Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add haabe/mycelium --skill eval-runnergit clone --depth 1 https://github.com/haabe/myceliumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/haabe/mycelium/eval-runner)<a href="https://agentmods.dev/skills/haabe/mycelium/eval-runner"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/eval-runner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/haabe/mycelium/eval-runner"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/eval-runner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.01061 |
| Opus 5.5 | $0.00012 | $0.00424 |
| Sonnet 5.5 | $0.00006 | $0.00212 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
eval-runner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Runner
Benchmark the agent's performance against defined scenarios. Adapted from n-trax eval system.
Commands
run <category/name>
- Read YAML from
.claude/evals/scenarios/<category>/<name>.yml - Parse fields (name, category, task_prompt, success_criteria, budget)
- Execute setup steps if defined
- Record start time
- Execute task via reflexion workflow (read corrections first)
- Record end time and iteration count
- Validate ALL success criteria
- Write result JSON to
.claude/evals/results/<timestamp>-<name>.json - Report summary
run-all [category]
- Glob
.claude/evals/scenarios/**/*.yml - Skip scenarios with
status: retired - For each: run in isolation (git stash), record result, restore
- Update
.claude/evals/pass-history.jsonwith each result - Aggregate and report
run-split <optimization|holdout>
- Glob
.claude/evals/scenarios/**/*.yml - Read each YAML, filter by
splitfield matching the requested set - Skip scenarios with
status: retired - For each matching scenario: run in isolation, record result, restore
- Update
.claude/evals/pass-history.jsonwith each result - Aggregate and report (label output clearly as "Optimization Set" or "Holdout Set")
report
- Read all results from
.claude/evals/results/ - Generate summary table:
| Category | Pass Rate | Avg Iterations | Avg Time | Notes |
|-------------|-----------|----------------|----------|-------|
| discovery | ... | ... | ... | |
| delivery | ... | ... | ... | |
| integration | ... | ... | ... | |
| **Overall** | ... | ... | ... | |
- List failure patterns and recommendations
prune
- Read
.claude/evals/pass-history.json - Flag evals where
last_5is all-pass (saturated) or all-fail (broken) - Flag evals with no runs in 30+ days (
stale) - For saturated evals, suggest: retire or increase difficulty
- For broken evals, suggest: fix criteria or retire
- Present recommendations — do NOT auto-retire
- On user confirmation: set
status: retiredin scenario YAML, update pass-history.json, log in .claude/harness/decision-log.md
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 92 lines · 29 tokens per session scan A f398c17e0461
eval-runner is a skill published in the GitHub repository haabe/mycelium (47 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 1,061 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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