Decision-Grade Data Science

Open-source AI agent skills for reliable data analysis, machine learning, and AI modeling — from ambiguous questions and messy data to validated, reproducible decisions.

한국어 · Methodology · Evaluation · Contributing

From messy data to defensible decisions — see the evidence-first methodology.

Seven skills — a router, a lifecycle orchestrator, and five specialists — that teach coding agents to preserve the chain from a real decision to the evidence that supports it. Built for work where a polished notebook or a high validation score is not enough.

Why this exists

AI agents can write analysis code quickly. They can also silently choose the wrong row grain, treat a convenient field as ground truth, leak future information, move an evaluation target after seeing results, or declare completion after one successful run.

This suite adds a human-controlled operating discipline:

decision contract
  → data and ground-truth audit
  → leakage-safe baseline
  → controlled experiments
  → independent validation
  → failure diagnosis
  → clean-room reproduction and handoff

The goal is not to slow work down. It is to make speed come from clear decisions, reusable evidence, and falsifiable checks instead of skipped validation.

Skills

One router picks the right entry, one orchestrator runs the full lifecycle, and five specialists own its stages:

using-data-analysis  (router — start here when the right skill is unclear)
  └─ running-decision-grade-data-science  (orchestrates the lifecycle)
       1. auditing-data-and-ground-truth      — audit
       2. designing-leakage-safe-experiments  — experiment design
       3. validating-models-and-claims        — validation
       4. diagnosing-ml-failures              — diagnosis
       5. shipping-reproducible-results       — reproducible handoff

Skill	Use it for
Using Data Analysis Skills — entry point	Route any data analysis or ML request to the narrowest matching skill
Run Decision-Grade Data Science	Orchestrate an ambiguous or end-to-end data science project
Audit Data and Ground Truth	Verify grain, joins, time semantics, missingness, labels, and source-of-truth reliability
Design Leakage-Safe Experiments	Lock prediction timing, splits, baselines, metrics, and fair comparisons
Validate Models and Claims	Determine exactly which model or analytical claims the evidence supports
Diagnose ML Failures	Isolate regressions across data, labels, pipelines, artifacts, metrics, and runtime
Ship Reproducible Results	Package provenance, reproduction status, limitations, and handoff ownership

Each skill includes focused instructions, deeper reference material, and a reusable output template. Use the orchestrator for a full project and the narrower skills for isolated goals; the router selects for you when the match is not obvious.

Install

Inspect any third-party skill before installation.

GitHub CLI