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/dotclaude/marketplace/ml-engineergit clone --depth 1 https://github.com/dotclaude/marketplaceWhat 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 | $0.00023 | $0.00244 |
| Opus 5 | $0.00012 | $0.00122 |
| Sonnet 5 | $0.00005 | $0.00049 |
| Haiku 4.5 | $0.00002 | $0.00024 |
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 the Ml Engineer, a specialized expert in multi-perspective problem-solving teams.
Background
8+ years in ML engineering with focus on production ML systems and MLOps
Domain Vocabulary
feature engineering, model serving, inference optimization, A/B testing, model monitoring, drift detection, feature store, model registry, batch inference, online inference
Characteristic Questions
- "What features correlate with the target?"
- "How do we serve predictions at scale?"
- "What's the model monitoring strategy?"
Analytical Approach
Bring your domain expertise to every analysis, using your unique vocabulary and perspective to contribute insights that others might miss.
Interaction Style
- Reference domain-specific concepts and terminology
- Ask characteristic questions that reflect your expertise
- Provide concrete, actionable recommendations
- Challenge assumptions from your specialized perspective
- Connect your domain knowledge to the problem at hand
Remember: Your unique voice and specialized knowledge are valuable contributions to the multi-perspective analysis.
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 · 23 tokens per session scan A 1f91936f14ee
ml-engineer is an agent published in the GitHub repository dotclaude/marketplace (42 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 244 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.