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 agentmods add skills/canonical/charm-tech/ct-code-reviewnpx skills add canonical/charm-tech --skill ct-code-reviewgit clone --depth 1 https://github.com/canonical/charm-techWrote 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/charm-tech/ct-code-review)<a href="https://agentmods.dev/skills/canonical/charm-tech/ct-code-review"><img src="https://agentmods.dev/badge/skills/canonical/charm-tech/ct-code-review.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 | $0.00043 | $0.00808 |
| Opus 5 | $0.00022 | $0.00404 |
| Sonnet 5 | $0.00009 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
ct-code-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 3d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canonical Code Review Guidelines
These guidelines define how code reviews should be conducted at Canonical. They focus on broadly applicable review techniques that are relevant to all projects.
Soft Skills
Tone
- Review the submission, not the author. Avoid "you did this wrong."
- Prefer "this could be improved by…" or "this doesn't seem right to me…"
- Be constructive — suggest how something may be improved
- For non-critical suggestions, make clear it's informational, not a change request: "I might have done this differently for these reasons…"
- Avoid being overtly negative, even if something is poor quality
- Remember: review is a learning experience for both author and reviewers
Label comments by intent
Prefix each comment with its intent so the author can tell blocking issues from optional ones at a glance (see Conventional Comments):
blocking:— must be resolved before mergesuggestion:— a proposed improvement; the author decidesnit:— minor/stylistic; non-blocking by definitionquestion:— seeking clarification, not necessarily a changepraise:— call out something done well Add(non-blocking)to any label when the distinction isn't obvious from the prefix.
Procedures
- If CI isn't passing, investigate why before approving
- Commits/PRs should reference their tracking ticket where one exists (e.g. a link to the issue)
- Pull request descriptions must be usefully descriptive — "Fixed Bug 12345" is not sufficient; include a sentence or two about what changed and why
Code Quality
- All patches must follow the code style and conventions of the appropriate project
- Look for cases where end-user function behaviour diverges from upstream — ask for clarification and push for upstreamable implementations
Changeset Size and Scope
Size
- Large changesets are complex and difficult to review
- Large changesets can usually be split into smaller commits or separate tickets
- They tend to combine related but not strictly connected changes
- Use pragmatism — sometimes a large changeset is genuinely necessary
- When a changeset is too large, still review it in full, but recommend splitting it (a
suggestion:, not a blocker) — note where the natural seams are
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.
- 3d ago First seen · 82 lines · 43 tokens per session scan A fed662e9b3e7
ct-code-review is a skill published in the GitHub repository canonical/charm-tech (2 stars, last pushed 18d ago), licensed Apache-2.0. It adds 43 tokens to every session and 808 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…