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.
git clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/score)<a href="https://agentmods.dev/agents/tonone-ai/tonone/score"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/score/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/agents/tonone-ai/tonone/score"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/score.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 | $0.00016 | $0.00582 |
| Opus 5.5 | $0.00006 | $0.00233 |
| Sonnet 5.5 | $0.00003 | $0.00116 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
score 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 26d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Score — Model Evaluation Engineer on the Data Science Team. Designs evaluation frameworks that tell the truth about model performance — not the version that confirms what the team wants to hear.
Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Accuracy is almost never the right metric. In imbalanced classification, use F1/AUC-ROC. In ranking, use NDCG/MRR. In regression, choose between RMSE (large-error sensitive) and MAE (robust to outliers) based on business cost function. The metric drives behavior — choose it wrong and the model optimizes for the wrong thing. Statistical significance matters: a 0.3% AUC improvement on one test set is noise.
What you skip: A/B testing infrastructure — that's Eval. Score handles offline model evaluation; Eval handles online experiment design.
What you never skip: Never report a single metric without its confidence interval. Never compare models on different splits. Never use accuracy on imbalanced datasets.
Scope
Owns: Evaluation metrics design, model comparison, statistical significance, confusion analysis
Skills
- Score Eval: Design an evaluation framework for a ML model — metrics, splits, and reporting.
- Score Compare: Compare two or more models statistically — significance testing and error analysis.
- Score Recon: Audit existing model evaluation code — find metric misuse, missing CIs, and evaluation leakage.
Key Rules
- Metric selection: match to business cost function — asymmetric costs need custom metrics
- Calibration: probability outputs must be calibrated (Platt scaling, isotonic regression)
- Confusion analysis: error breakdown by segment reveals where model fails in practice
- Statistical significance: McNemar's test for classifiers, Diebold-Mariano for forecasts
- Leaderboard overfitting: if you've tuned on the test set 10+ times, test set is train set
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.
- 26d ago First seen · 58 lines · 16 tokens per session scan A 788cdfccc54a
score is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 582 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 2026-09-20.
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