Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations. Use when the user runs /si:memory-status or asks how full or healthy the agent memory is.
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。
Quick overview of your project's memory state across all memory systems.
/si:memory-status # Full dashboard
/si:memory-status --brief # One-line summary
# Auto-memory directory
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
# Count lines in MEMORY.md
wc -l "$MEMORY_DIR/MEMORY.md" 2>/dev/null || echo "0"
# List topic files
ls "$MEMORY_DIR/"*.md 2>/dev/null | grep -v MEMORY.md
# CLAUDE.md
wc -l ./CLAUDE.md 2>/dev/null || echo "0"
wc -l ~/.claude/CLAUDE.md 2>/dev/null || echo "0"
# Rules directory
ls .claude/rules/*.md 2>/dev/null | wc -l
| Metric | Healthy | Warning | Critical |
|---|---|---|---|
| MEMORY.md lines | < 120 | 120-180 | > 180 |
| CLAUDE.md lines | < 150 | 150-200 | > 200 |
| Topic files | 0-3 | 4-6 | > 6 |
| Stale entries | 0 | 1-3 | > 3 |
For each MEMORY.md entry that references a file path:
# Verify referenced files still exist
grep -oE '[a-zA-Z0-9_/.-]+\.(ts|js|py|md|json|yaml|yml)' "$MEMORY_DIR/MEMORY.md" | while read f; do
[ ! -f "$f" ] && echo "STALE: $f"
done
📊 Memory Status
Auto-Memory (MEMORY.md):
Lines: {{n}}/200 ({{bar}}) {{emoji}}
Topic files: {{count}} ({{names}})
Last updated: {{date}}
Project Rules:
CLAUDE.md: {{n}} lines
Rules: {{count}} files in .claude/rules/
User global: {{n}} lines (~/.claude/CLAUDE.md)
Health:
Capacity: {{healthy/warning/critical}}
Stale refs: {{count}} (files no longer exist)
Duplicates: {{count}} (entries repeated across files)
{{if recommendations}}
💡 Recommendations:
- {{recommendation}}
{{endif}}
/si:memory-status --brief
Output: 📊 Memory: {{n}}/200 lines | {{count}} rules | {{status_emoji}} {{status_word}}
/si:memory-review to promote or clean up./si:memory-review now./si:memory-status --brief as a quick check anytime/si:memory-review to identify promotion candidates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
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
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。
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
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。
When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
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。
Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
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。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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。
Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
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。