Agent skill · software engineering · aiskillstore
quality-reviewer
Deep code review with web research to verify against latest ecosystem. Use when user says 'double check against latest', 'verify versions', 'check security', 'review against docs', or needs deep analysis beyond automatic quality hook.
Why this skill is useful
Adds a structured review process and web research techniques to verify code against the latest ecosystem, which the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Quality Reviewer Deep quality review with web research to verify code against the latest ecosystem state. Primary differentiator: Web research to verify against current versions, documentation, and best practices. Triggers: Explicit web research request: "double check against latest docs", "verify we're using latest version", "check for security issues" Deep dive needed: User wants analysis beyond automatic hook (performance, architecture alternatives, trade-offs) No SAFEWORD.md/CLAUDE.md: Projects without context files (automatic hook won't run, manual review needed) Pre-change review: User wants review before making changes (automatic hook only triggers after changes) Relationship to automatic quality hook: Automatic hook: Fast quality check using existing knowledge + project context (guaranteed, runs on every change) This skill: Deep review with web research when verification against current ecosystem is needed (on-demand, 2-3 min) Review Protocol 1. Identify What Changed Understand context: What files were just modified? What problem is being solved? What was the implementation approach? 2. Read Project Standards Read relevant standards: CLAUDE.md or SAFEWORD.md - Project-specific guidelines ARCHITECTURE.md - Architectural principles 3. Evaluate Correctness Will it work? Does the logic make sense? Are there obvious bugs? Edge cases: Empty inputs, null/undefined, boundary conditions (0, -1, max)? Concurrent access, network failures? Error handling: Are errors caught appropriately? Helpful error messages? Cleanup handled (resources, connections)? Logic errors: Off-by-one errors, race conditions, wrong assumptions? 4. Evaluate Anti-Bloat Are all dependencies necessary? Could we use stdlib/built-ins? Are abstractions solving real problems or imaginary ones? YAGNI: Is this feature actually needed now? 5. Evaluate Elegance Is the code easy to understand? Are names clear and descriptive? Is the intent obvious? Will this be easy to change later? 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/quality-reviewer