Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
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。
Writes an explicit entry to auto-memory when something is important enough that you don't want to rely on Claude noticing it automatically.
/si:remember <what to remember>
/si:remember "This project's CI requires Node 20 LTS — v22 breaks the build"
/si:remember "The /api/auth endpoint uses a custom JWT library, not passport"
/si:remember "Reza prefers explicit error handling over try-catch-all patterns"
| Situation | Example |
|---|---|
| Hard-won debugging insight | "CORS errors on /api/upload are caused by the CDN, not the backend" |
| Project convention not in CLAUDE.md | "We use barrel exports in src/components/" |
| Tool-specific gotcha | "Jest needs --forceExit flag or it hangs on DB tests" |
| Architecture decision | "We chose Drizzle over Prisma for type-safe SQL" |
| Preference you want Claude to learn | "Don't add comments explaining obvious code" |
Extract from the user's input:
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
grep -ni "<keywords>" "$MEMORY_DIR/MEMORY.md" 2>/dev/null
If a similar entry exists:
Append to the end of MEMORY.md:
- {{concise fact or pattern}}
Keep entries concise — one line when possible. Auto-memory entries don't need timestamps, IDs, or metadata. They're notes, not database records.
If MEMORY.md is over 180 lines, warn the user:
⚠️ MEMORY.md is at {{n}}/200 lines. Consider running /si:memory-review to free space.
If the knowledge sounds like a rule (imperative, always/never, convention):
💡 This sounds like it could be a CLAUDE.md rule rather than a memory entry.
Rules are enforced with higher priority. Want to /si:promote it instead?
✅ Saved to auto-memory
"{{entry}}"
MEMORY.md: {{n}}/200 lines
Claude will see this at the start of every session in this project.
/si:promote to write directly to CLAUDE.md~/.claude/CLAUDE.md for global rulespnpm build, tests with pnpm test:e2e"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。