mlflow-onboarding
Skill0 starsChanged 5 months agoNo licenceView on GitHub
Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".
We split this skill into the rules it gives an agent
19 rules. About 0 are common, 13 are distinctive, and 6 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈13
Distinctive · estimate
This author's own choices. Worth a look.
6
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 an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 2
- mlflow/skills★ 8670% same
- MateusKasuya/databricks_claude_code★ 299% same
Wrote their own version · 0
None found.
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 repository50 other files it gives its agents.
- gstack/CLAUDE.mdCLAUDE.md
- ios-simulator-skill/CLAUDE.mdCLAUDE.md
- agent-bricksSkill
- agent-evaluationSkill
- aibi-dashboardsSkill
- analyze-langfuse-traceSkill
- analyzing-mlflow-sessionSkill
- analyzing-mlflow-traceSkill
- architecture-diagramSkill
- arxiv-digestSkill
- asset-bundlesSkill
- core-methodologySkill
- databricks-app-apxSkill
- databricks-app-pythonSkill
- databricks-configSkill
- databricks-docsSkill
- databricks-genieSkill
- databricks-jobsSkill
- databricks-python-sdkSkill
- databricks-unity-catalogSkill
- descriptive-statisticsSkill
- design-discoverySkill
- design-principlesSkill
- difference-in-differencesSkill
- effective-writing-llmsSkill
- gstackSkill
- instrumenting-with-mlflow-tracingSkill
- interface-craftSkill
- lakebase-provisionedSkill
- marimo-math-vizSkill
- marimo-notebookSkill
- model-servingSkill
- querying-mlflow-metricsSkill
- rct-core-designSkill
- rct-data-analysisSkill
- rct-ethics-registrationSkill
- rct-power-calculationsSkill
- rct-randomizationSkill
- regression-diagnosticsSkill
- retrieving-langfuse-tracesSkill
- retrieving-mlflow-tracesSkill
- searching-mlflow-docsSkill
- spark-declarative-pipelinesSkill
- svg-animationsSkill
- synthetic-data-generationSkill
- taste-driven-devSkill
- design-taste-frontendSkill
- todaySkill
- unstructured-pdf-generationSkill
- visualizationSkill
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Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.