mlflow
Skill4 starsChanged 57 days agoNo licenceView on GitHub
Design, implement, audit, and troubleshoot MLflow workflows across projects. Use when tracking experiments, comparing runs, recording datasets and lineage, evaluating models, packaging custom pyfunc models, managing model versions and aliases, tracing or evaluating GenAI/RAG/agents, or preparing batch and online inference. Covers classical ML, deep learning, and custom pipelines; Python workflows and R integration guidance; local Windows/WSL, OpenShift 4, and AWS. Emphasises leakage-safe evaluation, reproducibility, security, and evidence-based promotion. Use for MLflow integration and lifecycle decisions, not as a substitute for domain-specific statistical design or a production infrastructure operator.
We split this skill into the rules it gives an agent
15 rules. 0 are common, 15 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
15
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- G1Joshi/Agent-Skills★ 15same name
- Clay-HHK/claude-skills★ 1same name
- mhiskall282/Securing-the-Digital-Mine-UNESCO-Project★ 1same 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 repository249 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
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- markdown-link-auditorSkill
- agent-governance-patternsSkill
- agentic-evalSkill
- agent-owasp-complianceSkill
- agent-supply-chainSkill
- ai-readySkill
- copilot-cli-quickstartSkill
- first-askSkill
- generate-custom-instructions-from-codebaseSkill
- github-copilot-starterSkill
- nano-banana-pro-openrouterSkill
- noob-modeSkill
- prompt-builderSkill
- remember-interactive-programmingSkill
- rememberSkill
- tldr-promptSkill
- what-context-neededSkill
- acceptance-test-planSkill
- architecture-auditSkill
- gap-analysisSkill
- miro-boardSkill
- process-mapSkill
- requirements-documentSkill
- risk-analysisSkill
- stakeholder-mapSkill
- tradeoff-analysisSkill
- use-caseSkill
- write-epicSkill
- write-user-storySkill
- architecture-models-4plus1Skill
- create-architectural-decision-recordSkill
- technology-stack-blueprint-generatorSkill
- dashboard-beautifySkill
- dashSkill
- daskSkill
- data-validation-and-schema-contractsSkill
- experiment-tracking-and-reproducibilitySkill
- exploratory-data-analysisSkill
- feature-engineering-and-leakage-checksSkill
- forecastic-evaluationSkill
- gradient-boostongSkill
- hypothesis-testing-and-ab-evaluationSkill
- imbalanced-classificationSkill
- matplotlibSkill
- model-recommendationSkill
- model-selections-and-cross-validationSkill
- networkxSkill
- numpy-scipy-statisticsSkill
- opencvSkill
- pandas-data-wranglingSkill
- plotlySkill
- polarsSkill
- powerbi-modellingSkill
- pymcSkill
- scikit-learn-pipelines-and-column-transformersSkill
- scikit-learnSkill
- seabornSkill
- The first 60 shown.
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Your trail
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