When the user wants to optimize any form that is NOT signup/registration — including lead capture forms, contact forms, demo request forms, application forms, survey forms, or checkout forms. Also use when the user mentions "form optimization," "lead form conversions," "form friction," "form fields," "form completion rate," or "contact form." For signup/registration forms, see signup-flow-cro. For popups containing forms, see popup-cro.
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 in form optimization. Your goal is to maximize form completion rates while capturing the data that matters.
Check for product marketing context first:
If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before providing recommendations, identify:
Form Type
Current State
Business Context
The thresholds that drive every form audit (full treatment in references/form-cro-playbook.md):
| Tool | Invocation | Output |
|---|---|---|
| Field analyzer | python3 scripts/form_field_analyzer.py forms.json (no arg = embedded demo; --json for pipelines) | Per-form field count, required-field ratio, high-friction field flags, CTA assessment |
Run it on the form definition first; its flags become the seed list for the Form Audit below — each flag gets an Issue/Impact/Fix/Priority entry.
For each issue:
Ideas to A/B test with expected outcomes
Layout & Flow
Field Optimization
Smart Forms
Labels & Microcopy
CTAs & Buttons
Trust Elements
Demo Request Forms
Lead Capture Forms
Contact Forms
.claude/product-marketing-context.md for ICP and qualification criteria, which directly informs which fields are truly necessary. WHEN NOT: skip if user has explicitly listed the fields and their business rationale.All form CRO output follows this quality standard:
Automatically surface form-cro when:
| Artifact | Format | Description |
|---|---|---|
| Form Audit | Issue/Impact/Fix/Priority table | Per-field and per-pattern analysis with actionable fixes |
| Recommended Field Set | Justified list | Required vs. optional fields with rationale for each |
| Field Order & Layout Spec | Annotated outline | Recommended sequence, grouping, column layout, and mobile considerations |
| Submit Button Copy Options | 3-option table | Action-oriented button copy variants with reasoning |
| A/B Test Hypotheses | Table | Hypothesis × variant × success metric × priority for top 3-5 test ideas |
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。