Prompt Optimizer

SkillAI & models

A/B test CLAUDE.md instruction changes against eval benchmarks. Capture baselines, test variants, compare results.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Prompt Optimizer skill

What this skill tells your AI

The instructions your AI receives, as published by haabe/mycelium in plugins/mycelium/skills/prompt-optimizer/SKILL.md and read by ahel’s review.

Systematically improve Mycelium instructions through measurement. Adapted from n-trax.

Commands

baseline -- Capture current performance

  1. Run /mycelium:eval-runner run-split optimization — record as optimization scores
  2. Run /mycelium:eval-runner run-split holdout — record as holdout scores
  3. Record both to .claude/optimization/baseline.json: timestamp, CLAUDE.md hash, optimization metrics, holdout metrics, overall and per-category metrics

test <variant> -- Test a variant

  1. Read variant from .claude/optimization/variants/<variant>.md
  2. Apply the CLAUDE.md changes described
  3. Run /mycelium:eval-runner run-split optimization — this is the hill-climbing signal
  4. Run /mycelium:eval-runner run-split holdout — this validates generalization
  5. Store results in .claude/optimization/results/<variant>.json
  6. Compare against baseline. Flag overfitting if optimization improves but holdout degrades.
  7. Do NOT auto-revert -- let user decide

report -- Compare all variants

Generate comparison table with split-aware columns:

| Variant | Opt Pass Rate | Holdout Pass Rate | Delta Opt | Delta Holdout | Overfit? | Decision |

Flag Overfit? = YES when optimization delta is positive but holdout delta is negative.

exemplar <eval-name> -- Capture winning trajectory

After a clean eval win (1 iteration, fast), save the approach to .claude/optimization/exemplars/.

Workflow

  1. Capture baseline
  2. Hypothesize an instruction improvement
  3. Document in variants/ directory
  4. Test the variant
  5. Compare via report
  6. Keep or revert based on data
  7. Capture exemplars from clean wins

Signals

GitHub stars
45
Forks
3
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
prompt-optimizer-haabe
Source
github.com/haabe/mycelium