17)

PM Agent Token Efficiency Redesign

• Removed: Auto-loading 7 files on startup (2,300 tokens wasted)
• Added: Layer 0 Bootstrap (150 tokens) + Intent Classification
• Added: Progressive Loading (5-layer) + Workflow Metrics
• Result:
  • Ultra-Light tasks: 2,300 → 650 tokens (72% reduction)
  • Light tasks: 3,500 → 1,200 tokens (66% reduction)
  • Medium tasks: 7,000 → 4,500 tokens (36% reduction)

Research Integration

• Report: docs/research/llm-agent-token-efficiency-2025.md
• Benchmarks: Trajectory Reduction (99%), AgentDropout (21.6%), Vector DB (90%)
• Source: Anthropic, Microsoft AutoGen v0.4, CrewAI + Mem0, LangChain

Known Issues

None currently.

Last Updated

2025-10-17