qwen_wsp_enhancement
Enhance WSP protocols using Qwen strategic analysis and 0102 supervision. Use when enhancing WSPs, analyzing protocol gaps, generating WSP recommendations, or coordinating multi-WSP updates.
npx skills add majiayu000/claude-skill-registry --skill qwen-wsp-enhancement-foundup-foundups-agent --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Qwen analyzes an existing WSP, identifies gaps between the WSP content and its implementation, and generates structured enhancement recommendations. It emphasizes updating existing WSPs with concrete additions, supported by evidence from related implementations and other WSPs, and stores successful patterns for future use. All recommendations are reviewed and approved by 0102 before application.
How it works
Phase 1: WSP Analysis (Qwen)
- Input: WSP protocol file path
- Read WSP content
- Identify current sections
- Analyze codebase for implementation
- Find missing sections
- Produce a gap analysis report with current sections, missing topics, evidence, and recommendation count
Phase 2: Recommendation Generation (Qwen)
- Input: Gap analysis report
- For each gap, read related implementation code and related WSPs
- Generate a WSP section content for the gap using code examples and related WSPs
- Produce a WSP Enhancement Recommendations document with sections, locations, priorities, and content
Phase 3: 0102 Review & Feedback (Big Brother Supervision)
- Input: Qwen recommendations
- 0102 reviews for technical accuracy, architectural alignment, and quality
- Output: APPROVED or REJECT with feedback; stores learning pattern
Phase 4: Application & Validation (0102)
- Apply approved recommendations to WSP framework
- Sync to knowledge layer (WSP 32 Three-State Architecture)
- Run validation and store success pattern in pattern_memory
Phase 5: Learning Integration (Qwen Pattern Memory)
- Integrate feedback into learning patterns for future WSP enhancements
When to use it
Use when enhancing WSPs, analyzing protocol gaps, generating WSP recommendations, or coordinating multi-WSP updates. Requires holo_index, pattern_memory, wsp_framework and uses claude-code as declared tool.
What it can touch
Uses inputs from WSP protocol files and related implementation code; outputs recommendations and updated WSP content for integration (no direct file edits described here).
Caveats
All recommendations require 0102 final approval before application. The process relies on existing MCP and DAE references for evidence. Pattern memory stores learning patterns only after successful approvals and applications.
# Qwen WSP Enhancement Skills ## Overview This skills file defines how Qwen (1.5B strategic planner) enhances WSP protocols under 0102 supervision. Qwen analyzes existing WSPs, identifies gaps, generates enhancement recommendations, and learns from 0102 feedback. ## Core Principles - **Precision Over Proliferation**: Enhance existing WSPs, don't create new ones unnecessarily - **Evidence-Based Updates**: Ground recommendations in actual implementation (MCP servers, DAE architectures) - **Preserve Intent**: Never delete or contradict existing WSP content, only enhance - **0102 Supervision**: All recommendations reviewed by 0102 before application - **Pattern Learning**: Store successful enhancement patterns for future WSP work --- ## Qwen's Role: Strategic WSP Analyst ### What Qwen Does Well (Strategic Planning) ✅ **Gap Analysis**: Read WSP, compare to implementation, identify missing sections ✅ **Structured Recommendations**: Generate specific additions with examples ✅ **Cross-Protocol Synthesis**: Connect related WSPs (WSP 80 ↔ WSP 96 ↔ WSP 91) ✅ **Pattern Recognition**: Learn what makes good WSP enhancements ✅ **Batch Processing**: Handle multiple WSP updates systematically ### W
- Overview
- Core Principles
- Qwen's Role: Strategic WSP Analyst
- What Qwen Does Well (Strategic Planning)
- What 0102 Does (Big Brother Supervision)
- WSP Enhancement Workflow
- Phase 1: WSP Analysis (Qwen)
- Phase 2: Recommendation Generation (Qwen)
- Phase 3: 0102 Review & Feedback (Big Brother Supervision)
- Phase 4: Application & Validation (0102)
- Phase 5: Learning Integration (Qwen Pattern Memory)
- Qwen Confidence Levels
- High Confidence Tasks (Qwen Autonomous)
- Medium Confidence Tasks (0102 Review Required)
python holo_index.py --search "WSP 80 Cube-Level DAE current content" Qwen reads WSP 80 via Holo python holo_index.py --search "VisionDAE MCP endpoints implementation" python holo_index.py --search "YouTube DAE cardiovascular design" Qwen finds implementation evidence Three-state sync to WSP_knowledge Qwen pattern memory updated with success
What does the qwen_wsp_enhancement skill do?
Enhance WSP protocols using Qwen strategic analysis and 0102 supervision. Use when enhancing WSPs, analyzing protocol gaps, generating WSP recommendations, or coordinating multi-WSP updates.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill qwen-wsp-enhancement-foundup-foundups-agent --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.