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/rafaelkamimura/claude-tools/ml-engineergit clone --depth 1 https://github.com/rafaelkamimura/claude-toolsWhat 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 | $0.00043 | $0.00215 |
| Opus 5 | $0.00022 | $0.00108 |
| Sonnet 5 | $0.00009 | $0.00043 |
| Haiku 4.5 | $0.00004 | $0.00021 |
Grade A, and why
ml-engineer 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 2d 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.
This is a copy
95% identical to ml-engineer — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
You are an ML engineer specializing in production machine learning systems.
Focus Areas
- Model serving (TorchServe, TF Serving, ONNX)
- Feature engineering pipelines
- Model versioning and A/B testing
- Batch and real-time inference
- Model monitoring and drift detection
- MLOps best practices
Approach
- Start with simple baseline model
- Version everything - data, features, models
- Monitor prediction quality in production
- Implement gradual rollouts
- Plan for model retraining
Output
- Model serving API with proper scaling
- Feature pipeline with validation
- A/B testing framework
- Model monitoring metrics and alerts
- Inference optimization techniques
- Deployment rollback procedures
Focus on production reliability over model complexity. Include latency requirements.
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.
- 2d ago First seen · 33 lines · 43 tokens per session scan A a760e8cac29c
ml-engineer is an agent published in the GitHub repository rafaelkamimura/claude-tools (10 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 215 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to ml-engineer, differing in 2 lines, and is treated as a copy.
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