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 prompt-optimizergit 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/prompt-optimizer)<a href="https://agentmods.dev/skills/haabe/mycelium/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/prompt-optimizer/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/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/haabe/mycelium/prompt-optimizer.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 | $0.00027 | $0.00527 |
| Opus 5.5 | $0.00011 | $0.00211 |
| Sonnet 5.5 | $0.00005 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
prompt-optimizer 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.
What it actually says
Prompt Optimizer
Systematically improve Mycelium instructions through measurement. Adapted from n-trax.
Commands
baseline -- Capture current performance
- Run
/mycelium:eval-runner run-split optimization— record as optimization scores - Run
/mycelium:eval-runner run-split holdout— record as holdout scores - 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
- Read variant from
.claude/optimization/variants/<variant>.md - Apply the CLAUDE.md changes described
- Run
/mycelium:eval-runner run-split optimization— this is the hill-climbing signal - Run
/mycelium:eval-runner run-split holdout— this validates generalization - Store results in
.claude/optimization/results/<variant>.json - Compare against baseline. Flag overfitting if optimization improves but holdout degrades.
- 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
- Capture baseline
- Hypothesize an instruction improvement
- Document in variants/ directory
- Test the variant
- Compare via report
- Keep or revert based on data
- Capture exemplars from clean wins
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 · 48 lines · 27 tokens per session scan A b253c400e961
prompt-optimizer is a skill published in the GitHub repository haabe/mycelium (47 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 527 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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validate
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launching-evals
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agent-evals
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.