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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/canonical/copilot-collectionsnpx agentmods add skills/canonical/copilot-collections/generate-path-instructionsWrote 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/generate-path-instructions)<a href="https://agentmods.dev/skills/canonical/copilot-collections/generate-path-instructions"><img src="https://agentmods.dev/badge/skills/canonical/copilot-collections/generate-path-instructions.svg" alt="Measured on agentmods" 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 | $0.00089 | $0.01771 |
| Opus 5 | $0.00044 | $0.00886 |
| Sonnet 5 | $0.00018 | $0.00354 |
| Haiku 4.5 | $0.00009 | $0.00177 |
Grade A, and why
generate-path-instructions 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 7d 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Path-Specific Instructions Generator
Overview
This skill generates scoped .github/instructions/*.md files that load Just-In-Time (JIT) when specific files are opened. It autonomously analyzes your repository to discover file patterns, extract scope-specific rules from existing code and documentation, and generate precise glob patterns for targeted instruction loading.
Key advantages over global instructions:
- Context Economics: Load rules only when needed (vs always-on global instructions)
- Higher Priority: Path instructions override global instructions
- Precision: Target specific frameworks, file types, or directories
Key principle: Leverage LLM strengths for pattern discovery and rule extraction rather than manual specification.
Workflow
Step 1: Intent Validation
Confirm the user wants path-specific instructions (not global).
Decision tree:
- User wants scoped/directory/framework-specific rules? → Continue to Step 2
- User wants global repository instructions?
- → STOP. Redirect them to use
generate-repo-instructionsskill instead - Explain: Global instructions go in
.github/copilot-instructions.md
- → STOP. Redirect them to use
- User wants a custom agent or skill?
- → STOP. Clarify asset type using Asset Decision Matrix
- Agent = role-based, Skill = capability-based, Instructions = rules
Proceed only if creating path-specific instructions.
Step 2: Scope Discovery
Goal: Understand what files/directories to target and what patterns exist.
Load the scope analysis checklist:
cat references/scope_analysis_checklist.md
Work through the checklist to discover:
- Target identification - What files does the user want to scope?
- Repository exploration - What file patterns actually exist?
- Framework detection - What tools/frameworks are in use?
- Existing conventions - What patterns are already established?
Output: Clear understanding of target scope with concrete file examples.
What ships with it
6 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.
- 7d ago First seen · 226 lines · 89 tokens per session scan A 3b66b49de9b1
generate-path-instructions is a skill published in the GitHub repository canonical/copilot-collections (29 stars, last pushed 4d ago), licensed Apache-2.0. It adds 89 tokens to every session and 1,771 once invoked, about $0.0004 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.
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.
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.
oncall-handoff
Generate a comprehensive on-call handoff document by aggregating open incidents, ongoing issues, recent deployments, and systems to watch. Orchestrates PagerDuty, Jira, and ArgoCD agents. Use during on-call rotation changes or shift handoffs.
sprint-progress-report
Generate a comprehensive sprint progress report from Jira with velocity metrics, burndown analysis, blocker identification, and team workload distribution. Use when preparing sprint reviews, standups, or tracking sprint health mid-cycle.