nimrodfisher/data-analytics-skillsGit

完成数据分析全流程

覆盖数据清洗到业务洞察交付的完整过程。

使用前,准备这些

输入素材:研究问题、来源范围与输出要求

  1. 在下方原始文档中确认支持的 AI 工具、模型和运行依赖。
  2. 打开原仓库,按作者的安装说明完成配置;模型 API 或外部服务可能需要单独开通。
  3. 用自己的素材试运行,再检查事实、版式与输出文件。
查看原仓库安装说明 ↗

许可证:未明确声明,请查看原仓库的使用条件。

来源快照:2026-09-15。README ↗

可以这样开始

使用这个 Skill 完成「完成数据分析全流程」:覆盖数据清洗到业务洞察交付的完整过程。 请先确认必要输入,再交付一份可直接使用的结果。

Data Analytics Skills for Claude & Codex

31 portable AI-powered skills that turn Claude or Codex into a hands-on analytics partner

No setup required · Works for any company or industry


     


What's in this repo?

A structured library of skills (reusable instruction sets) that Claude Code or Codex activates on demand to help with every stage of the analyst workflow: from data quality checks and deep-dive analysis, through documentation and dashboards, all the way to stakeholder communication. Skills follow the open Agent Skills standard (a plain SKILL.md format), so they also work with other compatible agents like Cursor and Windsurf.


🗺️ Skill Map

Open interactive version on Excalidraw


Why these skills are different

[!NOTE] Traditional AI assistants require extensive upfront configuration — schemas, metric definitions, business rules — before they're useful. These skills work on-demand.

Traditional approachThese skills
Needs prep before useZero setup required
Breaks when business rules changeAdapts naturally
Company-specific, hard to sharePortable across any org
Silent on assumptionsTeaches you what context matters

Each skill asks targeted questions to gather exactly what it needs, then executes a complete, structured workflow.


📚 Skill Categories

🔍 01  ·  Data Quality & Validation    3 skills

Foundation — start here whenever you're working with new data.

SkillWhat it does
programmatic-edaSystematic exploratory data analysis with automated sanity checks
data-quality-auditComprehensive quality assessment against business rules and schema
query-validationSQL review for correctness, performance, and edge cases
📝 02  ·  Documentation & Knowledge    7 skills

Build reusable context so you never explain the same thing twice.

SkillWhat it does
semantic-model-builderCreate a shared semantic layer for key metrics and dimensions
analysis-documentationDocument findings with reproducible methodology
data-catalog-entryStandardized metadata and descriptions for data assets
sql-to-business-logicTranslate complex SQL into plain business language
analysis-assumptions-logTrack every assumption and decision in an analysis
metric-reconciliationTrace and resolve discrepancies when metric sources disagree
schema-mapperColumn-level mapping between source and target schemas
📊 03  ·  Data Analysis & Investigation    7 skills

Core workflows for the analytical heavy lifting.

SkillWhat it does
cohort-analysisTime-based cohort tracking with retention curves
segmentation-analysisCustomer/user segmentation with actionable profiles
funnel-analysisConversion funnel with drop-off root-cause
time-series-analysisTrend detection, seasonality, and forecasting
root-cause-investigationStructured diagnosis of unexpected metric changes
ab-test-analysisRigorous experiment analysis with significance testing
business-metrics-calculatorStandard business metric calculation with benchmarks
🎨 04  ·  Data Storytelling & Visualization    5 skills

Turn raw findings into insights that drive decisions.

SkillWhat it does
insight-synthesisStructure analysis outputs into clear business insights
visualization-builderChart type selection, design guidance, and spec generation
executive-summary-generatorConcise executive-ready summaries of complex analysis
dashboard-specificationFull dashboard requirements with metrics and layout
data-narrative-builderCraft a compelling story arc from analytical findings
🤝 05  ·  Stakeholder Communication    5 skills

Bridge the gap between technical depth and business understanding.

