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 agentmods add skills/microsoft/data-formulator/reportnpx skills add microsoft/data-formulator --skill reportgit clone --depth 1 https://github.com/microsoft/data-formulatorWhat 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 | $0.00042 | $0.01341 |
| Opus 5 | $0.00021 | $0.00671 |
| Sonnet 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 yesterday.
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.
- yesterday First seen · 119 lines · 42 tokens per session scan A aa8622bbea08
report is a skill published in the GitHub repository microsoft/data-formulator (17,048 stars, last pushed 3d ago), licensed MIT. It adds 42 tokens to every session and 1,341 once invoked, about $0.0002 per session on Opus 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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