Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Validate whether a proposed new artifact is feasible in the current stack before creator workflows run.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Run a fast preflight feasibility check before creating a new agent/skill/workflow/hook/template/schema. This prevents low-value or impossible creator runs.
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Returning PASS without running the duplication check | Creates duplicate artifacts that split agent traffic and inflate catalogs | Always query catalog + registry + filesystem before PASS |
| Returning BLOCK without remediation tasks | Calling agent stalls with no path forward | Include nextActions with specific agents/skills to unblock |
| Skipping the security/creator boundary check | Creator paths may be blocked by governance hooks; silently bypassing them causes runtime failures | Always verify creator skill chain is reachable before PASS |
| Treating WARN as informational only | WARN caveats are not surfaced to the user; creation proceeds with unresolved risks | WARN must be acknowledged explicitly by the caller in its task metadata |
| Running creation steps inside the gate skill | Violates separation of concerns; gate outputs can't be validated independently | Gate outputs only the decision JSON; delegate creation to creator skills |
Use the shared duplicate detection library:
const { checkDuplicate } = require('.claude/lib/creation/duplicate-detector.cjs');
const result = checkDuplicate({
artifactType: artifactType, // from Step 1 classification
name: proposedName,
description: proposedDescription,
});
if (result.decision === 'EXACT_MATCH') {
return {
gate: 'BLOCK',
reason: `Artifact exists at ${result.matchedPath}. Use the ${artifactType}-updater skill instead.`,
};
}
if (result.decision === 'REGISTRY_MATCH') {
return {
gate: 'WARN',
reason: `"${proposedName}" found in ${result.message} but file may be missing. Investigate.`,
};
}
if (result.decision === 'SIMILAR_FOUND') {
return {
gate: 'WARN',
reason: `Similar artifacts found: ${result.candidates.map(c => `${c.name} (${(c.score * 100).toFixed(0)}%)`).join(', ')}. Confirm creation is intentional.`,
};
}
return { gate: 'PASS' };
The 3 detection layers (filesystem, registry/catalog, fuzzy/semantic) are handled internally by the library. See .claude/lib/creation/duplicate-detector.cjs for details.
Run these checks with concrete evidence:
Return one status:
PASS: creation is feasible nowWARN: feasible with clear caveatsBLOCK: not feasible; must resolve blockers firstUse this output shape:
{
"status": "PASS|WARN|BLOCK",
"artifactType": "agent|skill|workflow|hook|template|schema",
"artifactName": "example-name",
"evidence": ["..."],
"blockers": [],
"nextActions": ["..."]
}
If BLOCK, include concrete remediation tasks and recommended target agents.
If PASS or WARN, include exact creator skill chain to run next.
Record feasibility patterns and recurring blockers to .claude/context/memory/learnings.md.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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