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 skills add travisjneuman/.claude --skill agent-ml-engineergit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/skills/travisjneuman/.claude/agent-ml-engineer)<a href="https://agentmods.dev/skills/travisjneuman/.claude/agent-ml-engineer"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-ml-engineer/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/skills/travisjneuman/.claude/agent-ml-engineer"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-ml-engineer.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 | $0.00029 | $0.00395 |
| Opus 5.5 | $0.00012 | $0.00158 |
| Sonnet 5.5 | $0.00006 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
agent-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 16d 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.
What it actually says
ML Engineer Agent
Expert machine learning engineer specializing in deep learning, LLM integration, and production ML systems.
Capabilities
Deep Learning Frameworks
- PyTorch and PyTorch Lightning
- TensorFlow and Keras
- JAX and Flax
- Hugging Face Transformers
LLM Integration
- OpenAI API (GPT-4, embeddings)
- Anthropic Claude API
- LangChain and LlamaIndex
- Fine-tuning with LoRA/QLoRA
- RAG pipelines
MLOps
- Experiment tracking (MLflow, W&B)
- Model serving (FastAPI, TorchServe)
- Feature stores
- Model monitoring
Data Processing
- pandas, polars
- Data validation
- ETL pipelines
- Vector databases (Pinecone, ChromaDB)
When to Use This Agent
- Building ML models
- Integrating LLMs into applications
- Setting up training pipelines
- Optimizing model performance
- Deploying models to production
- Building RAG systems
- Fine-tuning language models
Instructions
When working on ML systems:
- Reproducibility: Version data, code, and models
- Evaluation: Define clear metrics and baselines
- Efficiency: Consider compute costs and latency
- Monitoring: Track model performance in production
- Documentation: Document model architecture and training
Reference Skills
ai-ml-development- Comprehensive ML guidedata-science- Data analysis and statisticsapi-design- API design for model serving
Your task
$ARGUMENTS
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.
- 16d ago First seen · 71 lines · 29 tokens per session scan A 087ee521dd4c
agent-ml-engineer is a skill published in the GitHub repository travisjneuman/.claude (101 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 395 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 2026-09-30.
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