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code-review

Automates project-aware code review enforcing configured lint standards and conventions from CLAUDE.md and cognitive-core.conf.

11|1|Updated Feb 9, 2026
Search Tags:#automation#architecture#static-analysis#lint#code-review#project-conventions
One-click install
npx skills add https://github.com/mindcockpit-ai/cognitive-core --skill code-review-mindcockpit-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review
Source: https://github.com/mindcockpit-ai/cognitive-core/tree/main/core/skills/code-review
Command: npx skills add https://github.com/mindcockpit-ai/cognitive-core --skill code-review-mindcockpit-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Developers often miss project‑specific coding standards and architectural constraints during manual reviews, leading to inconsistent quality and hidden bugs.

Core Features & Use Cases

  • Reads project conventions from CLAUDE.md and cognitive-core.conf.
  • Detects naming violations, missing error handling, hard‑coded secrets, and architecture mismatches.
  • Provides multi‑pass analysis for large change sets, generating a consolidated, provenance‑rich report.
  • Ideal for CI pipelines, pre‑commit checks, or on‑demand reviews of new feature branches.

Quick Start

Run the code-review skill on the src directory to receive a detailed review report.

Frequently Asked Questions about code-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate code reviews to enforce project-specific architecture and naming conventions?▼

Automated code reviews enforce project conventions by reading standards from CLAUDE.md and language settings from cognitive-core.conf. The skill detects naming violations, architecture mismatches, and missing error handling to ensure consistent source file quality.

Can I use static analysis to detect hard-coded secrets and anti-patterns in my source files?▼

Static analysis detects hard-coded secrets and anti-patterns by applying configured lint commands and project conventions. It analyzes source files against defined rules to identify hidden vulnerabilities and structural inconsistencies.

What's the best way to run project-aware code reviews for large change sets in a CI pipeline?▼

Project-aware code reviews handle large change sets using multi-pass analysis to generate a consolidated, provenance-rich report. This approach is ideal for CI pipelines and pre-commit checks, ensuring comprehensive coverage without missing architectural constraints.

Do I need a specific configuration file to define coding standards before running an automated code review?▼

You need a CLAUDE.md file defining coding standards and a cognitive-core.conf file specifying language and architecture settings. These configuration files provide the project context required to enforce naming conventions, error handling, and layer dependencies.

Why does manual code review miss project-specific architectural constraints and cause hidden bugs?▼

Manual code review misses architectural constraints because it lacks consistent enforcement of project-specific standards. Reviewers often overlook layer dependencies and anti-patterns, leading to inconsistent quality and hidden bugs across feature branches.

What limitations exist when using lint commands for layer dependency and anti-pattern detection?▼

Lint commands are limited by the rules defined in your configuration files, meaning they only detect violations explicitly covered by CLAUDE.md and cognitive-core.conf. They cannot identify architectural mismatches or anti-patterns outside these configured project conventions.

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