AI for Real Estate

Turn a residential or commercial lease (PDF, scan, or pasted text) into a clean abstract: a structured deal record with paragraph citations, a CAM/NNN reconciliation worksheet (commercial only), an SNDA / estoppel / assignment exposure flag set, and a triaged anomaly list — in one pass — so the agent, asset manager, or buyer's broker can read the lease in five minutes and know exactly where the financial and legal exposure lives.

Produce a concise, data-first market snapshot — weekly, monthly, or on-demand for a specific neighborhood, price band, or property type — that translates raw MLS numbers into a plain-English narrative an agent can paste into a client email, newsletter, social post, listing-or-buyer conversation, or 1-pager PDF. Distinct from `cma-presentation-generator.md`: this is a quick market-pulse summary, not a full subject-property valuation presentation. Distinct from `neighborhood-report-generator.md`: this is a market-segment summary for a known geography, not a full lifestyle-and-amenities buyer-facing area overview. The output is six aligned artifacts: a Market Snapshot Header, a Key Metrics Table with current-vs-comparison-period delta, a Market Diagnosis based on a four-band MOI ladder aligned with `cma-presentation-generator.md`, two or three Headline Stories with stat → translation → implication, an audience-specific "What This Means" closer, and a Caveat block that names what the data does not capture (small sample, rate volatility, seasonality unfolding, segment-narrowness).

Parse a purchase offer (or a multi-offer batch) and produce a structured review that flags contingencies, evaluates financial strength, identifies timeline risks, surfaces non-standard terms, computes net-to-seller, and — when more than one offer is on the table — produces a side-by-side comparison matrix the listing agent can present to the seller in a 60-second skim. The skill is **state-aware** (CA CAR-RPA, TX TREC One-to-Four, FL FAR-BAR AS-IS / FAR-7 Standard, WA NWMLS Form 21, MA GBREB / MAR, NJ Realtors Form, IL Multi-Board 7.0, GA GAMLS, DC/MD GCAAR, NY REBNY, AZ AAR, NC NCAR Form 2-T) and **brokerage-aware** (Compass / eXp / Side / KW / Anywhere / BHHS / Sotheby's / Douglas Elliman / Howard Hanna / @properties concession + dual-agency + commission-display rules), so the same skill produces a usable review whether the agent works in Highland Park, Houston, Hoboken, or Hartford. The review is analytical, not legal advice; it surfaces what the seller and listing agent need to discuss before counter, accept, or reject.

Turn a signed listing agreement into a coordinated, channel-by-channel launch package: a 14-day reverse-sequenced action plan, per-channel readiness checklists (MLS / Zillow / Realtor.com / brokerage site / Instagram / Facebook / email / signage), a fair-housing and compliance sweep, a Launch-Day Runsheet with hour-by-hour activations, and a 7-day post-launch monitoring plan — so the listing goes live on every channel within the same hour, the photography and copy reinforce the same engagement-driving features, and no missed disclosure or syndication setting becomes a re-list event two weeks in.

Turn a newly-executed residential purchase agreement (PDF, scan, or pasted text) into a clean deal record, a signature/initial audit, an anomaly report, and a 48-hour action list — in one pass — so the deal moves from mutual acceptance into transaction management with no missed deadlines and no surprises at closing.

Generate a full 30-day cross-platform social media calendar for a real estate agent — with hooks, captions, CTAs, Reels concepts, carousel ideas, and a daily posting schedule — tuned to the agent's market, brand voice, the current month's seasonal/market themes, **the agent's stated time budget** (the cadence-honesty constraint), **the agent's active listing inventory** (which slots get filled by which `listing-video-workflow.md` six-cut and which `listing-content-multiplier.md` 10-piece package), and **the agent's eight-platform aspect-and-duration matrix** (Instagram feed + Reels, TikTok, YouTube Shorts + Long, Facebook, LinkedIn, Zillow short-form, MLS short-form). v2.0 deconflicts Reel-vs-Short cannibalization (per `listing-video-workflow.md`'s posting cadence guidance), enforces the D5 cadence-honesty constraint from `agent-discoverability-audit.md`, and produces a calendar an agent could execute without further strategic decisions.

