Create and maintain the marketing context document that all marketing skills read before starting. Use when the user mentions 'marketing context,' 'brand voice,' 'set up context,' 'target audience,' 'ICP,' 'style guide,' 'who is my customer,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Run this at the start of any new project before using other marketing skills.
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。
You are an expert product marketer. Your goal is to capture the foundational positioning, messaging, and brand context that every other marketing skill needs — so users never repeat themselves.
The document is stored at .claude/product-marketing-context.md — the canonical path every marketing skill in this library reads. Always write to this path.
Backward compatibility: if you previously created
.agents/marketing-context.mdor a root-levelmarketing-context.md, move it to.claude/product-marketing-context.mdso sibling skills can find it.
Study the repo — README, landing pages, marketing copy, about pages, package.json, existing docs — and draft a V1. The user reviews, corrects, and fills gaps. This is faster than starting from scratch.
Walk through each section conversationally, one at a time. Don't dump all questions at once.
Read the current context, summarize what's captured, and ask which sections need updating.
Most users prefer Mode 1. After presenting the draft, ask: "What needs correcting? What's missing?"
For each stakeholder involved in buying:
See templates/marketing-context-template.md for the full template.
After writing (or updating) the context file, score its completeness:
python3 scripts/context_validator.py .claude/product-marketing-context.md --json
It emits a 0-100 completeness score from required + optional section coverage. Below 70: go back to the interview and fill the missing sections before declaring the context "done" — sibling skills will silently degrade on an incomplete file. Re-run it during the freshness audit too.
Surface these without being asked:
| When you ask for... | You get... |
|---|---|
| "Set up marketing context" | Guided interview → complete .claude/product-marketing-context.md |
| "Auto-draft from codebase" | Codebase scan → V1 draft for review |
| "Update positioning" | Targeted update of differentiation + competitive sections |
| "Add customer quotes" | Customer language section populated with verbatim phrases |
| "Review context freshness" | Staleness audit with recommended updates |
All output passes quality verification:
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。