Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Validate planned changes against local framework rules and policy guardrails before implementation or creation.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Evaluate a design/plan against framework policy and rule constraints before execution. Use this for regulated or high-risk changes.
.claude/rules/Assess against:
Return one policy decision:
PASS: policy-alignedCONDITIONAL: allowed with required mitigationsFAIL: not policy-compliantUse this output shape:
{
"decision": "PASS|CONDITIONAL|FAIL",
"policyFindings": ["..."],
"requiredMitigations": [],
"evidencePaths": ["..."],
"recommendedNextStep": "..."
}
For CONDITIONAL and FAIL, include precise remediation tasks and ownership (agent type).
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Implementing code during compliance check | Conflates assessment with implementation | Assess only; implementation happens separately after PASS |
| Skipping compliance for "small" changes | Small changes introduce policy violations silently | Run compliance check proportionally for all HIGH/EPIC work |
| Reporting PASS on partial compliance | Masks unresolved violations; gives false confidence | Report CONDITIONAL with specific remediation required |
| Vague violation reports without remediation | Violations aren't fixed without clear next steps | Include agent, task, and target file for every FAIL/CONDITIONAL |
| Only checking once before implementation | Post-change compliance drift goes undetected | Recheck compliance after major implementation changes |
Record recurring policy drift patterns in .claude/context/memory/issues.md and stabilized controls in .claude/context/memory/decisions.md.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。