# Analysis Reporting

> Use this when summarizing analysis results, writing reports, documenting experiment outcomes, or presenting model evaluation — including structuring findings in Japanese with conclusions, facts, assumptions, interpretations, and caveats.

- Skill: `gabrielmoreira/analysis-reporting` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/analysis-reporting`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/analysis-reporting/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/analysis-reporting

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# Skill: Analysis Reporting

Use this skill when summarizing analysis results, experiment outcomes, or model evaluation.

## Language

- Reports should be written in **Japanese** unless otherwise requested.

## Structure

1. **結論** — Start with the conclusion or key finding.
2. **事実** — Present objective facts from the data.
3. **仮定** — State assumptions made during analysis.
4. **解釈** — Provide interpretations and implications.
5. **制約・注意点** — Mention limitations, caveats, and possible bias.

## Required Context

Include the following when relevant:

- **データ期間**: Date range of the analysis.
- **フィルタ条件**: Filters applied to the data.
- **サンプルサイズ**: Number of records or observations.
- **指標定義**: How key metrics are calculated.

## Reproducibility Notes

Every report should include or reference:

- **入力データパス**: Path to input data used.
- **クエリ/スクリプトパス**: Path to the query, notebook, or script that produced the results.
- **出力パス**: Path to output artifacts (figures, tables, reports).
- **実行コマンド**: Command used to generate results.

## Tone

- Use **business-friendly language** for summaries intended for stakeholders.
- Keep **technical details** available for reviewers (in appendix or linked notebook).
- Avoid jargon when simpler terms suffice.
- Be precise about what the data shows vs. what is inferred.