# Analysis Documentation

Structured, reproducible analysis documentation.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill analysis-documentation -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill analysis-documentation -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill analysis-documentation -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill analysis-documentation -g -a gemini-cli -y`

## Description

Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Analysis Documentation

# When to use
- Finalising an analysis before sharing it with stakeholders
- Handing off an analysis to another team member or to a future self
- Archiving recurring analyses so they can be run again consistently
- Preparing for peer review or a formal audit
- Converting an exploratory notebook into a reference document

# Process
1. **Confirm audience and scope**, determine whether the primary reader is technical (data team), business (stakeholders), or both. For mixed audiences, use a tiered structure. See `references/audience_depth_guide.md` for calibration.
2. **Write the business context section**, state the business question, the stakeholders who requested the analysis, the decisions it informs, and the success criteria.
3. **Document data sources**, for each source, record the table or file, date range, row count, key columns, and any known quality issues or exclusions applied.
4. **Write the methodology section**, describe the analytical approach, tools and library versions, key assumptions, and important decisions made (and alternatives considered). Reference the assumptions log if one exists.
5. **Record results**, include key metrics and statistics, embed or link visualisations with descriptive captions, and present findings in order of importance.
6. **Write the insights, recommendations, and reproducibility section**, connect each finding to a business implication and a next action. Document the steps required to reproduce the analysis (data access, environment, execution order). Use `assets/analysis_doc_template.md` as the structure.

# Inputs the skill needs
- Final code (SQL, Python, notebook) and outputs (charts, tables)
- Business question and stakeholder context
- Key findings and recommendations already identified
- Data source details (tables, date ranges, sample sizes)
- Library and tool versions used

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/02-documentation-knowledge/analysis-documentation/SKILL.md