Borrowing it
Nothing to install: this file belongs to pnp/copilot-pro-dev-samples. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pnp/copilot-pro-dev-samples/main/.github/skills/copilot-studio-sample-review/SKILL.mdgit clone --depth 1 https://github.com/pnp/copilot-pro-dev-samplesWrote 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/pnp/copilot-pro-dev-samples/copilot-studio-sample-review)<a href="https://agentmods.dev/skills/pnp/copilot-pro-dev-samples/copilot-studio-sample-review"><img src="https://agentmods.dev/badge/skills/pnp/copilot-pro-dev-samples/copilot-studio-sample-review/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/pnp/copilot-pro-dev-samples/copilot-studio-sample-review"><img src="https://agentmods.dev/badge/skills/pnp/copilot-pro-dev-samples/copilot-studio-sample-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00033 | $0.00643 |
| Opus 5.5 | $0.00013 | $0.00257 |
| Sonnet 5.5 | $0.00007 | $0.00129 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
copilot-studio-sample-review 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 2mo 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Copilot Studio Sample Review
Review Copilot Studio sample PRs (folders starting with mcs-) for compliance with repo guidelines.
Use when a request asks to review an mcs-* sample PR (e.g., "review PR" or "review this sample").
Workflow
- Checkout PR:
gh pr checkout <number> - Verify sample folder starts with
mcs- - Run checks below, collecting comments (file, line, issue)
- Submit all comments in single API call (GitHub limitation)
Checks
1. Folder and file structure
- Sample is under
samples/mcs-* - Required top-level files/folders are present:
README.md,assets/,src/ assets/sample.jsonexists and includes correct sample folder references
2. No packaged exports
- Request changes if any packaged
.zipexports are committed undersamples/mcs-*(e.g.,samples/mcs-example/export.zip). Extracted source files are required, whether the contributor usedpac solution cloneor the VS Code clone method. - Copilot Studio exports must be committed as extracted source files only
3. README and template alignment
- README is based on
/templates/mcs-copilot-studio/README.md - Setup steps include one of the supported contributor flows:
- Solution export via Power Platform CLI (
pac solution clone) - Copilot Studio Visual Studio Code clone method
- Solution export via Power Platform CLI (
4. Metadata consistency
assets/sample.jsonnameusespnp-copilot-pro-dev-<sample-folder>urlanddownloadUrltarget the correctsamples/mcs-*path- Author information and thumbnail paths match repository conventions
Submitting Reviews
GitHub API does not support adding comments to a review after it has been created. Collect all comments first and submit them in one API call.
Get commit SHA:
gh pr view <PR> --json headRefOid
Create review.json:
{
"commit_id": "<sha>",
"event": "<REQUEST_CHANGES|COMMENT|APPROVE>",
"body": "Summary message",
"comments": [
{"path": "file/path", "line": 10, "body": "Issue description"}
]
}
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.
- 2mo ago First seen · 75 lines · 33 tokens per session scan A 3b017417098c
copilot-studio-sample-review is a skill published in the GitHub repository pnp/copilot-pro-dev-samples (169 stars, last pushed 24d ago), licensed MIT. It adds 33 tokens to every session and 643 once invoked, about $0.0001 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 2026-08-30.
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github-review-pr
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github-review-pr
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code-simplifier
Simplify existing code for clarity and maintainability without changing behavior. Use when asked to clean up or simplify code, especially recent changes.
code-review
Review pull requests for repository fit, meaningful value, trustworthy provenance, differentiation, and maintainability in awesome-copilot.
sage-review
Deep, platform-neutral code review for PRs and CRs in ONE thorough single pass — design reasoning (Problem Worth Solving & Solution Fit) as one dimension alongside the 9 code-level dimensions, with chain-of-consequences, self-critique, and draft-only comments. The single app-owned source of truth.
learn-from-sage
Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.