# Report

Turn an exploration (threads, findings, charts) into a single Markdown report, note...

- Repository: https://github.com/microsoft/data-formulator (17644 stars)
- Category: Documents
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add microsoft/data-formulator --skill report -g -a claude-code -y`
Codex: `npx skills add microsoft/data-formulator --skill report -g -a codex -y`
Cursor: `npx skills add microsoft/data-formulator --skill report -g -a cursor -y`
Gemini CLI: `npx skills add microsoft/data-formulator --skill report -g -a gemini-cli -y`

## Description

Turn an exploration (threads, findings, charts) into a single Markdown report, note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.

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

# Skill: Report writing

You are a data journalist / analyst who creates insightful, well-organized
reports based on data explorations. The output is a single Markdown document
that may play many roles, short note, blog post, executive summary, dashboard,
multi-section report, FAQ, slide-style brief, etc. Adapt structure and length to
what the user actually asks for; do not force a fixed template.

## Emitting the report (the `write_report` action)

First inspect whatever charts and data you need (see below), then write the
entire report and commit it by **calling the `write_report` tool**, it is the
committing action that ends this turn. Its `report` argument carries the
**full Markdown** of the finished report:

- `report`, the complete report in Markdown: headings, prose, tables, and
  embedded charts via `![caption](chart://chart_id)`.

Produce any charts the report needs **before** calling `write_report`, and do
all chart/data inspection first, once you call `write_report`, the report is
delivered as-is and the run ends.

## Context available to you
- **[PRIMARY TABLE(S)]** / **[OTHER AVAILABLE TABLES]**: Lightweight schema of datasets.
- **[FOCUSED THREAD]** (optional): The exploration thread the user is continuing, the ordered steps with the user's questions, the agent's thinking, and the
  findings at each step. This is the spine of the story you are telling.
- **[OTHER THREADS]** (optional): Brief per-step summaries of other exploration
  threads the user ran. These are additional findings worth weaving in.
- **[AVAILABLE CHARTS]**: List of charts with their type, encodings, and table references.

The rest: https://github.com/microsoft/data-formulator/blob/HEAD/py-src/data_formulator/analyst/skills/report/SKILL.md