SkillWhat it does
technical-to-business-translatorReframe technical findings for a business audience
stakeholder-requirements-gatheringStructured elicitation to clarify what stakeholders actually need
analysis-qa-checklistPre-delivery quality gate before sharing results
methodology-explainerExplain analysis approach to any audience level
impact-quantificationEstimate and frame the business impact of findings
⚙️ 06  ·  Workflow Optimization    4 skills

Work smarter across every project.

SkillWhat it does
analysis-planningStructure the approach before diving in
context-packagerPackage context efficiently for AI-assisted analysis
peer-review-templateStructured peer review checklist for analytical work
analysis-retrospectivePost-analysis learning and process improvement

📐 Also by the maintainer

Caliboard — visual plan review for Claude Code and Codex. See what your agent plans before it writes code: review exact steps, challenge decisions, and approve the revision that becomes code.


Version: 1.2.0  ·  Maintainer: Nimrod Fisher  ·  Last Updated: September 2026

🚀 Quick Start

Install

Each skill is a folder containing a SKILL.md (plus optional scripts/, references/, and assets/). Clone the repo and copy the skills you want into your agent's skills directory:

git clone https://github.com/nimrodfisher/data-analytics-skills.git

Claude Code: personal skills (all projects) go in ~/.claude/skills/, project skills in .claude/skills/ inside your project:

cp -r 01-data-quality-validation/programmatic-eda ~/.claude/skills/

Codex: user skills (all repos) go in ~/.agents/skills/, repo skills in .agents/skills/ at the repository root:

cp -r 01-data-quality-validation/programmatic-eda ~/.agents/skills/

New here? QUICKSTART.md walks you through your first skill in 5 minutes, and STRUCTURE.md explains how every skill is packaged.

[!TIP] Describe your task to Claude or Codex naturally — it will select and activate the right skill automatically. No slash commands needed.

Example:

You:    "I need to understand why our activation rate dropped 12% last week"
Agent:  [activates root-cause-investigation, asks for metric data and context]
You:    [provides data and business context]
Agent:  [runs structured investigation with hypothesis testing]

Which skill to start with?

You need to...Start here
Explore an unfamiliar datasetprogrammatic-eda → data-quality-audit
Write or review SQLquery-validation + schema-mapper
Understand a metric drop/spikeroot-cause-investigation
Analyze experiment resultsab-test-analysis
Build a dashboarddashboard-specification + visualization-builder
Present to leadershipexecutive-summary-generator + insight-synthesis
Document your methodologyanalysis-documentation + analysis-assumptions-log
Start a complex analysisanalysis-planning first, always

📖 How skills work

Each skill follows the same on-demand context pattern:

  1. Request minimum viable context — your agent asks only what's essential to start
  2. Execute the workflow — structured, step-by-step analytical process
  3. Surface assumptions — anything uncertain is flagged, not silently assumed
  4. Deliver a consistent output — templated result you can share or iterate on

[!NOTE] Skills degrade gracefully: if you can't provide everything, your agent states what it's assuming and proceeds.


🛠️ Customization

Skills work out-of-the-box. To make them company-specific, add a references/ folder inside any skill with:

skill-name/
├── SKILL.md
└── references/
    ├── company-schema.md       ← your table/column definitions
    ├── metric-definitions.md   ← standard metric formulas
    └── business-rules.md       ← thresholds, edge cases, etc.

Your agent will pull this context automatically when the skill runs.


🎓 Suggested ramp-up

Week 1 — Get comfortable

  • Run programmatic-eda on a familiar dataset
  • Practice providing context when your agent asks
  • Use analysis-planning at the start of your next project

Week 2–3 — Add your core toolkit

  • Set up semantic-model-builder for your key metrics (saves time forever)
  • Add query-validation to your SQL workflow
  • Pick 2 analysis skills that match your domain

Week 4+ — Go advanced

  • Chain 4–5 skills end-to-end on a full project
  • Add company-specific references to the skills you use most
  • Build team context documents for shared onboarding