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/eval-runner

Run eval scenarios to benchmark Mycelium effectiveness. Execute tasks using reflexion loop, validate against success criteria, record metrics.

From plugin
mycelium
4662 skills
Install
$ npx -y skills add haabe/mycelium --skill eval-runner --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/eval-runner

Context preview

The summary Claude sees to decide when to auto-load this skill.

Run eval scenarios to benchmark Mycelium effectiveness. Execute tasks using reflexion loop, validate against success criteria, record metrics.

SKILL.md

eval-runner.SKILL.md
name: eval-runner
description: "Run eval scenarios to benchmark Mycelium effectiveness. Execute tasks using reflexion loop, validate against success criteria, record metrics."
metadata:
  instruction_budget: "41"
  framework_dependency: "mycelium"
  framework_dependency_note: "This skill is designed to run within the Mycelium framework (https://github.com/haabe/mycelium). Standalone use will skip the canvas state, theory gates, and harness behavior the skill assumes. Install: /plugin install mycelium@haabe-mycelium."

Eval Runner

Benchmark the agent's performance against defined scenarios. Adapted from n-trax eval system.

Commands

`run <category/name>`

1. Read YAML from `.claude/evals/scenarios/<category>/<name>.yml` 2. Parse fields (name, category, task_prompt, success_criteria, budget) 3. Execute setup steps if defined 4. Record start time 5. Execute task via reflexion workflow (read corrections first) 6. Record end time and iteration count 7. Validate ALL success criteria 8. Write result JSON to `.claude/evals/results/<timestamp>-<name>.json` 9. Report summary

`run-all [category]`

1. Glob `.claude/evals/scenarios/**/*.yml` 2. Skip scenarios with `status: retired` 3. For each: run in isolation (git stash), record result, restore 4. Update `.claude/evals/pass-history.json` with each result 5. Aggregate and report

`run-split <optimization|holdout>`

1. Glob `.claude/evals/scenarios/**/*.yml` 2. Read each YAML, filter by `split` field matching the requested set 3. Skip scenarios with `status: retired` 4. For each matching scenario: run in isolation, record result, restore 5. Update `.claude/evals/pass-history.json` with each result 6. Aggregate and report (label output clearly as "Optimization Set" or "Holdout Set")

`report`

1. Read all results from `.claude/evals/results/` 2. Generate summary table:

| Category    | Pass Rate | Avg Iterations | Avg Time | Notes |
|-------------|-----------|----------------|----------|-------|
| discovery   | ...       | ...            | ...      |       |
| delivery    | ...       | ...            | ...      |       |
| integration | ...       | ...            | ...      |       |
| **Overall** | ...       | ...            | ...      |       |

3. List failure patterns and recommendations

`prune`

1. Read `.claude/evals/pass-history.json` 2. Flag evals where `last_5` is all-pass (`saturated`) or all-fail (`broken`) 3. Flag evals with no runs in 30+ days (`stale`) 4. For saturated evals, suggest: retire or increase difficulty 5. For broken evals, suggest: fix criteria or retire 6. Present recommendations — do NOT auto-retire 7. On user confirmation: set `status: retired` in scenario YAML, update pass-history.json, log in .claude/harness/decision-log.md

`mine`

Analyze audit logs to propose new eval scenarios from observed failure patterns.

1. Read `.claude/state/change-log.jsonl` (last 100 entries) 2. Read `.claude/state/diamond-state-audit.jsonl` (all entries) 3. Group change-log entries by `session_id`, identify: a. **Correction clusters**: 3+ edits to same file in one session (agent struggled) b. **Skill friction**: edits to `.claude/skills/*/SKILL.md` during a session (instructions unclear) c. **Missing test coverage**: 5+ files changed with no test file edits 4. Count diamond bypass entries from audit log 5. Cross-reference with existing scenarios in pass-history.json to avoid duplicates 6. For each pattern, output a proposed eval scenario as YAML template 7. Tag proposals with `source: trace-mining` and originating `session_id` 8. Do NOT auto-create files — present for human review

See `.claude/evals/schema.md` §Trace Mining Heuristics for pattern-to-eval mappings.

Result Format

See `.claude/evals/schema.md` for YAML scenario and JSON result formats.

Pass History

After writing each result JSON (step 8 of `run`), also update `.claude/evals/pass-history.json`:

  • Increment `runs` and `passes` (if passed) for the eval
  • Append `true`/`false` to `last_5` (trim to keep only last 5)
  • Update `last_run` timestamp

Creating Scenarios

Place YAML files in `.claude/evals/scenarios/<category>/`. Define task_prompt, success_criteria, and budget. Set `split`, `status`, and `source` fields per schema.

Read more
Ships withmycelium

A harness that asks who this is for before the agent writes code. Built on Claude Code, where the gates are structural. The files and skills port to opencode, Codex and Cursor. Outcome over output. You know how this goes.

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Python
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MIT
License
3d ago
Last commit
5mo ago
Created

Repo: haabe/mycelium

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