mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
synced 2025-12-29 16:16:08 +00:00
c6c828a926
- Add Layer 0 Bootstrap (150 tokens, 95% reduction) - Document Intent Classification System (5 complexity levels) - Add Progressive Loading strategy (5-layer) - Document mindbase integration incentive (38% savings) - Update with 2025-10-17 redesign details
91 lines
3.8 KiB
Markdown
91 lines
3.8 KiB
Markdown
# PM Agent Context
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**Project**: SuperClaude_Framework
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**Type**: AI Agent Framework
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**Tech Stack**: Claude Code, MCP Servers, Markdown-based configuration
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**Current Focus**: Token-efficient architecture with progressive context loading
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## Project Overview
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SuperClaude is a comprehensive framework for Claude Code that provides:
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- Persona-based specialized agents (frontend, backend, security, etc.)
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- MCP server integrations (Context7, Magic, Morphllm, Sequential, etc.)
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- Slash command system for workflow automation
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- Self-improvement workflow with PDCA cycle
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- **NEW**: Token-optimized PM Agent with progressive loading
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## Architecture
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- `superclaude/agents/` - Agent persona definitions
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- `superclaude/commands/` - Slash command definitions (pm.md: token-efficient redesign)
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- `docs/` - Documentation and patterns
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- `docs/memory/` - PM Agent session state (local files)
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- `docs/pdca/` - PDCA cycle documentation per feature
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- `docs/research/` - Research reports (llm-agent-token-efficiency-2025.md)
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## Token Efficiency Architecture (2025-10-17 Redesign)
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### Layer 0: Bootstrap (Always Active)
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- **Token Cost**: 150 tokens (95% reduction from old 2,300 tokens)
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- **Operations**: Time awareness + repo detection + session initialization
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- **Philosophy**: User Request First - NO auto-loading before understanding intent
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### Intent Classification System
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```yaml
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Ultra-Light (100-500 tokens): "進捗", "progress", "status" → Layer 1 only
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Light (500-2K tokens): "typo", "rename", "comment" → Layer 2 (target file)
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Medium (2-5K tokens): "bug", "fix", "refactor" → Layer 3 (related files)
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Heavy (5-20K tokens): "feature", "architecture" → Layer 4 (subsystem)
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Ultra-Heavy (20K+ tokens): "redesign", "migration" → Layer 5 (full + research)
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```
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### Progressive Loading (5-Layer Strategy)
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- **Layer 1**: Minimal context (mindbase: 500 tokens | fallback: 800 tokens)
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- **Layer 2**: Target context (500-1K tokens)
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- **Layer 3**: Related context (mindbase: 3-4K | fallback: 4.5K)
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- **Layer 4**: System context (8-12K tokens, user confirmation)
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- **Layer 5**: External research (20-50K tokens, WARNING required)
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### Workflow Metrics Collection
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- **File**: `docs/memory/workflow_metrics.jsonl`
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- **Purpose**: Continuous A/B testing for workflow optimization
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- **Data**: task_type, complexity, workflow_id, tokens_used, time_ms, success
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- **Strategy**: ε-greedy (80% best workflow, 20% experimental)
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### mindbase Integration Incentive
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- **Layer 1**: 500 tokens (mindbase) vs 800 tokens (fallback) = **38% savings**
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- **Layer 3**: 3-4K tokens (mindbase) vs 4.5K tokens (fallback) = **20% savings**
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- **Total Potential**: Up to **90% token reduction** with semantic search (industry benchmark)
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## Active Patterns
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- **Repository-Scoped Memory**: Local file-based memory in `docs/memory/`
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- **PDCA Cycle**: Plan → Do → Check → Act documentation workflow
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- **Self-Evaluation Checklists**: Replace Serena MCP `think_about_*` functions
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- **User Request First**: Bootstrap → Wait → Intent → Progressive Load → Execute
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- **Continuous Optimization**: A/B testing via workflow_metrics.jsonl
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## Recent Changes (2025-10-17)
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### PM Agent Token Efficiency Redesign
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- **Removed**: Auto-loading 7 files on startup (2,300 tokens wasted)
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- **Added**: Layer 0 Bootstrap (150 tokens) + Intent Classification
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- **Added**: Progressive Loading (5-layer) + Workflow Metrics
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- **Result**:
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- Ultra-Light tasks: 2,300 → 650 tokens (72% reduction)
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- Light tasks: 3,500 → 1,200 tokens (66% reduction)
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- Medium tasks: 7,000 → 4,500 tokens (36% reduction)
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### Research Integration
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- **Report**: `docs/research/llm-agent-token-efficiency-2025.md`
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- **Benchmarks**: Trajectory Reduction (99%), AgentDropout (21.6%), Vector DB (90%)
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- **Source**: Anthropic, Microsoft AutoGen v0.4, CrewAI + Mem0, LangChain
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## Known Issues
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None currently.
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## Last Updated
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2025-10-17
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