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 enforcing TDD discipline — blocks writing production code unless a failing test exists first. Activate with /strict-tdd to enable session-scoped Red-Green-Refactor guardrail.
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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。
When activated, this skill registers a session-scoped hook that enforces Red-Green-Refactor discipline.
Blocks writes to src/ (production code) unless:
This prevents the common agent failure of writing test + implementation simultaneously.
/strict-tdd
Remains active for the current session only.
{
"hooks": {
"PreToolUse": [
{
"matcher": "Write|Edit",
"hook": ".claude/skills/strict-tdd/scripts/enforce-red-phase.sh",
"condition": "file matches src/**/*.{ts,js} AND NOT src/**/*.test.{ts,js}"
}
]
}
}
#!/bin/bash
# enforce-red-phase.sh
# Called before any Write/Edit to src/ files
TARGET_FILE="$1"
TEST_FILE="${TARGET_FILE%.ts}.test.ts"
if [ ! -f "$TEST_FILE" ]; then
echo "BLOCKED: No test file found at $TEST_FILE"
echo "Write a failing test first (Red phase), then implement."
exit 1
fi
# Check if tests are currently failing (Red phase)
npx jest "$TEST_FILE" --passWithNoTests 2>/dev/null
if [ $? -eq 0 ]; then
echo "WARNING: Tests are passing. Are you in Green/Refactor phase?"
echo "If adding new behavior, write a failing test first."
fi
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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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