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@fcb0454

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

Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/analysis-documentation

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referencesaudience_depth_guide.md

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Audience Depth Guide for Analysis Documentation

Calibrating documentation depth

The same analysis can be documented at three different levels depending on who will read it. Over-documenting wastes time; under-documenting creates ambiguity that surfaces as repeated questions.


Level 1 — Executive / Stakeholder

Who reads this: VP, Director, non-technical business owner.

What they need:

  • The answer, not the method
  • Confidence that the analysis is trustworthy
  • What decision this enables, and what it does not

Format guidance:

  • Max 1 page (or equivalent)
  • Lead with the key finding, follow with supporting evidence
  • One chart maximum; no SQL, no technical terminology
  • Explicit recommendation or next step

Avoid:

  • Methodological detail ("we used a z-test at alpha=0.05")
  • Data caveats beyond the most critical one
  • Intermediate steps or alternative approaches considered

Level 2 — Analytical Peer / Manager

Who reads this: Senior analyst, analytics manager, product manager with data literacy.

What they need:

  • Methodology at a level that lets them evaluate whether it is sound
  • Key assumptions and their confidence level
  • How to reproduce or extend the analysis

Format guidance:

  • 2–4 pages
  • Context → Approach → Findings → Caveats → Next steps
  • Include the main SQL or code references (not the full script)
  • Explicit assumption log with confidence ratings

Avoid:

  • Full code dumps inline
  • Raw table dumps; use summarised outputs
  • Over-explaining standard techniques

Level 3 — Future Analyst / Handover

Who reads this: Someone who will extend, debug, or re-run this analysis in 6+ months.

What they need:

  • Complete reproducibility: where data lives, how to run the code, expected outputs
  • All design decisions and alternatives considered
  • Known issues and workarounds

Format guidance:

  • No page limit
  • Every query, script, and file path referenced
  • Step-by-step instructions to reproduce
  • Change log if the analysis is run repeatedly

Avoid:

  • Assuming context that is obvious now but won't be later
  • Skipping the "why" behind non-obvious decisions

Choosing the right level

Trigger Level
One-time ad-hoc for a meeting 1
Recurring report or dashboard 2
Strategic decision with board visibility 1 + summary of 2
Handover to another team 3
Reusable analysis template 3
Internal methodology reference 2–3

Reusable sections across levels

These sections appear in all levels, just at different depths:

  • Question — what business question this answers
  • Data sources — tables, systems, date ranges used
  • Key finding — the headline answer in one sentence
  • Caveats — what could be wrong or missing
  • Next steps — what action follows from this

Source: SKILL.md on GitHub

No alerts16d4 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    This skill provides a set of markdown templates and guidelines for documenting data analyses. It contains no executable scripts or security risks.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

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Last checked against GitHub 5 days ago.

Activeupdated 5 months ago

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