Data Formulator is an interactive workspace where AI agents connect to data sources, answer questions, and create visualizations. It is used to explore and analyze data while branching into related questions without losing the context of each investigative path.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add microsoft/data-formulator --skill reportgit clone --depth 1 https://github.com/microsoft/data-formulatorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/microsoft/data-formulator/report)<a href="https://agentmods.dev/skills/microsoft/data-formulator/report"><img src="https://agentmods.dev/badge/skills/microsoft/data-formulator/report/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/microsoft/data-formulator/report"><img src="https://agentmods.dev/badge/skills/microsoft/data-formulator/report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01341 |
| Opus 5.5 | $0.00017 | $0.00536 |
| Sonnet 5.5 | $0.00008 | $0.00268 |
| Haiku 4.5 | $0.00004 | $0.00134 |
Grade A, and why
report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
Ground the report in the exploration
The thread context is your most important input. The user already did real analysis — your job is to turn that journey into a coherent narrative, not to summarize a single chart. Before writing:
- Read the FOCUSED THREAD and OTHER THREADS to understand the full set of questions asked and findings reached.
- Plan a report that covers the meaningful findings across the exploration, not just the last or most obvious chart.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 119 lines · 42 tokens per session scan A aa8622bbea08
report is a skill published in the GitHub repository microsoft/data-formulator (17,584 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 1,341 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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