StamKavid

StamKavid/last-ds-mile

Hook

Runs after a tool call finishes for Read, Edit, Write, MultiEdit and NotebookEdit tool calls, executing ds-python.sh via bash (2 commands). From StamKavid/last-ds-mile.

StamKavid/last-ds-mile

Hook

Runs before the context is compacted, executing ds-python.sh via bash. From StamKavid/last-ds-mile.

StamKavid/last-ds-mile

Hook

Runs when the agent finishes a response, executing ds-python.sh via bash. From StamKavid/last-ds-mile.

StamKavid/last-ds-mile

Skill Claude CodeCodex

Records a real failure-and-fix pair as a project-local lesson, with the specifics that make it recognisable next time. Use when a bug, leakage mistake, or validation error was found and corrected and should not recur. Use when someone asks to save what was learned from an experiment that went wrong.

StamKavid/last-ds-mile

Skill Claude CodeCodex

Separates what a model found from what would happen if a feature were changed, and catches association reported as cause. Use when someone asks whether a feature causes an outcome, or whether changing it would change the result. Use when a write-up says something drives, causes, or leads to the target. Use when a…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Carries a tabular machine-learning request from a plain-language ask all the way to a scored model and an honest verdict, in one turn. Use when the user says build a model, train a classifier, classify or predict or forecast a column, or detect something in a CSV, table, or spreadsheet. Use when someone wants to look…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Chooses the right chart and library for the audience and keeps it honest — no truncated axes, no misleading aggregation. Use when someone asks which chart or plot type to use, or whether to reach for matplotlib, Plotly, or Altair. Use when a figure looks misleading or a chart choice seems arbitrary.

StamKavid/last-ds-mile

Skill Claude CodeCodex

Decides when pandas is fine and when to reach for Polars, plus vectorisation, dtype, and memory technique either way. Use when an operation such as a groupby, join, or apply is slow, or a script takes far longer than it should. Use when a dataset no longer fits in memory. Use when someone asks about Polars…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Checks whether training data still resembles the data a model will actually see, via adversarial validation and per-feature drift comparison. Use when a cross-validation score was strong but real or held-out performance fell apart. Use when someone asks whether training data still looks like production. Use when…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Scores a deliberately dumb baseline — majority class, mean, median, or a one-rule heuristic — so there is a number the real model has to beat. Use when someone asks whether a score is actually good, what to compare a model against, or whether a result beats guessing. Use before training or tuning any model, whenever…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Rewrites a technical write-up as a one-page brief for a non-technical reader — no metric names, framed in money, percentages, and counts. Use when someone asks to explain results to an executive, a business audience, or anyone who does not know what AUC means. Use when a plainer version is wanted for business…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Profiles a dataset before any modelling — schema, columns, row count, dtypes, missing values, duplicates, integrity checks, provenance, and a data dictionary. Use when someone asks what columns a file or table has, how many rows it contains, or what is inside a dataset they were just handed. Use when someone asks to…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Stands a packaged model up as a callable endpoint with prediction logging against the live baseline, drift detection, and a rollback pointer. Hard gate — refuses full traffic without all three, and stops before any remote or cloud push. Use when someone asks to serve, deploy, or stand up a model endpoint. Use when…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Measures how a model really performs — the metric at a stated operating point, a confusion matrix, calibration, and performance broken out by slice rather than one aggregate number. Use when someone asks how well a model does on held-out data, wants precision and recall at a threshold, or wants a confusion matrix. Use…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Interprets a trained model with permutation importance and SHAP to check it learned something sensible rather than an artifact. Use when someone asks which features a trained model is actually using or relying on, what drives its predictions, or why it decided something. Use when feature importance or SHAP values are…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Runs exploratory data analysis — distributions, relationships between each feature and the target, correlations — with a running hypothesis log. Use when someone asks for EDA, wants to explore or visualise a dataset, or wants to plot how features relate to what is being predicted. Use after a dataset has been profiled…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Turns a vague data request into a precise problem — unit of analysis, target definition, the decision it feeds, and what success means. Use when someone wants to predict something but has not said exactly what, or asks what the target variable should be. Use when a model is requested with no success criterion, no…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Packages a project so someone else can rerun it — pinned environment, model card, and a rerunnable artifact. Hard gate — refuses to proceed without exact pinned versions. Use when someone asks to make work reproducible, pin dependencies, or write a model card. Use when a project is being handed to another person or…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Reads the evaluation's findings, names what is actually wrong — bias, variance, a weak slice, suspected leakage, a data problem — and routes back to the stage that fixes it. Use when a result is not good enough and someone asks what to try next. Use when deciding whether to go back and change features, change the…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Holds the shared discipline every stage of this pipeline cites — the Red Flags, the Common Rationalizations, and the Hard Gates. Use when someone pushes back on a gate, asks why a baseline is needed, or wants to peek at the test set just once. Use when deciding whether a stage should stop and ask or produce the…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Trains, tunes, and compares candidate models against the chosen validation scheme and the baseline number. Use when someone asks to train models, tune hyperparameters, or compare algorithms such as logistic regression, random forest, XGBoost, or LightGBM. Use when picking which of several trained candidates to carry…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Wraps a handed-off model as a servable unit — inference contract, thin predict wrapper, reproducible Dockerfile — and proves it returns the same predictions it produced offline. Hard gate on that parity check. Use when someone asks to turn a model into a service, build a container or Docker image for it, or define its…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Cleans data and builds features with leakage prevention as the organising principle — every transform fit on training rows only, wrapped in a pipeline. Use when someone asks to clean data, encode categorical variables, scale numerics, or handle missing values. Use when engineering or transforming features ahead of…

StamKavid/last-ds-mile

Skill Claude CodeCodex

Turns evaluation and interpretation into a stakeholder-facing narrative — recommendation, assumptions, and limitations stated plainly. Use when someone asks to write up, summarise, or present results. Use when preparing findings for a product team, a manager, or any audience that will act on them.