npx claudepluginhub haabe/mycelium --plugin myceliumThis skill uses the workspace's default tool permissions.
Systematically improve Mycelium instructions through measurement. Adapted from n-trax.
Runs metric-driven iterative optimization loops for code performance, prompts, clustering, search relevance, or other metrics. Builds measurement scaffolding, runs parallel experiments, evaluates via hard gates/LLM judges, iterates to best solution.
Runs autonomous optimization loops to iteratively improve prompts, templates, configs, or code using four-way separation of main agent, eval agent, test runner, and deterministic eval.py judge. Invoke via /autoresearch or 'optimize this prompt'.
Share bugs, ideas, or general feedback.
Systematically improve Mycelium instructions through measurement. Adapted from n-trax.
baseline -- Capture current performance/mycelium:eval-runner run-split optimization — record as optimization scores/mycelium:eval-runner run-split holdout — record as holdout scores.claude/optimization/baseline.json: timestamp, CLAUDE.md hash, optimization metrics, holdout metrics, overall and per-category metricstest <variant> -- Test a variant.claude/optimization/variants/<variant>.md/mycelium:eval-runner run-split optimization — this is the hill-climbing signal/mycelium:eval-runner run-split holdout — this validates generalization.claude/optimization/results/<variant>.jsonreport -- Compare all variantsGenerate 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 trajectoryAfter a clean eval win (1 iteration, fast), save the approach to .claude/optimization/exemplars/.