Individual skill
eda-playbook
Runs a structured exploratory data analysis on a new or suspect dataset - schema audit, target analysis, feature profiling, missingness patterns, and leakage checks - ending in a written decision log. Use when someone says "I just got this dataset, where do I start", "my model metrics look too good", "audit this data before we model it", or is debugging unexpected model behavior. Do NOT use for writing the transformation code itself - use pandas-expert instead; for ongoing production data monitoring use data-quality; for constructing model features after EDA use ml-feature-engineering; for answering a one-off business question from a database use sql-to-insights.
skills/eda-playbook/SKILL.md
Source commit 97a13607d669157a4a16d5ba79b77f208319f7b9
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 24
- Repository last updated
- Oct 13, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- claude Repository topic
- claude-skills Repository topic
- skill-me Repository topic
- data Derived category
- engineering Derived category
- writing Derived category
- security Derived category
- testing Derived category
- data-databases Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# EDA Playbook
Skipping EDA before modeling is the single most common source of silent failures in ML pipelines: leakage that makes offline metrics fictional, structural missingness treated as random, a target definition nobody actually agreed on. This playbook is a fixed-order audit that surfaces those problems while they cost hours instead of quarters - and its output is a decision log, not a pile of plots.
## Operating procedure
Run the sections in order: schema problems invalidate the target analysis, and the target definition must be pinned before any feature-target relationship means anything.
### Step 1: gather inputs
- The dataset, its supposed grain (one row = ?), and the expected row count. If the provider cannot state either, derive them and label the result a guess.
- The target column and its **written definition** - precisely what event, over what window, measured when. Ambiguity here cascades everywhere; do not proceed on a verbal shrug.
- What information will exist **at prediction time**. This single fact powers every leakage check later.
- Any known collection changes (new form fields, pipeline migrations) that could create structural breaks.