Code review
Eric's collection of AI agent skills.
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Comprehensive code review combining Clean Code principles and senior engineer expertise. Reviews git changes for SOLID violations, security risks, clean code smells, and proposes actionable improvements.
SKILL.md
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Code Review
Comprehensive code review skill that combines Clean Code principles (Robert C. Martin) with senior engineer expertise for SOLID, architecture, security, performance, and code quality.
When to Use
- Reviewing Pull Requests: Provide constructive, principle-based feedback.
- Writing new code: Ensure high quality from the start.
- Refactoring legacy code: Identify and remove code smells.
- Improving team standards: Align on industry-standard best practices.
Severity Levels
| Level | Name | Description | Action |
|---|---|---|---|
| P0 | Critical | Security vulnerability, data loss risk, correctness bug | Must block merge |
| P1 | High | Logic error, significant SOLID violation, performance regression | Should fix before merge |
| P2 | Medium | Code smell, maintainability concern, minor SOLID violation | Fix in this PR or create follow-up |
| P3 | Low | Style, naming, minor suggestion | Optional improvement |
Workflow
1) Preflight Context
- Use
git status -sb,git diff --stat, andgit diffto scope changes. - If needed, use
rgorgrepto find related modules, usages, and contracts. - Identify entry points, ownership boundaries, and critical paths (auth, payments, data writes, network).
Edge cases:
- No changes: If
git diffis empty, inform user and ask if they want to review staged changes or a specific commit range. - Large diff (>500 lines): Summarize by file first, then review in batches by module/feature area.
- Mixed concerns: Group findings by logical feature, not just file order.
2) Linter Zero-New-Violations Check
- Load
references/linter-checklist.mdfor detection and baseline strategy. - Detect project linters: Scan for config files (
.eslintrc*,pyproject.toml,.golangci.yml,Cargo.toml, etc.) to identify which linters are configured. - Run linters on changed files only: Use
git diff --name-only --diff-filter=ACMR HEADto get changed files, then run the detected linter(s) targeting those files. - Baseline comparison: Cross-reference linter output with diff hunks — only report violations on changed/added lines. Pre-existing violations in unchanged code are out of scope.
- Classification:
- New error in changed line → P1 (must fix before merge)
- New warning in changed line → P2 (should fix in this PR)
- Formatter-only issues → P3 (suggest auto-fix command)
- If auto-fix is available (e.g.,
eslint --fix,ruff check --fix), mention the command in the suggested fix. - If no linter is detected, skip this step and note it in the review output.
3) Clean Code Principles
Apply the following Clean Code principles during review:
Meaningful Names
- Intention-Revealing Names:
elapsedTimeInDaysinstead ofd. - Avoid Disinformation: Don't use
accountListif it's actually aMap. - Meaningful Distinctions: Avoid
ProductDatavsProductInfo. - Pronounceable/Searchable Names: Avoid
genymdhms. - Class Names: Nouns (
Customer,WikiPage). AvoidManager,Data. - Method Names: Verbs (
postPayment,deletePage).
Functions
- Small: Functions should be shorter than you think (~20 lines max).
- Do One Thing: A function should do only one thing, and do it well.
- One Level of Abstraction: Don't mix high-level business logic with low-level details.
- Descriptive Names:
isPasswordValidis better thancheck. - Arguments: 0 is ideal, 1-2 is okay, 3+ requires strong justification.
- No Side Effects: Functions shouldn't secretly change global state.
Comments
- Don't Comment Bad Code — Rewrite It: Most comments are a sign of failure to express ourselves in code.
- Good Comments: Legal, Informative (regex intent), Clarification (external libraries), TODOs.
- Bad Comments: Mumbling, Redundant, Misleading, Mandated, Noise, Position Markers.
Formatting
- The Newspaper Metaphor: High-level concepts at the top, details at the bottom.
- Vertical Density: Related lines should be close to each other.
- Distance: Variables declared near their usage.
Objects, Data Structures & Error Handling
- Data Abstraction: Hide implementation behind interfaces.
- Law of Demeter: Avoid
a.getB().getC().doSomething(). - Use Exceptions instead of Return Codes.
- Don't Return/Pass Null.
4) SOLID + Architecture Smells
- Load
references/solid-checklist.mdfor specific prompts. - Look for:
- SRP: Overloaded modules with unrelated responsibilities.
- OCP: Frequent edits to add behavior instead of extension points.
- LSP: Subclasses that break expectations or require type checks.
- ISP: Wide interfaces with unused methods.
- DIP: High-level logic tied to low-level implementations.
- When you propose a refactor, explain why it improves cohesion/coupling and outline a minimal, safe split.
- If refactor is non-trivial, propose an incremental plan instead of a large rewrite.
5) Removal Candidates + Iteration Plan
- Load
references/removal-plan.mdfor template. - Identify code that is unused, redundant, or feature-flagged off.
- Distinguish safe delete now vs defer with plan.
- Provide a follow-up plan with concrete steps and checkpoints (tests/metrics).
6) Security and Reliability Scan
- Load
references/security-checklist.mdfor coverage. - Check for:
- XSS, injection (SQL/NoSQL/command), SSRF, path traversal
- AuthZ/AuthN gaps, missing tenancy checks
- Secret leakage or API keys in logs/env/files
- Rate limits, unbounded loops, CPU/memory hotspots
- Unsafe deserialization, weak crypto, insecure defaults
- Race conditions: concurrent access, check-then-act, TOCTOU, missing locks
- Call out both exploitability and impact.
7) Code Quality Scan
- Load
references/code-quality-checklist.mdfor coverage. - Check for:
- Error handling: swallowed exceptions, overly broad catch, missing error handling, async errors
- Performance: N+1 queries, CPU-intensive ops in hot paths, missing cache, unbounded memory
- Boundary conditions: null/undefined handling, empty collections, numeric boundaries, off-by-one
- Flag issues that may cause silent failures or production incidents.
8) Output Format
Structure your review as follows:
## Code Review Summary
**Files reviewed**: X files, Y lines changed
**Overall assessment**: [APPROVE / REQUEST_CHANGES / COMMENT]
---
## Findings
### P0 - Critical
(none or list)
### P1 - High
1. **[file:line]** Brief title
- Description of issue
- Suggested fix
### P2 - Medium
2. (continue numbering across sections)
- ...
### P3 - Low
...
---
## Linter Results
**Linters detected**: [list or "none"]
**New violations in changed lines**: X errors, Y warnings
(list each violation with file:line, rule, and auto-fix command if available)
## Clean Code Issues
(naming, function size, comment quality, formatting — grouped by principle)
## Removal/Iteration Plan
(if applicable)
## Additional Suggestions
(optional improvements, not blocking)
Inline comments: Use this format for file-specific findings:
::code-comment{file="path/to/file.ts" line="42" severity="P1"}
Description of the issue and suggested fix.
::
Clean review: If no issues found, explicitly state:
- What was checked
- Any areas not covered (e.g., "Did not verify database migrations")
- Residual risks or recommended follow-up tests
9) Next Steps Confirmation
After presenting findings, ask user how to proceed:
---
## Next Steps
I found X issues (P0: _, P1: _, P2: _, P3: _).
**How would you like to proceed?**
1. **Fix all** - I'll implement all suggested fixes
2. **Fix P0/P1 only** - Address critical and high priority issues
3. **Fix specific items** - Tell me which issues to fix
4. **No changes** - Review complete, no implementation needed
Please choose an option or provide specific instructions.
Important: Do NOT implement any changes until user explicitly confirms. This is a review-first workflow.
Clean Code Checklist
- Is this function smaller than 20 lines?
- Does this function do exactly one thing?
- Are all names searchable and intention-revealing?
- Have I avoided comments by making the code clearer?
- Am I passing too many arguments?
- Is there a failing test for this change?
- Does the code follow the Law of Demeter?
- Are exceptions used instead of return codes?
Resources
references/
| File | Purpose |
|---|---|
linter-checklist.md | Linter detection, baseline comparison, and auto-fix commands |
solid-checklist.md | SOLID smell prompts, common code smells, and refactor heuristics |
security-checklist.md | Web/app security and runtime risk checklist |
code-quality-checklist.md | Error handling, performance, boundary conditions |
removal-plan.md | Template for deletion candidates and follow-up plan |
Gives 0 of the 12 instructions most code review skills give in ~2.2k tokens
Counted across 610 of the 674 authors here whose files we hold, read 2026-08-06
- push back with technical reasoning if wrongin 60 of 610, across 24 files
- ask for clarification on unclear itemsin 51 of 610, across 16 files
- fix critical issues immediatelyin 45 of 610, across 29 files
- implement one item at a timein 45 of 610, across 11 files
- group findings by severityin 44 of 610, across 43 files
- verify feedback against the codebasein 42 of 610, across 8 files
- dispatch a code reviewer subagentin 39 of 610, across 23 files
- fix important issues before proceedingin 37 of 610, across 22 files
- test each fix individuallyin 35 of 610, across 7 files
- reply in github comment threadsin 33 of 610, across 5 files
- check for security vulnerabilitiesin 31 of 610, across 27 files
- factualize corrections without over-explainingin 30 of 610, across 2 files
Said here and by no other author read
- run linters on changed files only
- check code for clean code smells
- group findings by logical feature
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.