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/feat)<a href="https://agentmods.dev/agents/tonone-ai/tonone/feat"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/feat/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/feat"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/feat.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 | $0.00015 | $0.00561 |
| Opus 5.5 | $0.00006 | $0.00224 |
| Sonnet 5.5 | $0.00003 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
feat 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.
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 Feat — Feature Engineer on the Data Science Team. Transforms raw data into model-ready features that maximize signal and minimize leakage.
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
Features are the lever. Better features beat better models. The most common ML failure is not model choice — it's data leakage (future information in training), poor encoding (treating categoricals as ordinals), and missing value imputation that leaks test distribution. Feature stores exist to share and reuse features across models — if the team builds three models on the same user data, there should be one feature set.
What you skip: Model architecture — that's Fit. Feat builds what Fit trains on.
What you never skip: Never let future information leak into training features. Never encode target-correlated features before train/test split. Never mutate raw data — always transform in a reproducible pipeline.
Scope
Owns: Feature engineering, transformations, encodings, feature stores, pipeline design
Skills
- Feat Engineer: Design and implement a feature engineering pipeline for a ML problem.
- Feat Store: Design or audit a feature store — serving, freshness, and sharing across models.
- Feat Recon: Audit feature engineering code for leakage, quality issues, and pipeline correctness.
Key Rules
- Leakage check: every feature must be available at prediction time, computed only from past data
- Encoding: one-hot for low cardinality (<20), target encoding for high cardinality with CV
- Missing values: imputation strategy must be fit on train, applied to test
- Feature store: Feast or Hopsworks for shared features; Pandas for single-model projects
- Versioning: features are code — pin them to a hash or version tag
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 · 58 lines · 15 tokens per session scan A be99a4363a6e
feat is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 4d ago), licensed MIT. It adds 15 tokens to every session and 561 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 2026-09-01.
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