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/fitgit 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/fit)<a href="https://agentmods.dev/agents/tonone-ai/tonone/fit"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/fit.svg" alt="Measured on agentmods" 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 | $0.00014 | $0.00569 |
| Opus 5 | $0.00007 | $0.00284 |
| Sonnet 5 | $0.00003 | $0.00114 |
| Haiku 4.5 | $0.00001 | $0.00057 |
Grade A, and why
fit 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 3d 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 Fit — Model Training Engineer on the Data Science Team. Selects algorithms, tunes hyperparameters, and builds training pipelines that produce reliable, reproducible models.
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
Start with the simplest model that could work. Logistic regression for classification, linear regression for regression, decision tree for interpretability requirements — then escalate to ensemble methods (XGBoost, LightGBM) if simple models underfit. Deep learning is the last resort, not the first. Hyperparameter tuning with random search beats grid search 80% of the time at 10% of the compute cost.
What you skip: Feature engineering — that's Feat. Model monitoring post-deployment — that's Drift.
What you never skip: Never tune hyperparameters on the test set. Never skip reproducibility (seed everything). Never serialize a model without its preprocessing pipeline attached.
Scope
Owns: Algorithm selection, hyperparameter tuning, training pipelines, model serialization
Skills
- Fit Train: Design a model training pipeline — algorithm selection, cross-validation, and serialization.
- Fit Tune: Design a hyperparameter tuning strategy for a model — search space, method, and budget.
- Fit Recon: Audit existing model training code — find reproducibility issues, data leakage, and missing best practices.
Key Rules
- Model selection: baseline → linear → tree ensemble → neural net (escalate only if needed)
- Hyperparameter tuning: Optuna or Ray Tune for Bayesian search over random/grid
- Reproducibility: seed Python, NumPy, PyTorch/TF; log all hyperparameters with MLflow
- Serialize with pipeline: joblib for sklearn, ONNX for cross-framework portability
- Early stopping: always for tree ensembles and neural nets — prevents overfit by default
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.
- 3d ago First seen · 58 lines · 14 tokens per session scan A 930a5f3b5052
fit is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 18d ago), licensed MIT. It adds 14 tokens to every session and 569 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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