AI-Powered Code Review Specialist workflow skill. Use this skill when the user needs You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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name
code-review-ai-ai-review
description
AI-Powered Code Review Specialist workflow skill. Use this skill when the user needs You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/code-review-ai-ai-review from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
AI-Powered Code Review Specialist You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Context, Requirements, Architecture Analysis, Security Vulnerability Detection, Performance Review, Review Comment Generation.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Working on ai-powered code review specialist tasks or workflows
Needing guidance, best practices, or checklists for ai-powered code review specialist
The task is unrelated to ai-powered code review specialist
You need a different domain or tool outside this scope
Use when the request clearly matches the imported source intent: You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.
Parse diff to determine modified files and affected components
Match file types to optimal static analysis tools
Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
Imported Workflow Notes
Imported: Instructions
Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.
If detailed examples are required, open resources/implementation-playbook.md.
Imported: Automated Code Review Workflow
Initial Triage
Parse diff to determine modified files and affected components
Match file types to optimal static analysis tools
Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
Classify change type: feature, bug fix, refactoring, or breaking change
Multi-Tool Static Analysis
Execute in parallel:
CodeQL: Deep vulnerability analysis (SQL injection, XSS, auth bypasses)
Semgrep: Organization-specific rules and security policies
Snyk/Dependabot: Supply chain security
GitGuardian/TruffleHog: Secret detection
AI-Assisted Review
# Context-aware review prompt for Claude 4.5 Sonnet
review_prompt = f"""
You are reviewing a pull request for a {language}{project_type} application.
**Change Summary:** {pr_description}
**Modified Code:** {code_diff}
**Static Analysis:** {sonarqube_issues}, {codeql_alerts}
**Architecture:** {system_architecture_summary}
Focus on:
1. Security vulnerabilities missed by static tools
2. Performance implications at scale
3. Edge cases and error handling gaps
4. API contract compatibility
5. Testability and missing coverage
6. Architectural alignment
For each issue:
- Specify file path and line numbers
- Classify severity: CRITICAL/HIGH/MEDIUM/LOW
- Explain problem (1-2 sentences)
- Provide concrete fix example
- Link relevant documentation
Format as JSON array.
"""
Model Selection (2025)
Fast reviews (<200 lines): GPT-4o-mini or Claude 4.5 Haiku
Deep reasoning: Claude 4.5 Sonnet or GPT-5 (200K+ tokens)
Code generation: GitHub Copilot or Qodo
Multi-language: Qodo or CodeAnt AI (30+ languages)
30+ language support with language-specific linters
Actionable review comments with severity and fix examples
DORA metrics tracking for review effectiveness
Quality gates preventing low-quality code
Auto-test generation via Qodo/CodiumAI
Use this tool to transform code review from manual process to automated AI-assisted quality assurance catching issues early with instant feedback.
Imported: Context
Multi-layered code review workflows integrating with CI/CD pipelines, providing instant feedback on pull requests with human oversight for architectural decisions. Reviews across 30+ languages combine rule-based analysis with AI-assisted contextual understanding.
Examples
Example 1: Ask for the upstream workflow directly
Use @code-review-ai-ai-review to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @code-review-ai-ai-review against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @code-review-ai-ai-review for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @code-review-ai-ai-review using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/code-review-ai-ai-review, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: Requirements
Review: $ARGUMENTS
Perform comprehensive analysis: security, performance, architecture, maintainability, testing, and AI/ML-specific concerns. Generate review comments with line references, code examples, and actionable recommendations.
Imported: Architecture Analysis
Architectural Coherence
Dependency Direction: Inner layers don't depend on outer layers
SOLID Principles:
Single Responsibility, Open/Closed, Liskov Substitution
Interface Segregation, Dependency Inversion
Anti-patterns:
Singleton (global state), God objects (>500 lines, >20 methods)
