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/declarative-agent-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/declarative-agent-sample-review)<a href="https://agentmods.dev/skills/pnp/copilot-pro-dev-samples/declarative-agent-sample-review"><img src="https://agentmods.dev/badge/skills/pnp/copilot-pro-dev-samples/declarative-agent-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/declarative-agent-sample-review"><img src="https://agentmods.dev/badge/skills/pnp/copilot-pro-dev-samples/declarative-agent-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 | $0.00069 | $0.00716 |
| Opus 5.5 | $0.00028 | $0.00286 |
| Sonnet 5.5 | $0.00014 | $0.00143 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
declarative-agent-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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Declarative Agent Sample Review
Review declarative agent sample PRs (folders starting with da-) for compliance with repo guidelines.
Workflow
- Checkout PR:
gh pr checkout <number> - Verify sample folder starts with
da- - Run checks below, collecting comments (file, line, issue)
- Submit all comments in single API call (GitHub limitation)
Checks
1. Manifest Schema Versions
Fetch latest versions from Microsoft Learn docs (never use other samples as reference):
| Manifest | File | Property | Docs |
|---|---|---|---|
| App | appPackage/manifest.json |
manifestVersion |
learn.microsoft.com/microsoft-365/extensibility/schema |
| Declarative Agent | appPackage/*DeclarativeAgent.json |
version + $schema |
learn.microsoft.com/microsoft-365-copilot/extensibility/declarative-agent-manifest |
| API Plugin | appPackage/ai-plugin.json |
schema_version |
learn.microsoft.com/microsoft-365-copilot/extensibility/api-plugin-manifest |
2. m365agents.yml
- No
projectId- auto-generated on provision, must not be committed - Check both
m365agents.ymlandm365agents.local.yml
3. README.md
Compare against templates/da-declarative-agent/README.md:
- Contributors section present with actual author names
- Version history lists real author (not "Microsoft")
- Tracking image uses markdown format at end of file:
 - URLs consistent (avoid mixed
en-us/locales in same file) - No trailing
---at end
4. assets/sample.json
Compare against templates/sample-template.json:
- name:
pnp-copilot-pro-dev-<sample-folder> - url/downloadUrl: correct sample folder path
- authors: matches actual contributors (verify against PR author)
- URLs: consistent locale format
Submitting Reviews
GitHub API cannot add comments to pending reviews after creation. Collect all comments first.
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 · 89 lines · 69 tokens per session scan A 3a93cccd28bd
declarative-agent-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 69 tokens to every session and 716 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 2026-08-30.
Other skills, from other repositories
github-review-pr
Review GitHub pull requests with detailed, multi-perspective code analysis using parallel subagents. Use this skill whenever the user wants to review a PR, asks for code review on a pull request, mentions "review PR", "check this PR", "look at pull request", or references a PR number or GitHub PR URL. Do NOT use for…
github-review-pr
Review a GitHub pull request by number or URL for evidence-backed defects. Not for reviewing local uncommitted changes.
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.