microsoft/cat-agent-skills is a static website that catalogs reusable instruction sets and related packages for AI agents. People use it to search, filter, rate, and download skills for Cowork, Copilot Studio, and Scout, along with Copilot plugins and Scout automations. The catalogue entries are the skills, instructions, plugins, and settings displayed by the site.
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/cat-agent-skills --skill acroform-writergit clone --depth 1 https://github.com/microsoft/cat-agent-skillsWrote 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/cat-agent-skills/acroform-writer)<a href="https://agentmods.dev/skills/microsoft/cat-agent-skills/acroform-writer"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/acroform-writer/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/cat-agent-skills/acroform-writer"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/acroform-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 25 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 86 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00175 | $0.01436 |
| Opus 5 | $0.00088 | $0.00718 |
| Sonnet 5 | $0.00035 | $0.00287 |
| Haiku 4.5 | $0.00017 | $0.00144 |
Grade A, and why
acroform-writer 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 9d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fill an existing PDF's real AcroForm fields with supplied data, and optionally lock the result read-only so it's ready to send or file without further edits. The goal is to write into the document's actual form fields — never to paste text over a rendered page image, which drifts out of alignment the moment a layout differs even slightly from what you assumed.
Instructions
- Find the source PDF via knowledge search — do not ask the user to upload it. The blank fillable form (e.g. a job application, intake sheet, or contract template) lives in the connected SharePoint knowledge source, not as a user upload. Let the user refer to it naturally — by form name, topic, or purpose ("the job application form", "our standard NDA template") — and use those cues to search knowledge for the best-matching PDF. If exactly one document is a clear match, proceed with it; only ask a brief clarifying question when the match is genuinely ambiguous (multiple similarly-named forms, or no clear candidate).
- Pull the FULL document, not retrieved chunks. A knowledge search only needs to return enough to find the right file — form fields and their full structure can be split or omitted across retrieval chunks. Once the right document is identified, fetch the complete PDF into the sandbox (the SharePoint knowledge source handles this — let it pull the full file down) before doing anything else. Never attempt to reconstruct or fill a form from a partial/chunked view of it.
- Collect the values to fill. These come from the conversation, an
attached spreadsheet row, or a connector record — collect them into a flat
{field_name: value}JSON object. Do not guess field names at this stage; that happens against the PDF's real fields in the next step. - Always list fields first. Run:
This prints every real form field (name, type, current value, and — for checkboxes/radios/dropdowns — the valid states or options). Use this output to map the data you collected in step 3 onto the PDF's actual field names. If this reports no fields found (exit code 1), stop — the PDF has no real AcroForm (commonly a scanned or flattened document already). Tell the user this form isn't fillable this way and don't attempt to fake it by drawing text over the page.python scripts/fill_form.py list <downloaded_form.pdf> - Match data to fields carefully.
- Checkboxes/radio buttons must be set to one of the exact
statesvalues reported in step 4 (typically/Yesand/Off), nottrue/false. - Dropdown/choice fields must use one of the reported
optionsvalues verbatim. - If a value you were given doesn't obviously map to any listed field, do not force it into the closest-sounding one — leave it out and flag it to the user rather than guessing.
- Checkboxes/radio buttons must be set to one of the exact
- Fill (and lock read-only, if the result should be final):
Addpython scripts/fill_form.py fill <downloaded_form.pdf> <data.json> <output.pdf> [--flatten]--flattenwhenever the output is meant to be a finished, submission- ready document (the common case) so the fields are marked read-only and most viewers treat it as no longer editable. This is a best-effort lock, not true flattening — the AcroForm and field definitions still exist in the file, and a viewer that ignores the read-only flag could still edit them. Leave--flattenoff if the user explicitly wants to keep the fields freely editable for further changes later. - Check the JSON result the script prints. It reports
fields_filledand anyunmatched_fields— data keys that didn't correspond to a real field. Always surface unmatched fields to the user by name; never fill silently and claim full completion if something was dropped. - Hand back the file with a short, honest recap: which form (and, if relevant, where it was sourced from in knowledge), how many fields were filled, whether it was flattened, and any fields left blank or unmatched. Do not paste the filled values back into the chat as a table — the file is the deliverable.
What ships with it
4 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.
- 9d ago First seen · 104 lines · 175 tokens per session scan A f495ef74d92d
acroform-writer is a skill published in the GitHub repository microsoft/cat-agent-skills (66 stars, last pushed yesterday), licensed MIT. It adds 175 tokens to every session and 1,436 once invoked, about $0.0009 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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