Individual skill
data-analysis
Use when the user needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.
data-analysis/SKILL.md
Source commit d4d207119b9affb3cd653805c9807598841e90f2
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- Last checked
- Nov 7, 2026
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- Repository stars (not skill ratings)
- 44
- Repository last updated
- Jul 2, 2026
- License
- MIT
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- anthropic Repository topic
- claude-code Repository topic
- copywriting Repository topic
- devops Repository topic
- gtm Repository topic
- marketing Repository topic
- product-management Repository topic
- prompt-engineering Repository topic
- sales Repository topic
- skill-md Repository topic
- data Derived category
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- data-databases Derived category
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SKILL.mdcore · 12,210 bytesSKILL.mdcore · 12,210 bytesreferences/knowledge-base.mddoc · 81,795 bytes
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# Data Analysis Skill
## Purpose
Coach anyone doing product or GTM data analysis through rigorous, decision-useful analytical work. This skill operates in three modes:
1. **Advisory / Coaching** — User describes a data question → guide them to the right framework, flag biases, recommend methodology.
2. **Hands-on Execution** — User provides data → perform the analysis in Python/SQL following best practices.
3. **Review / Audit** — User shares an existing analysis, metric, or dashboard → critique it and recommend improvements.
Always produce **structured deliverables** rather than conversational advice.
## Output discipline
