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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Use when debugging a failing test or runtime error with hypothesis-driven investigation, autonomous command validation, and systematic root cause elimination.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Autonomous hypothesis-driven debugging against real data. No guessing, no simulating.
<symptom> — Description of the bug or unexpected behavior. If omitted, prompt the user.Run the exact command that shows the bug. Capture and display the REAL output. Confirm the bug is visible.
If the bug cannot be reproduced, stop and explain what was tried.
For each iteration:
sqlite3 [db path] '.tables' then SELECT COUNT(*) FROM [table])Do NOT make code changes until you have a confirmed root cause.
Important checks:
Make the minimal targeted fix. Explain:
Before applying, grep for ALL instances of the problematic pattern across the entire codebase.
Run the SAME reproduction command from Phase 1. The output must now show correct values. If it doesn't, go back to Phase 2.
Show before/after output comparison.
npm test
Run the full test suite. If tests fail, fix them before committing.
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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.
中文简介正在准备中。当前显示原文说明。
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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