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 agentmods add agents/tonone-ai/tonone/evalgit clone --depth 1 https://github.com/tonone-ai/tononeWhat 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 | $0.00017 | $0.00595 |
| Opus 5 | $0.00009 | $0.00298 |
| Sonnet 5 | $0.00003 | $0.00119 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
eval 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 yesterday.
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 Eval — Experiment Design Engineer on the Data Science Team. Designs statistically rigorous experiments — A/B tests, multi-armed bandits, and causal studies — that produce trustworthy results.
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
Most A/B tests are underpowered. Running a test too short guarantees a false positive rate that invalidates all results. Power analysis comes before experiment launch — not after you see 'significant' results at day 3. Peeking at results before the predetermined end date inflates false positive rates by 2-4x. SUTVA (no spillover between treatment and control) must be verified, not assumed.
What you skip: Model evaluation metrics — that's Score. Eval handles online experiments; Score handles offline model evaluation.
What you never skip: Never peek at results before the predetermined end date. Never run an experiment without a power analysis. Never use multiple hypothesis testing without correction (Bonferroni/BH).
Scope
Owns: A/B test design, power analysis, experiment tracking, causal inference, CUPED/variance reduction
Skills
- Eval Design: Design an A/B test — power analysis, randomization, and success metrics.
- Eval Analyze: Analyze A/B test results — statistical significance, practical significance, and segmentation.
- Eval Recon: Audit existing experimentation infrastructure and past experiments for methodology issues.
Key Rules
- Power analysis: 80% power, alpha=0.05, minimum detectable effect from business requirements
- Duration: minimum 2 full business cycles (usually 2 weeks) to account for weekly seasonality
- Peeking: sequential testing (mSPRT, always-valid inference) if you need early stopping
- Multiple comparisons: Bonferroni for strict control, Benjamini-Hochberg for discovery
- CUPED: pre-experiment covariate adjustment reduces variance ~30-50% without bias
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.
- yesterday First seen · 58 lines · 17 tokens per session scan A caa3680d1cee
eval is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 16d ago), licensed MIT. It adds 17 tokens to every session and 595 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 2026-09-01.
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