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 knowledge-matchgit 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/knowledge-match)<a href="https://agentmods.dev/skills/microsoft/cat-agent-skills/knowledge-match"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/knowledge-match/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/knowledge-match"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/knowledge-match.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00162 | $0.02173 |
| Opus 5.5 | $0.00065 | $0.00869 |
| Sonnet 5.5 | $0.00032 | $0.00435 |
| Haiku 4.5 | $0.00016 | $0.00217 |
Grade A, and why
knowledge-match 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Match
Purpose
Read only knowledge_record and released_assets from Closeout output (knowledge-reuse-rules.md #1.3), keep release candidates, list governed lessons as excluded until Closeout supplies written lesson text (#2.4), and score relevance deterministically.
When to use
After engagement-retrieve, when Closeout governed.json is available.
When NOT to Use
- Closeout harvesting, sanitisation or release decisions - use
engagement-closeout-knowledge-harvest. - Whether this joiner may access an item - use
wall-check; matched items are candidates until it runs. - Drafting the digest prose - use
pack-draft.
Inputs
scope.json and Scenario 01 governed.json (ps.engagement-closeout-knowledge-harvest.v1), plus the raw directory, staffing input and confirmed roster engagement-retrieve read: the engine re-runs scope_digest on them and refuses a scope.json that differs from the re-derivation in any field (confidentiality-and-ethical-wall-rules.md #1.4).
Steps
- Run
python3 scripts/knowledge_match.py --scope scope.json --raw-dir <raw> --staffing <staffing.json> --confirmed-roster <raw>/confirmed-roster.csv --closeout <closeout governed.json> --wall-register <raw>/ethical-wall-register.csv --out matched.json --generated-at <YYYY-MM-DDTHH:MM:SSZ>. - If the engine exits non-zero (wrong contract, missing, null or malformed
knowledge_record,released_assets,knowledge_record.reusable_assetsorknowledge_record.lessons(an absent list is never read as empty), wrong field types such as a stringconfidenceor non-booleanreview_required, a scope.json that fails its stage schema, carries hold evidence without its hold, or is not the documentscope_digestre-derives from the raw inputs), stop and report (see Failure handling;knowledge-reuse-rules.md #1.4). - Report included items with
relevance_score,review_statusand citations;pending_practice_reviewitems may be referenced by title and citation only, and their summary is always the engine's fixed title-and-citation-only statement, never the Closeout description (knowledge-reuse-rules.md #2.3). An asset excluded asdescription_is_stage_guidance_not_written_contenthas no written description: its Closeout text is guidance to a drafter, so never quote, paraphrase or write a summary from it (knowledge-reuse-rules.md #2.6). - Report
excluded_itemsby ID and reason; never describe held or candidate assets. A lesson excluded aslesson_text_not_written_in_governed_closeout_recordhas no written lesson yet: say so with its citation, and never quote, paraphrase or write a lesson from Closeout'sdrafting_guidance, which is an instruction to the Closeout drafter, not lesson content (knowledge-reuse-rules.md #2.4). - Hand
matched.jsontowall-check.
What ships with it
10 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.
- contracts/ps.engagement-mobilization-onboarding-pack.v1.json 28 KB
- references/confidentiality-and-ethical-wall-rules.md 46 KB
- references/knowledge-reuse-rules.md 13 KB
- references/role-access-matrix.md 4.6 KB
- references/workspace-inventory-rules.md 4.1 KB
- scripts/contract_validate.py 8.9 KB runs code
- scripts/knowledge_match.py 37 KB runs code
- scripts/safety_text.py 125 KB runs code
- scripts/scope_digest.py 42 KB runs code
- scripts/steering_guard.py 187 KB runs code
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 · 62 lines · 162 tokens per session scan A f6c04cf1c6d8
knowledge-match is a skill published in the GitHub repository microsoft/cat-agent-skills (101 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 2,173 once invoked, about $0.0006 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 2026-11-07.
knowledge-match compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| knowledge-matchmicrosoft/cat-agent-skills | Skill | 101 | yesterday | MIT | A | 162 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |