Agent skill

Code Review Quality

by proffesor-for-testing in proffesor-for-testing/agentic-qe

Conduct context-driven code reviews focusing on quality, testability, and maintainability.

MITAuto-check passedDevelopment

Install Code Review Quality

skills CLI
$ npx skills add proffesor-for-testing/agentic-qe --skill code-review-quality -a claude-code

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

GitHub CLI
$ gh skill install proffesor-for-testing/agentic-qe code-review-quality --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/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/assets/skills/code-review-quality .claude/skills/code-review-quality && 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
code-review-quality
GitHub stars
494
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
576 words
Files
3 (incl. scripts)
Skills in repo
111
Repo updated
First seen
Licence
MIT

At a glance

Conduct context-driven code reviews focusing on quality, testability, and maintainability.

  • Works in 5 steps: PRIORITIZE feedback: 🔴 Blocker (must… → FOCUS on: Bugs, security, testability,… → ASK questions over commands: "Have you… → …
  • Providing feedback
  • SKILL.md covers Quick Reference Card, Feedback Templates, Review Questions to Ask and Minimum Findings Enforcement, plus 7 more sections
  • Establishing review practices

What it does

Code Review Quality is an agent skill from proffesor-for-testing/agentic-qe. Conduct context-driven code reviews focusing on quality, testability, and maintainability. Use when reviewing code, providing feedback, or establishing review practices.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `schemas/output.json` and `scripts/validate-config.json`).

It sits in Development, covering Code review. The repository describes itself as: Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support… The licence is MIT.

When your agent uses it

  • Providing feedback
  • Establishing review practices

Example prompts

  • “/code-review-quality”

Workflow steps

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

  1. PRIORITIZE feedback: 🔴 Blocker (must fix) → 🟡 Major → 🟢 Minor → 💡 Suggestion
  2. FOCUS on: Bugs, security, testability, maintainability (not style preferences)
  3. ASK questions over commands: "Have you considered...?" > "Change this to..."
  4. PROVIDE context: Why this matters, not just what to change
  5. LIMIT scope: Review < 400 lines at a time for effectiveness

What it can do on your machine

Read from SKILL.md and the folder at commit 829d030. 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

    Ships 1 file in scripts/, which the agent can run.

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

  • Network

    No URLs in SKILL.md.

    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

Code Review Quality loads about 1.9k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 576 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from proffesor-for-testing/agentic-qe at commit 829d030, republished under its MIT licence (© proffesor-for-testing). 576 words, ~1,932 tokens.

Download SKILL.mdSave it as .claude/skills/code-review-quality/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
code-review-quality
description
Conduct context-driven code reviews focusing on quality, testability, and maintainability. Use when reviewing code, providing feedback, or establishing review practices.
category
development-practices
priority
high
tokenEstimate
900
agents
qe-quality-analyzer, qe-security-scanner, qe-performance-tester, qe-coverage-analyzer
implementation_status
optimized
optimization_version
1
last_optimized
2025-12-02
quick_reference_card
true
tags
code-review, feedback, quality, testability, maintainability, pr-review
trust_tier
2

Code Review Quality

<default_to_action> When reviewing code or establishing review practices:

  1. PRIORITIZE feedback: 🔴 Blocker (must fix) → 🟡 Major → 🟢 Minor → 💡 Suggestion
  2. FOCUS on: Bugs, security, testability, maintainability (not style preferences)
  3. ASK questions over commands: "Have you considered...?" > "Change this to..."
  4. PROVIDE context: Why this matters, not just what to change
  5. LIMIT scope: Review < 400 lines at a time for effectiveness

Quick Review Checklist:

  • Logic: Does it work correctly? Edge cases handled?
  • Security: Input validation? Auth checks? Injection risks?
  • Testability: Can this be tested? Is it tested?
  • Maintainability: Clear naming? Single responsibility? DRY?
  • Performance: O(n²) loops? N+1 queries? Memory leaks?

