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atlas-agent-peer-reviewer

Review pull requests and patches with evidence-based verdicts.

Updated May 27, 2025
Search Tags:#automation#testing#devops#software engineering#code review#security#quality gate
One-click install
npx skills add https://github.com/ajstack22/StackMap --skill atlas-agent-peer-reviewer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: atlas-agent-peer-reviewer
Source: https://github.com/ajstack22/StackMap/tree/main/atlas-skills/atlas-agent-peer-reviewer
Command: npx skills add https://github.com/ajstack22/StackMap --skill atlas-agent-peer-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Manual code reviews are time-consuming, prone to human error, and often miss critical flaws, leading to bugs in production. This Skill eliminates those pain points by acting as an unyielding quality gate, catching issues before they impact users.

Core Features & Use Cases

  • Adversarial Quality Gate: Automatically scrutinizes code for architectural violations, security risks, and non-compliance with project standards.
  • Comprehensive Validation: Runs type checks, tests, linting, and platform-specific verifications to ensure code integrity.
  • Evidence-Based Verdicts: Provides clear REJECTED, CONDITIONAL PASS, or PASS verdicts with specific evidence (code snippets, command output) for every finding.
  • Use Case: Before merging any pull request, invoke this Skill to get an unbiased, thorough review that identifies all potential issues, saving developer time and preventing production bugs.

Quick Start

Review my pull request for the new user authentication module. Perform an adversarial security and architectural review.

Frequently Asked Questions about atlas-agent-peer-reviewer

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

FAQPage Schema
How do I enforce code quality gates automatically in my CI/CD pipeline?▼

Code quality gates automatically block low-quality changes by running adversarial reviews on pull requests and patches before merge. This Skill scrutinizes code for architectural violations, security risks, and standard non-compliance, returning clear REJECTED, CONDITIONAL PASS, or PASS verdicts with evidence—catching issues before production without manual review overhead.

Can automated code review catch security vulnerabilities and architectural flaws?▼

Yes. Automated code review performs comprehensive validation including type checks, tests, linting, and security verification to identify architectural violations and security risks. Evidence-based verdicts include specific code snippets and command output for every finding, ensuring thorough coverage beyond surface-level issues.

How do I configure project-specific code review rules and rejection criteria?▼

Project-specific rules load from .atlas/conventions.md and .atlas/rejection-criteria.md files in your repository. These files define your quality standards and rejection thresholds, allowing the automated review to enforce your project's unique architectural patterns and compliance requirements.

Does automated code review work with generic software projects across different tech stacks?▼

Yes. This approach applies to generic software projects across stacks and CI/CD pipelines, handling reviews of pull requests, patches, and configuration changes. It adapts to your project's specific conventions and rejection criteria rather than enforcing one-size-fits-all rules.

What's the difference between automated adversarial review and standard code linting?▼

Adversarial review goes beyond linting by combining type checks, tests, linting, and platform-specific verifications into an evidence-based quality gate with explicit verdicts. It enforces architectural standards and project conventions—not just syntax—preventing bugs that reach production.

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