rome: Skill for Claude Code

.claude/skills/respond-to-review/SKILL.md

respond-to-review is a skill for Claude Code from rome-os/rome. It costs 105 tokens per session (991 once invoked), scanned A, original, MIT.

A process for handling automated code-review findings on a pull request, which is a proposed set of changes to a code repository.

In plain words
What is it for?
Collecting review comments, classifying them, making minimal fixes with tests, correcting claims, creating follow-up issues, or declining findings in writing.
Why use it?
It prevents developers from blindly changing code or expanding the pull request's scope. Each finding receives a clear outcome after duplicates and already-documented issues are removed.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is rome-os/rome's own configuration. It tells Claude Code how to work on rome itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything rome configures →

Reuse

Borrowing it

Nothing to install: this file belongs to rome-os/rome. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/rome-os/rome/main/.claude/skills/respond-to-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/rome-os/rome

Made for: Claude Code.

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 respond-to-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/rome-os/rome/respond-to-review/github.svg)](https://agentmods.dev/skills/rome-os/rome/respond-to-review)
Your own site
<a href="https://agentmods.dev/skills/rome-os/rome/respond-to-review"><img src="https://agentmods.dev/badge/skills/rome-os/rome/respond-to-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for respond-to-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/rome-os/rome/respond-to-review"><img src="https://agentmods.dev/badge/skills/rome-os/rome/respond-to-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 991 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00105 $0.00991
Opus 5 $0.00053 $0.00495
Sonnet 5 $0.00021 $0.00198
Haiku 4.5 $0.00011 $0.00099

Measured 12d ago against content hash 8aedfd3494c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

respond-to-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 12d 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.

.claude/skills/respond-to-review/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Respond to review feedback

Goal end-state: every finding on the PR has exactly one of four answers — a minimal fix with a test, a corrected claim in the PR description, a follow-up issue, or a written decline. The PR's diff grows as little as possible.

Classification precedes code. Never start implementing a finding before classifying it.

Phase 1 — Gather and dedupe

  1. Pull every review body and inline comment on the PR. Drop reviews marked "This review has been superseded."
  2. Merge duplicate findings — same file, same defect — into one item. Post the answer once and cross-link it from the other thread.
  3. Discard the bots' severity labels and verdicts. Phase 2 re-derives priority.

Phase 2 — Classify every finding

Run each finding through these tests in order. The first test that matches decides the bucket.

Test 1 — the bot hedged

If the bot itself presents the finding as optional or non-blocking — "no action required", "consider", "worth noting": decline, with a one-line acknowledgment.

Test 2 — the PR already disclosed it

If the finding restates a "Not in this PR" item, a stated tradeoff, or a documented transitional state: decline, pointing at the section that covers it.

Test 3 — no caller can reach it

Ask what concrete caller, with what concrete input, hits the defect on main plus this diff. If the answer needs a future adapter, a third-party implementation, an input no caller produces, or a scale nothing is wired to reach: decline, stating what would have to exist first. If the concern matters later, record it as one sentence in the interface contract or as a follow-up issue.

Test 4 — the defect predates the diff

If the defect exists on main without this diff: follow-up issue, linked from the reply.

Exception: if the PR's own claims depend on the old code being correct — for example, a claim of parity with it — fix the defect in this PR.

Test 5 — code or claim

The finding is real, reachable, and introduced here. If it does not contradict a claim in the PR description, fix the code. If it does, trace the claim to the issue the PR closes:

Read the full file on GitHub · 78 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. 12d ago First seen · 78 lines · 105 tokens per session scan A 8aedfd3494c6

Subscribe to this mod's changes

respond-to-review is a skill published in the GitHub repository rome-os/rome (488 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 991 once invoked, about $0.0005 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.

Related

Other skills, from other repositories

review-prs

Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…

googleapis/mcp-toolbox · 162 tokens

bootstrap-repo-analysis

First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the…

langchain-ai/open-swe · 73 tokens

continual-learning

Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt…

langchain-ai/open-swe · 89 tokens

github

GitHub operations via gh CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating/commenting on issues, (3) listing/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available)…

opensquilla/opensquilla · 101 tokens

meta-codereview-current-diff

Read the current uncommitted diff, run three independent reviewers (safety + tests-coverage + style) in parallel, then arbitrate a single BLOCK / BLOCKWITHOVERRIDE / PASSWITHNOTES verdict. Use before commit when you want a multi-perspective second-opinion instead of a single-reviewer agent loop.

opensquilla/opensquilla · 73 tokens

code-review

Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. owner/repo N). For PRs, can post findings as line-level review comments.

suyoumo/ClawProBench · 57 tokens