Critical Success Factors:

  • Review the code, not the person
  • Catching bugs > nitpicking style
  • Fast feedback (< 24h) > thorough feedback </default_to_action>

Quick Reference Card

When to Use
  • PR code reviews
  • Pair programming feedback
  • Establishing team review standards
  • Mentoring developers
Feedback Priority Levels
LevelIconMeaningAction
Blocker🔴Bug/security/crashMust fix before merge
Major🟡Logic issue/test gapShould fix before merge
Minor🟢Style/namingNice to fix
Suggestion💡Alternative approachConsider for future
Review Scope Limits
Lines ChangedRecommendation
< 200Single review session
200-400Review in chunks
> 400Request PR split
What to Focus On
✅ Review❌ Skip
Logic correctnessFormatting (use linter)
Security risksNaming preferences
Test coverageArchitecture debates
Performance issuesStyle opinions
Error handlingTrivial changes

Feedback Templates

Blocker (Must Fix)
markdown
🔴 **BLOCKER: SQL Injection Risk**

This query is vulnerable to SQL injection:
```javascript
db.query(`SELECT * FROM users WHERE id = ${userId}`)

Fix: Use parameterized queries:

javascript
db.query('SELECT * FROM users WHERE id = ?', [userId])

Why: User input directly in SQL allows attackers to execute arbitrary queries.


### Major (Should Fix)
```markdown
🟡 **MAJOR: Missing Error Handling**

What happens if `fetchUser()` throws? The error bubbles up unhandled.

**Suggestion:** Add try/catch with appropriate error response:
```javascript
try {
  const user = await fetchUser(id);
  return user;
} catch (error) {
  logger.error('Failed to fetch user', { id, error });
  throw new NotFoundError('User not found');
}

### Minor (Nice to Fix)
```markdown
🟢 **minor:** Variable name could be clearer

`d` doesn't convey meaning. Consider `daysSinceLastLogin`.
Suggestion (Consider)
markdown
💡 **suggestion:** Consider extracting this to a helper

This validation logic appears in 3 places. A `validateEmail()` helper would reduce duplication. Not blocking, but might be worth a follow-up PR.

Review Questions to Ask

Logic
  • What happens when X is null/empty/negative?
  • Is there a race condition here?
  • What if the API call fails?
Security
  • Is user input validated/sanitized?
  • Are auth checks in place?
  • Any secrets or PII exposed?
Testability
  • How would you test this?
  • Are dependencies injectable?
  • Is there a test for the happy path? Edge cases?
Maintainability
  • Will the next developer understand this?
  • Is this doing too many things?
  • Is there duplication we could reduce?

Minimum Findings Enforcement

Reviews must meet a minimum weighted finding score of 3.0 (CRITICAL=3, HIGH=2, MEDIUM=1, LOW=0.5, INFORMATIONAL=0.25). If the initial review falls short, run the qe-devils-advocate agent as a meta-reviewer to find additional observations. Every review should have at least 3 actionable observations.


Show full SKILL.md (217 more words)Show less

Agent-Assisted Reviews

typescript
// Comprehensive code review
await Task("Code Review", {
  prNumber: 123,
  checks: ['security', 'performance', 'testability', 'maintainability'],
  feedbackLevels: ['blocker', 'major', 'minor'],
  autoApprove: { maxBlockers: 0, maxMajor: 2 }
}, "qe-quality-analyzer");

// Security-focused review
await Task("Security Review", {
  prFiles: changedFiles,
  scanTypes: ['injection', 'auth', 'secrets', 'dependencies']
}, "qe-security-scanner");

// Test coverage review
await Task("Coverage Review", {
  prNumber: 123,
  requireNewTests: true,
  minCoverageDelta: 0
}, "qe-coverage-analyzer");

Agent Coordination Hints

Memory Namespace
aqe/code-review/
├── review-history/*     - Past review decisions
├── patterns/*           - Common issues by team/repo
├── feedback-templates/* - Reusable feedback
└── metrics/*            - Review turnaround time
Fleet Coordination
typescript
const reviewFleet = await FleetManager.coordinate({
  strategy: 'code-review',
  agents: [
    'qe-quality-analyzer',    // Logic, maintainability
    'qe-security-scanner',    // Security risks
    'qe-performance-tester',  // Performance issues
    'qe-coverage-analyzer'    // Test coverage
  ],
  topology: 'parallel'
});

Review Etiquette

✅ Do❌ Don't
"Have you considered...?""This is wrong"
Explain why it mattersJust say "fix this"
Acknowledge good codeOnly point out negatives
Suggest, don't demandBe condescending
Review < 400 linesReview 2000 lines at once


Remember

Prioritize feedback: 🔴 Blocker → 🟡 Major → 🟢 Minor → 💡 Suggestion. Focus on bugs and security, not style. Ask questions, don't command. Review < 400 lines at a time. Fast feedback (< 24h) beats thorough feedback.

