Canonical Copilot Collections is a repository for organizing and distributing GitHub Copilot instructions, prompts, agents, and skills across Canonical repositories. Teams configure repositories to subscribe to shared collections, such as Python, documentation, or Juju development guidance, and keep those assets synchronized.
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 canonical/copilot-collections --skill documentation-reviewgit clone --depth 1 https://github.com/canonical/copilot-collectionsWrote 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/canonical/copilot-collections/documentation-review)<a href="https://agentmods.dev/skills/canonical/copilot-collections/documentation-review"><img src="https://agentmods.dev/badge/skills/canonical/copilot-collections/documentation-review.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 | $0.00037 | $0.01549 |
| Opus 5 | $0.00018 | $0.00775 |
| Sonnet 5 | $0.00007 | $0.00310 |
| Haiku 4.5 | $0.00004 | $0.00155 |
Grade A, and why
documentation-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 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Review
Scope
Orchestration only: defines the end-to-end review workflow,
specifies the order in which atomic skills are invoked,
and renders the final consolidated report using the report template
at references/doc-review-report-template.md.
Persona
You are a technical documentation reviewer and editor for the project. Your job is to ensure the documentation is clear, accurate, consistent with code, and follows the project's style guide. You apply the Diataxis framework (Tutorial, How-to, Explanation, Reference) rigorously.
Workflow
Follow these stages sequentially. Do not skip stages.
Execution Requirements
CRITICAL: After completing each stage, you MUST:
- Confirm the skill was actually invoked (not just described)
- Capture the output and record findings
- State the completion status explicitly
Verification Pattern: After each stage, state:
✓ Stage [N] complete: [skill-name] generated [N] findings- If no findings:
✓ Stage [N] complete: [skill-name] found no issues
Do NOT proceed to Stage [N+1] until Stage [N] is verified complete.
Stage 1: Build Validation
Execute: Use the documentation-build skill to validate the documentation build.
Capture: Record all build errors and warnings.
Verify: Confirm build status (pass/fail) before proceeding.
Decision Point: If the build fails, report build issues immediately and STOP. Do not proceed to content analysis until the documentation builds without errors.
Checkpoint: ✓ Stage 1 complete: documentation-build [passed/failed with N errors]
Stage 2: Documentation Structure Discovery
Execute: Map the documentation structure before analyzing content.
Actions:
- List all documentation files under
docs/(or equivalent) - Identify the documentation build system (Sphinx, MkDocs, Jekyll, etc.)
- Note the directory structure (flat vs. categorized)
- Record any metadata patterns (frontmatter, sidebar configs)
What ships with it
1 file 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 · 199 lines · 37 tokens per session scan A 1f027f2c5ef3
documentation-review is a skill published in the GitHub repository canonical/copilot-collections (29 stars, last pushed 5d ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,549 once invoked, about $0.0002 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.
Other skills, from other repositories
incident-postmortem-report
Produce a thorough incident post-mortem report after an outage or customer-impacting event. Covers executive summary, impact, detailed timeline, root cause, contributing factors, corrective and preventive actions, and lessons learned. Use when the user asks to write, draft, or complete a post-mortem, blameless review…
documentation-search
Search the internal knowledge base for runbooks, architecture documentation, ADRs, best practices, and troubleshooting guides using RAG. Use when looking for internal documentation, deployment procedures, architecture decisions, or operational runbooks.
review-specific-pr
Perform a comprehensive code review of a specific GitHub Pull Request. Analyzes code changes, checks for bugs, security issues, test coverage, and coding standards compliance. Use when a user provides a PR URL or asks to review a specific pull request.
incident-investigation
Correlate PagerDuty incidents with Jira tickets and recent ArgoCD deployments to accelerate root cause analysis. Orchestrates multiple agents to build a timeline of events. Use when investigating active incidents, performing post-mortems, or correlating alerts with changes.
aws-cost-analysis
Analyze AWS costs by service, account, and time period. Identifies top spenders, cost anomalies, and optimization opportunities. Use when reviewing cloud spend, preparing cost reports, or investigating unexpected charges.
check-deployment-status
Check the health and sync status of all ArgoCD applications across clusters. Identifies out-of-sync, degraded, or unhealthy deployments and provides actionable remediation steps. Use when monitoring deployments, troubleshooting sync failures, or verifying environment health after a release.