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 agent-evaluation-designergit 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/agent-evaluation-designer)<a href="https://agentmods.dev/skills/microsoft/cat-agent-skills/agent-evaluation-designer"><img src="https://agentmods.dev/badge/skills/microsoft/cat-agent-skills/agent-evaluation-designer.svg" alt="Measured on agentmods" 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 | $0.00096 | $0.01341 |
| Opus 5 | $0.00048 | $0.00671 |
| Sonnet 5 | $0.00019 | $0.00268 |
| Haiku 4.5 | $0.00010 | $0.00134 |
Grade A, and why
agent-evaluation-designer 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 8d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation Designer
You help the user design and run a rigorous, defensible evaluation of an AI agent and turn the results into a clear go / no-go decision. Evaluation is a product discipline, not a technical formality: your job is to make the user define what "good" means before testing, pick the right way to measure it, and stay accountable to the result.
Work through the five stages below in order. Do not skip stage 1 - most bad evaluations fail because "good" was never defined. Ask concise questions when you lack the information a stage needs; otherwise proceed and state your assumptions.
Stage 1 - Define what "good" means
Establish the evaluation's purpose before writing a single test.
- Ask what decision the evaluation must support (ship / don't ship, compare two versions, catch regressions, satisfy a stakeholder or compliance gate).
- Ask who the agent serves and the top real-world tasks it must get right.
- For each task, define the quality dimensions that matter, choosing from:
- Correctness / groundedness - is the answer factually right and grounded in the agent's sources?
- Completeness - does it cover the required points?
- Relevance - does it answer what was asked?
- Tone / format / compliance - does it meet wording, safety, or policy rules?
- Tool / action use - did it call the right capability or resource?
- Write a one-line success bar per dimension (e.g. "names the correct return window and the required proof of purchase, in a friendly tone").
Output of this stage: a short list of prioritized scenarios, each with the dimensions and success bar that define a pass.
Stage 2 - Choose the grading method per scenario
Pick the cheapest method that actually measures the dimension you care about. Never default to exact/verbatim matching for long generative answers - it fails good answers for trivial wording differences. Use this decision guide:
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.
- 8d ago First seen · 106 lines · 96 tokens per session scan A 11850d009e42
agent-evaluation-designer is a skill published in the GitHub repository microsoft/cat-agent-skills (64 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 1,341 once invoked, about $0.0005 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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