meta-learn

name	meta-learn
description	This skill should be used when the user asks to "improve evaluator", "learn from evaluations", "evolve evaluation prompts", "check prediction accuracy", "review overridden verdicts", "meta-learn", or "how accurate are plugin evaluations". Reviews past evaluations vs actual outcomes and evolves the screening/review prompts.
argument-hint	[--report-only] [--evolve-prompts]
allowed-tools	["Read","Write","Edit","Glob","Grep","Bash","Agent"]

Meta-Learning for Marketplace Evaluator

Analyze past evaluation accuracy and evolve the evaluator's own prompts and criteria.

Data Sources

1. Evaluations JSON — All past verdicts with confidence scores
2. Settings JSON — Which plugins are actually enabled (ground truth)
3. Meta-learning JSON — History of prompt evolution and accuracy metrics
4. User overrides — Verdicts manually changed by the user

Analysis Pipeline

Step 1: Collect Outcomes

1. Read current .claude/settings.json to find all enabled plugins
2. Compare against evaluation verdicts:
   • INSTALL verdicts that ARE enabled → True Positive
   • INSTALL verdicts that are NOT enabled → False Positive (user rejected)
   • REJECT verdicts that ARE enabled → False Negative (user overrode)
   • REJECT verdicts that are NOT enabled → True Negative
3. Compute accuracy, precision, recall, F1

Step 2: Analyze Patterns

1. For False Positives: Why did the evaluator recommend plugins the user rejected?
   • Policy violations missed?
   • Overlap with existing tooling not detected?
   • Context budget impact underestimated?
2. For False Negatives: Why did the evaluator reject plugins the user installed?
   • Policy too strict? (user accepted a policy violation)
   • Value underestimated?
   • Goal alignment missed?

Step 3: Evolve Prompts (if --evolve-prompts)

1. Spawn meta-learner agent (model from config)
2. Agent reviews the pattern analysis and current agent prompts
3. Agent proposes specific prompt modifications:
   • Screening criteria adjustments
   • Policy strictness calibration
   • Goal keyword additions
   • Confidence scoring weight changes
4. Write proposals to meta-learning JSON for human review
5. Do NOT auto-apply prompt changes — present for approval

Step 4: Report

Output a meta-learning report:

Meta-Learning Report
====================
Evaluations analyzed: 51
Accuracy: 82% | Precision: 75% | Recall: 90%

False Positives (recommended but not installed): 5
  - plugin-x: User rejected because [pattern]
  - plugin-y: Overlap with existing tooling

False Negatives (rejected but user installed): 2
  - plugin-z: Policy override accepted by user

Prompt Evolution Proposals:
  1. Relax bash-hooks policy for guard utilities (3 FN from this)
  2. Add "worktree" keyword to agent-improvement goal
  3. Increase overlap detection weight from 0.2 to 0.3

Additional Resources

• references/meta-learning-patterns.md — Common patterns in evaluation drift and correction strategies