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analysis-documentation

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Mis à jour31 mai 2026 à 05:25

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

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Source

ryudkiss-hue

ryudkiss-hue/nyc_data

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Scientifiques des donnéesProfessions informatiques et mathématiques·SOC 15-2051

SKILL.md

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

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

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.
Write the business context section — state the business question, the stakeholders who requested the analysis, the decisions it informs, and the success criteria.
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.
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.
Record results — include key metrics and statistics, embed or link visualisations with descriptive captions, and present findings in order of importance.
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

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Process

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.

Write the business context section — state the business question, the stakeholders who requested the analysis, the decisions it informs, and the success criteria.

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.

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.

Record results — include key metrics and statistics, embed or link visualisations with descriptive captions, and present findings in order of importance.

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.