Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Audit memory retrieval quality (drift, staleness, citation-groundedness) and produce remediation backlog.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Audit the memory system as a unified retrieval layer (STM/MTM/LTM files + index + spawn citation outcomes).
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Auditing without a baseline | Cannot distinguish regression from steady-state; all findings are ambiguous | Snapshot current metrics at session start; compute delta against the previous run |
| Closing findings without re-check | Produces false-positive resolution; degradation persists silently behind green metrics | Re-run the specific retrieval query after each remediation; close only on confirmed green metric |
| Skipping citation groundedness | Citation failures are the leading cause of agent hallucination; missing this check omits the highest-severity defect class | Include citation_coverage and grounded_ratio metrics in every audit report |
| Full-mode audit on every spawn | Full audit is expensive; running it unconditionally inflates cost and slows workflows | Use --mode summary for routine checks; reserve --mode full for scheduled or triggered audits |
| Auditing STM only | MTM/LTM corruption is invisible in STM-only scans; stale LTM entries contaminate future sessions | Sample all three tiers: STM (current session), MTM (last 10 sessions), LTM (permanent summaries) |
Before starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
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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