data-science
Skill0 starsChanged 30 days agoNo licenceView on GitHub
Use for any ML/data science modeling task: training classifiers, regressors, or clustering; feature engineering and selection; model evaluation; experiment design and A/B testing; causal inference; forecasting; recommender systems; anomaly detection; optimization; AutoML; NLP pipelines; MLOps and deployment. Triggers: 'build a model', 'predict', 'classify', 'cluster', 'recommend', 'detect anomalies', 'forecast', 'train', 'evaluate', 'SHAP', 'XGBoost', 'LightGBM', 'TabPFN', 'causal forest', 'uplift', 'experiment design', 'hyperparameter tuning', 'deploy model', 'feature store', 'embeddings', 'topic modeling', 'sentiment', 'time series', 'Prophet', 'Chronos', 'fraud detection', 'fairness', 'model card'. Do NOT use for pure EDA without modeling (data-analysis skill), ETL (data-engineering skill), or data quality checks (data-quality skill).
7 other projects use a version of this.
Adopted by
Most recently taken up by
- dmoreq/agent-grimoiretheir own version, first seen Nov 8, 2026
- ludo-technologies/python-best-practicestheir own version, first seen Jul 18, 2026
- karim-bhalwani/agentic-harnesstheir own version, first seen Jul 12, 2026
- TheWatcher01/skillstheir own version, first seen Apr 1, 2026
- travisjneuman/.claudetheir own version, first seen Feb 25, 2026
- lubluniky/awesome-agent-blueprintstheir own version, first seen Jan 25, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 7
- travisjneuman/.claude★ 106same name
- ludo-technologies/python-best-practices★ 34same name
- justaddcoffee/open-science-skills★ 13same name
- lubluniky/awesome-agent-blueprints★ 1same name
- dmoreq/agent-grimoire★ 0same name
- karim-bhalwani/agentic-harness★ 0same name
- TheWatcher01/skills★ 0same 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 repository7 other files it gives its agents.
- ralph/CLAUDE.mdCLAUDE.md
- brainSkill
- data-analysisSkill
- data-designSkill
- data-engineeringSkill
- data-qualitySkill
- ralphSkill
The file
We split this skill into the rules it gives an agent
10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
10
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 5 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.
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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.