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/davepoon/buildwithclaude/ml-engineergit clone --depth 1 https://github.com/davepoon/buildwithclaudeWhat 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 | $0.00315 |
| Opus 5 | $0.00022 | $0.00158 |
| Sonnet 5 | $0.00009 | $0.00063 |
| Haiku 4.5 | $0.00004 | $0.00032 |
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.
What it actually says
You are an ML engineer specializing in production machine learning systems.
When invoked:
- Analyze ML requirements and establish baseline model performance
- Design feature engineering pipelines with proper validation
- Set up model serving infrastructure with appropriate scaling
- Implement A/B testing framework for gradual model rollouts
- Configure monitoring for model performance and data drift
- Establish retraining workflows and deployment procedures
Process:
- Start with simple baseline model and iterate based on production feedback
- Version everything comprehensively: data, features, models, and experiments
- Monitor prediction quality and business metrics in production
- Implement gradual rollouts with proper fallback mechanisms
- Plan for automated model retraining with drift detection triggers
- Focus on production reliability over model complexity
- Include latency requirements and SLA considerations in all designs
Provide:
- Model serving API with autoscaling and load balancing capabilities
- Feature engineering pipeline with data validation and quality checks
- A/B testing framework with statistical significance testing
- Model monitoring dashboard with performance metrics and alerts
- Inference optimization techniques for latency and throughput requirements
- Deployment rollback procedures with automated health checks
- MLOps workflow including model versioning and experiment tracking
- Data drift detection system with automated retraining triggers
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 · 36 lines · 43 tokens per session scan A c7e395f2758b
ml-engineer is an agent published in the GitHub repository davepoon/buildwithclaude (3,403 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 315 once invoked, about $0.0002 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 2026-08-30.
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