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SuperClaude/docs/memory/pm_context.md
Cedric Hurst bea4bfe289
docs: Replace Mindbase References with ReflexionMemory (#464) 
* docs: fix mindbase syntax and document as optional MCP enhancement

Fix incorrect method call syntax and clarify mindbase as optional
enhancement that coexists with built-in ReflexionMemory.

Changes:
- Fix syntax: mindbase.search_conversations() → natural language
  instructions that allow Claude to autonomously select tools
- Clarify mindbase requires airis-mcp-gateway "recommended" profile
- Document ReflexionMemory as built-in fallback (always available)
- Show coexistence model: both systems work together

Architecture:
- ReflexionMemory (built-in): Keyword-based search, local JSONL
- Mindbase (optional MCP): Semantic search, PostgreSQL + pgvector
- Claude autonomously selects best available tool when needed

This approach allows users to enhance error learning with mindbase
when installed, while maintaining full functionality with
ReflexionMemory alone.

Related: #452

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add comprehensive ReflexionMemory user documentation

Add user-facing documentation for the ReflexionMemory error learning
system to address documentation gap identified during mindbase cleanup.

New Documentation:
- docs/user-guide/memory-system.md (283 lines)
  * Complete user guide for ReflexionMemory
  * How it works, storage format, usage examples
  * Performance benefits and troubleshooting
  * Manual inspection and management commands

- docs/memory/reflexion.jsonl.example (15 entries)
  * 15 realistic example reflexion entries
  * Covers common scenarios: auth, DB, CORS, uploads, etc.
  * Reference for understanding the data format

- docs/memory/README.md (277 lines)
  * Overview of memory directory structure
  * Explanation of all files (reflexion, metrics, patterns)
  * File management, backup, and git guidelines
  * Quick command reference

Context:
Previous mindbase cleanup removed references to non-existent external
MCP server, but didn't add sufficient user-facing documentation for
the actual ReflexionMemory implementation.

Related: #452

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: translate Japanese text to English in documentation

Address PR feedback to remove Japanese text from English documentation files.

Changes:
- docs/mcp/mcp-integration-policy.md: Translate headers and descriptions
- docs/reference/pm-agent-autonomous-reflection.md: Translate error messages
- docs/research/reflexion-integration-2025.md: Translate error messages
- docs/memory/pm_context.md: Translate example keywords

All Japanese text in English documentation files has been translated to English.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-10-31 08:44:35 +05:30

3.9 KiB
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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

  • plugins/superclaude/agents/ - Agent persona definitions
  • plugins/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):   "progress", "status", "update" → 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)

Error Learning & Memory Integration

  • ReflexionMemory (built-in): Layer 1: 650 tokens | Layer 3: 3.5-4K tokens
  • mindbase (optional): Layer 1: 500 tokens | Layer 3: 3-3.5K tokens (semantic search)
  • Profile: Requires airis-mcp-gateway "recommended" profile for mindbase
  • Savings: 20-35% with ReflexionMemory, additional 10-15% with mindbase enhancement

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