Scope-aware GitHub PR review with user-friendly tone and trust tier validation
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Review pull requests with correct AQE scope boundaries, clear communication, and actionable feedback.
<pr-number> — GitHub PR number to review. If omitted, prompt the user.gh pr diff <pr-number>
gh pr view <pr-number>
Read the complete diff and PR description. Do not skim — read every changed file.
Write a user-friendly summary of what changed and why:
For any skill changes, validate trust_tier assignments:
Check for:
gh pr review <pr-number> --body "review comments"
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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