With Agents: Agents automate security, performance, and coverage checks, freeing human reviewers to focus on logic and design. Use agents for consistent, fast initial review.

Skill Composition

  • Security concerns → Compose with /security-testing for security-focused review
  • Coverage check → Run /qe-coverage-analysis on changed files
  • Ship decision → Feed review results into /qe-quality-assessment

Gotchas

  • Agent reviews >400 lines at once and misses issues — chunk reviews to 200-400 lines maximum
  • Nitpicking style while missing logic bugs is the #1 agent review failure — prioritize correctness over formatting
  • Agent approves code that compiles but has subtle race conditions — always check shared state and async patterns
  • Review comments without suggested fixes are unhelpful — always include a proposed alternative
  • Agent doesn't check if the PR actually solves the linked issue — verify the stated problem is actually fixed

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

Files

SKILL.md and 2 other files (scripts) in assets/skills/code-review-quality of proffesor-for-testing/agentic-qe.

  • SKILL.md
  • schemas/output.json
  • scripts/validate-config.json

Open the folder on GitHubat commit 829d030

Used in 1 other repository

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in proffesor-for-testing/agentic-qe, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Code Review Quality 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.

Code Review Quality compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Review Quality this skillproffesor-for-testing/agentic-qe4941 repos~1.9kAutomated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Backend Code Reviewlangflow-ai/langflow156k—~3.5kAutomated safety check: NotesMIT
Understand Diff AnalysisEgonex-AI/Understand-Anything85k1 repos~1.4kAutomated safety check: PassMIT
Mole Bug Patternstw93/Mole69k—~2kAutomated safety check: PassGPL-3.0

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Categories

Questions about Code Review Quality

What does Code Review Quality do?

Conduct context-driven code reviews focusing on quality, testability, and maintainability. Code Review Quality is an agent skill from proffesor-for-testing/agentic-qe. Conduct context-driven code reviews focusing on quality, testability, and maintainability.

When should I use Code Review Quality?

Code Review Quality fits situations like: providing feedback; establishing review practices.

How do I install Code Review Quality in Claude Code?

Run `npx skills add proffesor-for-testing/agentic-qe --skill code-review-quality -a claude-code`. Or copy the skill folder (assets/skills/code-review-quality in proffesor-for-testing/agentic-qe) into .claude/skills/code-review-quality in your project. Claude Code loads it when a task matches its description.

How do I install Code Review Quality in Codex?

Run `npx skills add proffesor-for-testing/agentic-qe --skill code-review-quality -a codex`. Or copy the skill folder (assets/skills/code-review-quality in proffesor-for-testing/agentic-qe) into .agents/skills/code-review-quality in your project. Codex loads it when a task matches its description.

Can I use Code Review Quality 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 proffesor-for-testing/agentic-qe --skill code-review-quality -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-review-quality, .gemini/skills/code-review-quality, .github/skills/code-review-quality and .opencode/skills/code-review-quality in your project.

What does Code Review Quality need to run?

SKILL.md names no scripts, command-line tools or credentials: Code Review Quality is instructions for the agent only.

Does Code Review Quality access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Code Review Quality 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Code Review Quality use?

Code Review Quality 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 Code Review Quality use?

About 1.9k tokens (SKILL.md is roughly 7.7k 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 Code Review Quality?

Skills that share tags, products or a category with Code Review Quality: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Backend Code Review (langflow-ai/langflow, 156k stars) and Understand Diff Analysis (Egonex-AI/Understand-Anything, 85k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Review Quality?

proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 494 GitHub stars. The repository holds 111 skills in this directory. The repository was last updated on October 4, 2026.

Source: proffesor-for-testing/agentic-qe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.