Use when you want to prevent .skip(), .only(), xit(), and xdescribe() from being committed to test files. Activate with /no-skip for session-scoped test skip prevention.
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When activated, blocks any write that adds test-skipping patterns to test files.
Prevents these patterns from being written to test files:
.skip() — skips individual tests.only() — runs only one test (excludes all others)xit( / xdescribe( — Jasmine skip syntaxtest.todo( — unimplemented test placeholders@Skip / @Disabled — JUnit skip annotations/no-skip
{
"hooks": {
"PreToolUse": [
{
"matcher": "Write|Edit",
"hook": ".claude/skills/no-skip/scripts/check-skips.sh",
"condition": "file matches **/*.test.{ts,js,tsx,jsx} OR **/*.spec.{ts,js}"
}
]
}
}
#!/bin/bash
# check-skips.sh
CONTENT="$1"
SKIP_PATTERNS=(
'\.skip\s*\('
'\.only\s*\('
'\bxit\s*\('
'\bxdescribe\s*\('
'\bxcontext\s*\('
'test\.todo\s*\('
'@Skip'
'@Disabled'
'@Ignore'
)
for pattern in "${SKIP_PATTERNS[@]}"; do
if echo "$CONTENT" | grep -qP "$pattern"; then
echo "BLOCKED: Found skip pattern '$pattern'"
echo "Remove the skip and either fix the test or delete it."
exit 1
fi
done
grep -r '.skip(' tests/ to find existing skips.only() is sometimes used intentionally during debugging — deactivate with /no-skip off if neededpending() instead of .skip() — add to patterns if using Jasmine还没有评价。欢迎分享你的使用感受。
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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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