mcpField
A
analysis-documentation

analysis-documentation

github.com/nimrodfisherData & databases504220 installsUpdated 5w ago
Repository

About analysis-documentation

A skill for turning completed analyses into structured documentation for stakeholders, handoffs, review, or reuse. It covers business context, data, methods, findings, and the steps needed to reproduce the work.

Key features

  1. 01Audience and scope calibration
  2. 02Business context and decision framing
  3. 03Data source and methodology records
  4. 04Findings, recommendations, and reproducibility
  5. 05Structured document template

Use cases

  1. 01Preparing an analysis for stakeholder sharing
  2. 02Handing off work to another analyst
  3. 03Archiving recurring analyses for reuse
  4. 04Preparing for peer review or audit

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill analysis-documentation

After installing, type/analysis-documentationin Claude Code to confirm it loaded.

Bundled filesassets/analysis_doc_template.mdreferences/audience_depth_guide.md

Details

From the project README

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

Output

  • assets/analysis_doc_template.md — completed analysis document covering context, data, methodology, results, and reproducibility
  • Linked or embedded visualisations and code references

Comments

Markdown supported