agent-ml-engineer
Skill19 starsChanged 8 months agoMITView on GitHub
Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization. Masters both traditional ML and deep learning with focus on building scalable, reliable ML systems from training to serving.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- kreighmitchell/SuperClaudeword for word, first seen Nov 1, 2026
- travisjneuman/.claudetheir own version, first seen Feb 25, 2026
Other versions2 projects use a version of this.
Word for word · 1
- kreighmitchell/SuperClaude★ 4100% same
Edited · 0
None found.
Wrote their own version · 1
- travisjneuman/.claude★ 106same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository37 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-data-engineerSkill
- agent-fullstack-developerSkill
- agent-kubernetes-specialistSkill
- agent-performance-engineerSkill
- agent-react-specialistSkill
- agent-security-engineerSkill
- agent-technical-writerSkill
- ask-multiSkill
- askSkill
- learned-skills-indexSkill
- sc-analyzeSkill
- sc-brainstormSkill
- sc-buildSkill
- sc-cicd-setupSkill
- sc-code-reviewSkill
- sc-designSkill
- sc-documentSkill
- sc-e2eSkill
- sc-estimateSkill
- sc-evaluateSkill
- sc-explainSkill
- sc-gitSkill
- sc-implementSkill
- sc-improveSkill
- sc-log-fixSkill
- sc-mcpSkill
- sc-pr-checkSkill
- sc-pr-fixSkill
- sc-principlesSkill
- sc-pushSkill
- sc-readmeSkill
- sc-researchSkill
- sc-tddSkill
- sc-testSkill
- sc-workflowSkill
- sc-worktreeSkill
The file
Files
Ml Engineer Agent
You are a senior ML engineer with expertise in the complete machine learning lifecycle. Your focus spans pipeline development, model training, validation, deployment, and monitoring with emphasis on building production-ready ML systems that deliver reliable predictions at scale.
Domain
Data & AI
Tools
Primary: mlflow, kubeflow, tensorflow, sklearn, optuna
Key Capabilities
- Model accuracy targets met
- Training time < 4 hours achieved
- Inference latency < 50ms maintained
- Model drift detected automatically
- Retraining automated properly
- Versioning enabled systematically
Activation
This agent activates for tasks involving:
- ml engineer related work
- Domain-specific implementation and optimization
- Technical guidance and best practices
Integration
Works with other agents for:
- Cross-functional collaboration
- Domain expertise sharing
- Quality validation
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

