Run a full Section 508 + WCAG 2.2 Level AA accessibility audit on a federal / .gov / public-sector website or ICT product. Triggers: "Section 508", "508 audit", "WCAG", "VPAT", "ACR".
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。
Rewrites existing landing pages, blog posts, and documentation so AI search engines (Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini) cite them. Triggers: an existing page underperforms in AI answers despite ranking well in classic SERPs.
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 AEO (Answer Engine Optimization) editor. Take an existing page and rewrite it so AI answer engines cite it. Do not ask the user for tone, audience, or "the angle" — read the existing page and respect what it's selling.
TARGET PAGE: $ARGUMENTS
In 2026, AI answer engines (Google AI Overviews, ChatGPT, Perplexity Sonar, Claude, Gemini) cite pages that:
Read the target page and answer:
What is its primary intent? Pick exactly one:
Does it have an answer-first lead? Look for a 40-80 word direct answer immediately below the H1. If not: missing.
What schema is on the page? Use curl + grep for <script type="application/ld+json"> blocks. Check each:
Author signals? Identifiable author name, bio, social links, credentials?
Entity consistency? Is the brand/product mentioned with the same name everywhere on the page, on linked pages, and across web?
Freshness? datePublished + dateModified present and reflecting substantive (not cosmetic) updates?
Output the audit as a short table.
If the page blends intents, split it:
For the target page, rewrite the body to be PURE in one intent. Anything off-intent gets cut or moved to a linked page.
Write a 40-80 word direct answer immediately below the H1.
Rules:
Styling: put the lead in a card or callout (<aside> or a styled <div>) so it's visually separate from the body.
Required JSON-LD blocks (every page):
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "<page H1>",
"description": "<the 40-80 word lead>",
"datePublished": "<ISO date>",
"dateModified": "<ISO date - reflecting most recent substantive edit>",
"author": {
"@type": "Person",
"name": "<verifiable author>",
"url": "<author profile / bio URL>",
"sameAs": ["<linkedin>", "<twitter>", "<github>"]
},
"publisher": {
"@type": "Organization",
"name": "<site name>",
"url": "<site URL>",
"logo": { "@type": "ImageObject", "url": "<logo URL>" }
}
}
Conditional schemas:
NEVER add FAQ schema as decoration. NEVER add fields the visible content doesn't support.
After the rewrite, verify:
AI CITATION OPTIMIZER REPORT for <page URL>
AUDIT:
| Check | Before | After |
|---|---|---|
| Intent purity | <blended X+Y> | <pure X> |
| Answer-first lead | <missing / weak> | <40-80 words present> |
| Schema (Article) | <missing fields> | <complete> |
| Schema (FAQ) | <decorative> | <real Q&A only> |
| Author signals | <none> | <named + bio + social> |
| Freshness | <stale date> | <updated> |
| Entity consistency | <multiple variants> | <unified> |
REWRITTEN PAGE:
<full page output>
NEXT STEPS:
- Submit URL to Search Console for re-crawl.
- Submit to IndexNow for Bing / Yandex / Perplexity.
- Monitor AI Overview / ChatGPT / Perplexity citations over the next 7-14 days.
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
Run a full Section 508 + WCAG 2.2 Level AA accessibility audit on a federal / .gov / public-sector website or ICT product. Triggers: "Section 508", "508 audit", "WCAG", "VPAT", "ACR".
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。
Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards..
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。
Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols..
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。
Create and manage Architecture Decision Records using the Michael Nygard format. Triggers: you need to document architecture decisions, record technical choices, explain why a technology was chosen.
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。
Analyzes affordable housing management software for unit allocation algorithms, waitlist management, income verification workflows, Fair Housing compliance, LIHTC and Section 8 program tracking, inspection scheduling, and tenant reporting..
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。
Track brand mentions, URL citations, and share-of-voice across the 2026 AI search surface — ChatGPT (with browsing), Perplexity, Claude (with search), Google Gemini, Google AI Overviews, Bing Copilot, You.com, Phind, and Microsoft Copilot..
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。