review-feedback-quality

review-feedback-quality is a skill for Claude Code, Codex from mickeyyaya/refactoring-skills. It costs 50 tokens per session (2,294 once invoked), scanned A, original, MIT.

A guide to writing code-review comments that clearly explain the problem, its impact, and the required fix. It also explains when feedback should block a change or act as a suggestion.

In plain words
What is it for?
Use it to write constructive comments for bugs, security risks, quality issues, and different severity levels.
Why use it?
It reduces vague comments that are ignored or lead to incorrect fixes, making review feedback easier to act on.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is Why: Path traversal attack — a filename like "../../etc/passwd" lets an attacker read arbitrary files.

Good fit Use it to write constructive comments for bugs, security risks, quality issues, and different severity levels.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/mickeyyaya/refactoring-skills
agentmods
npx agentmods add skills/mickeyyaya/refactoring-skills/review-feedback-quality

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,294 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.
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 $0.00050 $0.02294
Opus 5 $0.00025 $0.01147
Sonnet 5 $0.00010 $0.00459
Haiku 4.5 $0.00005 $0.00229

Measured 9d ago against content hash e69093497850, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

review-feedback-quality 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 9d 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/review-feedback-quality/SKILL.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Feedback Quality — Writing Comments That Get Results

Overview

The quality of a code review is measured by how many issues get fixed with minimum friction. Vague comments are 3x more likely to be ignored or result in incorrect fixes than comments that state what is wrong, why it matters, and how to fix it.

Use this alongside review-cheat-sheet (what to look for) and review-code-quality-process (how to run a review end to end).


Comment Templates by Severity

Every comment should answer three questions: What is the problem, Why it matters, and How to fix it. Depth scales with severity.

CRITICAL — Must Fix Before Merge

[CRITICAL] What: <one-line description of the issue>
Why: <the risk or harm — data loss, security breach, crash in production>
Fix: <specific action — code snippet, API to call, pattern to use>
Ref: <link to CVE, docs, or deeper skill if applicable>

Example:

[CRITICAL] What: User-supplied filename passed directly to os.path.join without sanitization.
Why: Path traversal attack — a filename like "../../etc/passwd" lets an attacker read arbitrary files
on the server.
Fix: Validate that the resolved path starts with the expected base directory:
  base = "/var/uploads"
  safe = os.path.realpath(os.path.join(base, user_filename))
  if not safe.startswith(base):
      raise ValueError("Invalid path")
Ref: skills/security-patterns-code-review

HIGH — Strong Confidence the Change Is Wrong

[HIGH] What: <issue description>
Why: <likely defect or maintenance burden this introduces>
Fix: <concrete suggestion>

Example:

[HIGH] What: getUser() is called inside the loop at line 47.
Why: This fires one DB query per iteration. With 500 users in the list, this is 500 round trips
where a single query with IN (...) would suffice.
Fix: Extract the IDs before the loop, call getUsersByIds(ids), then look up results in a map.

MEDIUM — Likely to Cause a Defect or Maintenance Burden

[MEDIUM] What: <issue>
Why: <why this will hurt later>
Fix: <suggested improvement>

Read the full file on GitHub · 215 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. 9d ago First seen · 215 lines · 50 tokens per session scan A e69093497850

Subscribe to this mod's changes

review-feedback-quality is a skill published in the GitHub repository mickeyyaya/refactoring-skills (6 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 2,294 once invoked, about $0.0003 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 2026-09-03.