Agent skill · Code Review & Quality

metacognitive-self-mod

Analyze and improve the improvement process. Use for detecting quality regressions and refining meta-optimization.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill metacognitive-self-mod --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.9.0
Path: skills/agent/metacognitive-self-mod/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Metacognitive Self-Modification Analyze the effectiveness of past skill improvements and refine the improvement process itself. This is the core innovation from the Hyperagents paper: not just improving skills, but improving HOW skills are improved. ## Context Triggers (auto-invocation) This skill should be invoked automatically when: 1. **Regression detected**: The homeostatic monitor finds a skill's evaluation window ended in `pending_rollback_review` status. The improvement made things worse -- we need to understand why. 2. **Low effectiveness rate**: When `ImprovementMemory.get_effective_strategies()` vs `get_failed_strategies()` shows effectiveness below 50%, the improvement process itself needs refinement. 3. **Degradation despite improvements**: When `PerformanceTracker.get_improvement_trend()` returns negative for a skill that was recently improved. 4. **Periodic check**: After every 10 improvement cycles (tracked via outcome count in ImprovementMemory). ### Hook integration The homeostatic monitor emits `"improvement_triggered": true` when a skill crosses the flag threshold. At that point, before dispatching the skill-improver, check if metacognitive analysis is warrante

What's inside
Steps it walks through
  1. Context Triggers (auto-invocation)
  2. Hook integration
  3. When To Use (Manual)
  4. When NOT To Use
  5. Workflow
  6. Step 1: Load improvement data
  7. Step 2: Classify improvement outcomes
  8. Step 3: Extract meta-patterns
  9. Step 4: Analyze improvement trends
  10. Step 5: Generate strategy recommendations
  11. Step 6: Store meta-insights
  12. Step 7: Update skill-improver strategy
  13. Output
  14. Related
Ships with 1 file
  • metadata.json
Commands it runs
Check for improvement memory
if [ ! -f "$MEMORY_FILE" ]; then
echo "No improvement memory found."
echo "Run skill-improver first to generate improvement data."
exit 0
fi
More from claude-skill-registry
All skills →
About this skill
What does the metacognitive-self-mod skill do?

Analyze and improve the improvement process. Use for detecting quality regressions and refining meta-optimization.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill metacognitive-self-mod --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.

Keep going