ct-code-review

ct-code-review is a skill for Claude Code, Codex from canonical/charm-tech. It costs 43 tokens per session (808 once invoked), scanned A, original, Apache-2.0.

A set of guidelines for reviewing code and changes before they are merged into a project.

In plain words
What is it for?
Use it to review code, prepare pull requests for review, and write constructive, clearly labeled feedback.
Why use it?
It gives reviewers a consistent way to describe blocking problems, optional suggestions, questions, and minor issues without attacking the author.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/canonical/charm-tech/ct-code-review
Any agent
npx skills add canonical/charm-tech --skill ct-code-review
Clone the repo
git clone --depth 1 https://github.com/canonical/charm-tech

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ct-code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/canonical/charm-tech/ct-code-review.svg)](https://agentmods.dev/skills/canonical/charm-tech/ct-code-review)
Your own site
<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>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 808 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash fed662e9b3e7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

skills/engineering/ct-code-review/SKILL.md · 82 lines

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 merge
  • suggestion: — a proposed improvement; the author decides
  • nit: — minor/stylistic; non-blocking by definition
  • question: — seeking clarification, not necessarily a change
  • praise: — 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

Read the full file on GitHub · 82 lines

Changes

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.

  1. 3d ago First seen · 82 lines · 43 tokens per session scan A fed662e9b3e7

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

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.

microsoft/vscode · 51 tokens

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.

microsoft/vscode · 53 tokens

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…

microsoft/vscode · 71 tokens