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 niels-emmer/myace --skill model-evaluationgit clone --depth 1 https://github.com/niels-emmer/myaceWrote 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/niels-emmer/myace/model-evaluation)<a href="https://agentmods.dev/skills/niels-emmer/myace/model-evaluation"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/model-evaluation/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/niels-emmer/myace/model-evaluation"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/model-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00019 | $0.00316 |
| Opus 5 | $0.00010 | $0.00158 |
| Sonnet 5 | $0.00004 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
Model Evaluation 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 6d 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
Purpose
Ensure models are evaluated rigorously before deployment decisions are made.
When to use it
After training, before declaring a model ready for deployment review.
Checklist
- Split strategy: holdout for large datasets, k-fold or stratified for smaller ones.
- Metric selection: classification (precision/recall/F1/AUC-ROC/AUC-PR), regression (MAE/RMSE/MAPE/R²), ranking (NDCG/MAP). Pick metrics that match the business problem.
- Baseline comparison: compare against a simple heuristic, dummy classifier, or previous model version.
- Confidence intervals: report uncertainty around metrics, not just point estimates.
- Calibration: for probabilistic models, check calibration curves.
- Per-slice evaluation: evaluate on subgroups (by category, value range, data source) to find failure pockets.
- Failure mode documentation: list known failure cases, edge behaviors, and conditions where performance degrades.
Expected output
An evaluation report with metrics, baseline comparison, and documented failure modes, logged to the experiment tracker.
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.
- 6d ago First seen · 30 lines · 19 tokens per session scan A 1ab0c6fe43bd
Model Evaluation is a skill published in the GitHub repository niels-emmer/myace (1 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 316 once invoked, about $0.0001 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 2026-09-03.
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