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 zjunlp/Mechanist --skill finetune-hyperparameter-sweepgit clone --depth 1 https://github.com/zjunlp/MechanistWrote 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/zjunlp/mechanist/finetune-hyperparameter-sweep)<a href="https://agentmods.dev/skills/zjunlp/mechanist/finetune-hyperparameter-sweep"><img src="https://agentmods.dev/badge/skills/zjunlp/mechanist/finetune-hyperparameter-sweep.svg" alt="Measured on agentmods" 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 | $0.00224 | $0.00406 |
| Opus 5 | $0.00112 | $0.00203 |
| Sonnet 5 | $0.00045 | $0.00081 |
| Haiku 4.5 | $0.00022 | $0.00041 |
Grade A, and why
finetune-hyperparameter-sweep 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 8d 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
Fine-Tuning LR Sweep
-
Any fine-tune → always sweep LR over a wide range. Whether the plan hard-codes an LR, copies one from a paper, or leaves it open makes no difference: that value is one grid point, never the answer.
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The only test of an LR is the criteria — the pass criteria in
task.md, or the claim criteria of the relevant experiment-plan milestone. Nothing else counts as acceptance evidence: a converged loss, a healthy grad norm, a rising reward, a "reasonable-looking" curve certify nothing. Criteria not met = LR not acceptable, no matter how good the training curves look. -
Criteria not met → change LR and re-run. Keep going until an LR passes. Only after the whole LR range has failed should you touch anything else.
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.
- 8d ago First seen · 13 lines · 224 tokens per session scan A 7fb4dd3d3f97
finetune-hyperparameter-sweep is a skill published in the GitHub repository zjunlp/Mechanist (72 stars, last pushed 12d ago), licensed MIT. It adds 224 tokens to every session and 406 once invoked, about $0.0011 per session on Opus 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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