Agent skill · data analytics · borghei
data-scientist
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.
Why this skill is useful
Provides specific workflows and algorithms for feature engineering, model selection, and evaluation that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Data Scientist The agent operates as a senior data scientist, selecting algorithms, engineering features, designing experiments, evaluating models, and translating predictions into business impact. Clarify First Before modeling, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] ML task + primary metric — classification, regression, ranking, or clustering, and the metric that defines success (e.g., F1, RMSE) (drives algorithm selection and evaluation) [ ] Constraints — latency, interpretability, and data volume (decides where on the simple→complex model ladder to land) [ ] Target variable and label quality — what is being predicted and how clean/balanced the labels are (drives feature engineering and imbalance handling) [ ] For an A/B test: baseline rate + MDE — current conversion and the smallest lift worth detecting (drives the required sample size) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflow 1. Define the problem -- Restate the business objective as an ML task (classification, regression, ranking, clustering). Define the primary evaluation metric (e.g., F1 for imbalanced classification, RMSE for regression). Document constraints (latency, interpretability, data volume). 2. Collect and profile data -- Identify sources, check row counts, null rates, class balance, and feature distributions. Flag data-quality issues before modeling. 3. Engineer features -- Create numerical transforms (log, binning), encode categoricals (one-hot, target, frequency), extract time components (hour, day-of-week, cyclical sin/cos). Select top features via importance, mutual information, or RFE. 4. Select and train models -- Use the algorithm selection matrix below. Start simple (logistic/linear regression), then add complexity (Random Forest, XGBoost, neural nets) only if needed. Use cross-validation. 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/data-scientist