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。
Generate MLS-compliant property listing copy in five length variants (tagline, MLS-short ≤250 char, MLS-long ≤4000 char, marketing description ~600-800 words, social caption) PLUS schema.org RealEstateListing JSON-LD for SEO/AEO syndication..
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 generate property listing copy that is MLS-format-correct, character-limit-compliant, fair-housing-safe, and syndication-ready (Zillow, Realtor.com, Redfin, Apple Maps, Google). Five length variants from one input pass plus JSON-LD for structured-data SEO.
Listing copywriting is the #1 most-adopted AI use case among real estate agents (multiple 2026 industry surveys). The most common failures are (a) violating character limits and getting auto-rejected by MLS systems and (b) accidentally using language that triggers a Fair Housing Act violation. Both are solved by construction.
Gather and verify before writing:
Recovery:
Before writing any copy, load the banned-pattern list and apply it as a hard filter on ALL output.
HUD-enforced banned categories (Fair Housing Act, 42 U.S.C. § 3604):
Patterns that trigger violations (NAR + HUD guidance):
The compliance gate runs on the FINAL output, not the intermediate drafts. If any banned pattern is detected:
VALIDATION: A red-team test fixture (tests/fair_housing_redteam.txt) contains 20 sample listings with known violations. The gate must catch ≥ 19 of 20.
FALLBACK: If a phrase is ambiguous (could be discriminatory in some contexts, not others), flag with a yellow "review required" rather than auto-rewrite.
Take the input facts and organize into a structured features dictionary:
hero_feature: "Renovated chef's kitchen with quartz waterfall island"
secondary_features:
- "Vaulted ceilings throughout main level"
- "Primary suite with walk-in closet and spa bath"
- "Fenced backyard with covered patio"
neighborhood:
- "1/2 mile to Whole Foods"
- "Walk Score 84"
- "Highly rated public schools (avoid naming specific schools in copy — fair housing risk)"
financial_highlights:
- "$8,400/year property taxes"
- "$320 HOA covers exterior + landscaping"
recent_updates:
- "New roof 2024"
- "HVAC replaced 2023"
The HERO feature gets prime placement in every length variant. Secondary features get rotated to avoid each variant reading identical.
VALIDATION: Hero feature appears in tagline, MLS-short, and MLS-long. Secondary features rotate. No feature appears more than twice in the same variant.
Generate each variant with strict character/word limits:
One sentence. Hero feature + neighborhood signal. Example: "Renovated 4-bed with chef's kitchen in walkable Westside neighborhood."
The "search results card" copy. Hook + 3 features + call to action. Example: "Light-filled 4-bed/3-bath with chef's kitchen, vaulted ceilings, and fenced yard. New roof and HVAC. Two-car garage. Move-in ready. See it before it's gone."
Full property description. Structure:
For the agent's website, brochures, social posts (long form). Same structure as Variant 3 plus:
Instagram/Facebook ready. Hook + 1 feature + 3 hashtags. Example: "Renovated kitchen, dreamy primary suite, fully fenced yard. Open house Saturday 1-3. #JustListed #YourCityRealEstate #DreamHome"
VALIDATION: Each variant under its character limit. No banned phrases from Phase 1. Each variant has the hero feature.
FALLBACK: If 4000-char MLS Long Remarks runs short on details (sparse input), do NOT pad — output what's substantive and note "Add: ___" placeholders.
Generate schema.json with the RealEstateListing schema for SEO + AEO (Answer Engine Optimization — surfaces in Google AI Overviews, ChatGPT real estate queries, Perplexity).
{
"@context": "https://schema.org",
"@type": "RealEstateListing",
"name": "4-Bedroom Home with Chef's Kitchen",
"url": "{listing_url}",
"datePosted": "{ISO_date}",
"address": {
"@type": "PostalAddress",
"streetAddress": "...",
"addressLocality": "...",
"addressRegion": "...",
"postalCode": "...",
"addressCountry": "US"
},
"geo": { "@type": "GeoCoordinates", "latitude": 0, "longitude": 0 },
"offers": {
"@type": "Offer",
"price": 0,
"priceCurrency": "USD",
"availability": "https://schema.org/InStock"
},
"numberOfBedrooms": 4,
"numberOfBathroomsTotal": 3,
"floorSize": { "@type": "QuantitativeValue", "value": 0, "unitCode": "FTK" },
"yearBuilt": 0,
"description": "{MLS_long_remarks}"
}
VALIDATION: JSON-LD passes Google's Rich Results Test (no required-field errors). Lat/long, if missing, are flagged as TODO (don't fabricate coordinates).
Final deliverable directory:
listing-{address-slug}/
├── README.md # how to publish where
├── tagline.txt
├── mls_short.txt # paste into MLS public remarks (short)
├── mls_long.txt # paste into MLS public remarks (long)
├── marketing.md # paste into agent website / brochure
├── social.txt # Instagram / Facebook caption
├── schema.json # add to listing page <script type="application/ld+json">
└── compliance_report.md # what was checked, anything flagged
The README explains exactly where each file goes and why.
Score 1–5:
If any < 4:
Append to ~/.claude/skills/mls-listing-craft/LEARNINGS.md:
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。
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.
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。