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 similar-work-rankgit 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/similar-work-rank)<a href="https://agentmods.dev/skills/microsoft/cat-agent-skills/similar-work-rank"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/similar-work-rank/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/similar-work-rank"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/similar-work-rank.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 | $0.00068 | $0.00581 |
| Opus 5.5 | $0.00027 | $0.00232 |
| Sonnet 5.5 | $0.00014 | $0.00116 |
| Haiku 4.5 | $0.00007 | $0.00058 |
Grade A, and why
similar-work-rank 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 today.
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.
What it actually says
Similar-Work Rank
Purpose
Deterministically rank the asset's prior work orders by similarity to the current work order, detect a recurring SOP-default fix, and surface a superseding-fix recommendation from a prior fix note.
When to use
- After knowledge-retrieve, in every run.
- "What's the closest prior fix?" / "have we done this before?" / "is this a repeat failure?".
Inputs
- The contract payload with
issue_keywords[],required_parts[],retrieved.prior_fixes[]andhistory.prior_work_orders[](contractmfg.work-order-assist.v1).
Steps
- Validate the payload against the contract.
- Run
scripts/similar_work.py --intake wo-intake.json --out similar-work.json. - Do not restate or recompute any similarity score in prose; quote the engine output verbatim.
- Emit the
{similar_work}and{superseding_fix}contract payload plus the preliminarynext_best_action.
Output
Contract mfg.work-order-assist.v1 with populated similar_work[] (rank, wo_id, similarity_score,
shared_signals, outcome, recommended_fix, repeat_failure, confidence, source=engine:similar_work,
citation), superseding_fix{}, and history.repeat_promotion — the {similar_work} hop.
Grounding requirements
Every similar match carries its CMMS record citation; a recurring-failure promotion cites
references/similar-work-rules.md #3.1; a superseding fix cites its fix-note ID and #3.3.
Constraints
- All similarity math happens in the engine. The model must never re-rank prior work itself.
- No parts-readiness talk here — that is parts-readiness's job.
- Never suppress a recurring-failure or superseding-fix finding because "the SOP says to do X".
Escalation / uncertainty
- Top match below the 0.75 confidence floor, no comparable history, or a repeat failure detected:
the engine adds to
escalations[]; surface it and route a recurring failure to an RCA (similar-work-rules.md #4.1-#4.3).
What ships with it
7 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.
- today First seen · 51 lines · 68 tokens per session scan A fbfd482f3902
similar-work-rank is a skill published in the GitHub repository microsoft/cat-agent-skills (93 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 581 once invoked, about $0.0003 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 2026-10-28.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…