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Agent skill

Address Reviews

by FastLED in FastLED/FastLED

Fetch CodeRabbit review comments on the current PR, classify each finding, apply fixes for valid issues, and reply to each thread.

MITAuto-check passedDevelopment

Install Address Reviews

skills CLI
$ npx skills add FastLED/FastLED --skill address-reviews -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install FastLED/FastLED address-reviews --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/FastLED/FastLED.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/address-reviews .claude/skills/address-reviews && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
address-reviews
GitHub stars
7.5k
Token cost
~741 tokens
SKILL.md length
315 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Fetch CodeRabbit review comments on the current PR, classify each finding, apply fixes for valid issues, and reply to each thread.

  • Works in 6 steps: Fetch comments. Run → Review the plan. For each valid-fix,… → Apply fixes one comment at a time. Edit… → …
  • Development work in your project
  • SKILL.md covers Workflow, Safety rails, When to invoke and When NOT to invoke
  • Calls uv and gh

What it does

Address Reviews is an agent skill from FastLED/FastLED. Fetch CodeRabbit review comments on the current PR, classify each finding, apply fixes for valid issues, and reply to each thread. Use before attempting gh pr merge when CodeRabbit has posted a review. Blocks merge on unresolved threads. Routes security-critical findings to a human.

Its SKILL.md is about 740 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The repository describes itself as: The FastLED library for colored LED animation on Arduino. Please direct questions/requests for help to the FastLED Reddit community: http://fastled.io/r We'd like to use github… The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/address-reviews”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Fetch comments. Run
  2. Review the plan. For each valid-fix, read the file/line being flagged and decide the concrete change. For false-positive, draft the reply…
  3. Apply fixes one comment at a time. Edit the referenced file, re-read to confirm the change is correct, and stage it.
  4. Reply to each thread via
  5. Commit and push using a conventional-commit-format message that lists each addressed comment
  6. Re-run the plan. If valid-fix count hits zero and no security-flag remains, the PR is ready to merge. Otherwise iterate — hard limit: 3…

What it can do on your machine

Read from SKILL.md and the folder at commit 7d26d7f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Address Reviews loads about 741 tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 315 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~741

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from FastLED/FastLED at commit 7d26d7f, republished under its MIT licence (© FastLED). 315 words, ~741 tokens.

Download SKILL.mdSave it as .claude/skills/address-reviews/SKILL.md (or your agent's skills folder).
name
address-reviews
description
Fetch CodeRabbit review comments on the current PR, classify each finding, apply fixes for valid issues, and reply to each thread. Use before attempting gh pr merge when CodeRabbit has posted a review. Blocks merge on unresolved threads. Routes security-critical findings to a human.
argument-hint
pr-number, optional — defaults to PR for current branch
context
fork

Address CodeRabbit review comments on a PR end-to-end: fetch, classify, fix, reply, commit, push. See ci/tools/coderabbit_addressor.py for the helper that drives the workflow.

Arguments: $ARGUMENTS

Workflow

  1. Fetch comments. Run:

    bash
    uv run ci/tools/coderabbit_addressor.py $ARGUMENTS --plan

    This prints a JSON plan listing every unresolved CodeRabbit comment, classified into one of four buckets:

    • valid-fix — actionable code change
    • style — preference/nit; reply-only
    • false-positive — disagree; reply with rationale
    • security-flag — flag a human; do not auto-fix

  2. Review the plan. For each valid-fix, read the file/line being flagged and decide the concrete change. For false-positive, draft the reply. Do not touch anything in security-flag — leave it for a human reviewer.

  3. Apply fixes one comment at a time. Edit the referenced file, re-read to confirm the change is correct, and stage it.

  4. Reply to each thread via:

    bash
    uv run ci/tools/coderabbit_addressor.py $ARGUMENTS --reply <comment-id> "<message>"

    Keep replies terse: either "Fixed in <commit-sha-short>" or a one-sentence rationale for disagreement.

  5. Commit and push using a conventional-commit-format message that lists each addressed comment:

    fix: address CodeRabbit review feedback

- <comment-1-summary>
- <comment-2-summary>

  6. Re-run the plan. If valid-fix count hits zero and no security-flag remains, the PR is ready to merge. Otherwise iterate — hard limit: 3 passes.

Safety rails

  • Max 3 iterations per PR. If CodeRabbit keeps asking for changes after 3 rounds, stop and flag the human reviewer — something structural is off.
  • Never auto-fix security-flag comments. The classifier routes any comment mentioning security, CVE, auth, credential, secret, RCE, injection, CRITICAL, data loss, UAF, use-after-free, buffer overflow, or out-of-bounds into this bucket.
  • The pre-merge hook (ci/hooks/check_pr_merge_reviews.py) blocks gh pr merge until all CodeRabbit threads are resolved, so you cannot accidentally ship with pending feedback.

When to invoke

  • Before any gh pr merge on a PR where CodeRabbit has posted review comments.
  • After pushing a new commit that might have triggered a re-review — the hook will remind you.

When NOT to invoke

  • On draft PRs where CodeRabbit has not yet reviewed.
  • When every open comment is already in a security-flag state — defer to a human.

© FastLED, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/address-reviews of FastLED/FastLED.

Open the folder on GitHubat commit 7d26d7f

Compare with similar skills

Address Reviews next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Address Reviews compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Address Reviews this skillFastLED/FastLED7.5k—~741Automated safety check: PassMIT
Vercel Composition Patternssupabase/supabase111k58 repos~726Automated safety check: PassMIT
Finishing a Development Branchobra/superpowers297k5 repos~1.9kAutomated safety check: PassMIT
Typescript Advanced Typesrolling-scopes/rsschool-app10k25 repos~4.2kAutomated safety check: PassMPL-2.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k4 repos~1.1kAutomated safety check: PassMIT

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Categories

Questions about Address Reviews

What does Address Reviews do?

Fetch CodeRabbit review comments on the current PR, classify each finding, apply fixes for valid issues, and reply to each thread. Address Reviews is an agent skill from FastLED/FastLED. Fetch CodeRabbit review comments on the current PR, classify each finding, apply fixes for valid issues, and reply to each thread.

When should I use Address Reviews?

Address Reviews fits situations like: development work in your project.

How do I install Address Reviews in Claude Code?

Run `npx skills add FastLED/FastLED --skill address-reviews -a claude-code`. Or copy the skill folder (.claude/skills/address-reviews in FastLED/FastLED) into .claude/skills/address-reviews in your project. Claude Code loads it when a task matches its description.

How do I install Address Reviews in Codex?

Run `npx skills add FastLED/FastLED --skill address-reviews -a codex`. Or copy the skill folder (.claude/skills/address-reviews in FastLED/FastLED) into .agents/skills/address-reviews in your project. Codex loads it when a task matches its description.

Can I use Address Reviews in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add FastLED/FastLED --skill address-reviews -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/address-reviews, .gemini/skills/address-reviews, .github/skills/address-reviews and .opencode/skills/address-reviews in your project.

What does Address Reviews need to run?

Going by SKILL.md and its folder, Address Reviews needs the command-line tools its instructions call (uv and gh).

Does Address Reviews access the network?

SKILL.md contains no URLs. Its commands use uv and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Address Reviews safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Address Reviews use?

Address Reviews is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Address Reviews use?

About 741 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Address Reviews?

Skills that share tags, products or a category with Address Reviews: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Address Reviews?

FastLED (a GitHub organization) maintains it in FastLED/FastLED, which has 7,505 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 10, 2026.

Source: FastLED/FastLED on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.