Audit a real estate agent as an *entity* across the AI discovery layer — not a listing page, not a single bio, but the cross-surface footprint that determines whether ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, or Bing Copilot will name this agent when a buyer or seller asks "who's the best agent for [my situation] in [my market]?" Produces an **Off-Page Discoverability Score** (six audit surfaces × six AI engines), a **Content-Engine Taste Score** (five-dimension anti-slop rubric for AI-assisted personal-brand content), a **Specialization Ladder placement** (five tiers from Generalist → Luxury), a **Brand-Voice + Voice-Clone Asset Inventory** (three-tier discipline shared with `listing-video-workflow.md`), a **6-check compliance sweep**, and a **90-day Action Plan** sequenced across three phases (Stabilize 0–30, Differentiate 30–60, Compound 60–90). The skill is tool-agnostic — it governs the methodology decisions before HeyGen, ElevenLabs, Regen IO, Luxury Presence, Lofty, Sierra Interactive, or any vendor production.

Produce a complete, multi-touch nurture sequence — personalized to a specific buyer's search criteria, timeline, qualification status, and lead source — with ready-to-send messages across multiple channels (SMS, email, voicemail, handwritten note), a cadence optimized for their urgency tier, and branching paths for common responses and non-responses.

Turn a raw set of comps and a subject property into a seller-ready (or buyer-ready) Comparative Market Analysis with (a) a defensible price band, (b) a one-page narrative that anchors the seller in today's market rather than the Zestimate they saw last night, (c) a buyer-pool math block that ties price to expected days-on-market, and (d) a paired talking-points script the agent can actually read from at the listing appointment. The skill exists because most agent CMAs fail in one of three predictable ways: they recite every comp with equal weight (so the seller doesn't know which comp matters), they give a single price point (which the seller either anchors above or counter-negotiates below), or they justify the number with vibes instead of adjustments (so the price collapses at the first seller objection).

Produce a structured, conversational qualification script (usable by a live agent, ISA, voicemail follow-up, chatbot, or email sequence) that reliably separates serious buyers and sellers from browsers — and outputs a 0–100 lead score with a routing recommendation — using an extended BANT framework adapted for real estate.

Rewrite and restructure a listing page, neighborhood guide, agent bio, market report, or process article so it is the source that answer engines (ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini, Bing Copilot) cite when buyers and sellers ask questions — not just a page that ranks in classic blue-link search. The output is a drop-in content block with an entity-dense AEO Lede, an explicit Q&A surface, a Key Facts table, JSON-LD structured-data blocks, an Authoritative Sources footnote stack, an Entity Glossary, an `llms.txt` snippet for crawler guidance, an engine-specific tactics ledger (per surface: ChatGPT / Perplexity / Google AI Overviews / Claude / Gemini / Bing Copilot), an 8-check fair-housing + advertising compliance sweep, and an AEO Readiness Score on a five-dimension rubric (Entity Density / Answerability / Citation Density / Structured Data / Author Authority).

Transform a single property listing into a complete, multi-platform marketing content package — 10+ ready-to-publish assets spanning social posts, Reels/TikTok scripts, email campaigns, carousels, blog content, and buyer-direct messaging — without rewriting from scratch for each channel.

Generate MLS-ready property descriptions that are compelling, compliant with fair housing guidelines, optimized for search, and tailored to the target buyer persona — all from basic property details, agent notes, and optional photos.

Take a property's raw feature inventory — what's actually in the house, on the lot, and in the neighborhood — and produce a research-backed prioritization that tells the agent exactly which three features to lead with, which five to support with, and which to deemphasize or omit. The output is not the listing copy itself; it is the strategic hand-off that upstream-feeds `listing-description-writer.md`, `listing-content-multiplier.md`, and `listing-aeo-optimizer.md` so each downstream skill leads with features that measurably drive buyer engagement (views, saves, shares, tour requests) rather than generic real-estate filler. The skill exists because April 2026 research (Zillow's 600-feature engagement analysis published 4/16/2026) confirmed that the features a listing leads with can swing daily engagement 10–20% — a compounding effect over the critical first-7-day window when engagement velocity correlates with sale price and days-on-market.

Turn a raw listing — photos, feature inventory, price band, target buyer, and the agent's brand voice — into a tool-agnostic, compliance-ready video production plan that a photo-to-video AI (Editora, AutoReel, VideoTour.AI, Opus, Amplifiles, Property Video AI, vProp, Pixlmob, PhotoAIVideo, Zyka, CloudPano, Delta Create Studio, etc.), a human videographer, or the agent's own phone can execute without guesswork. The skill produces a six-cut video package (Flagship / Reel / Short / Teaser / Just-Listed / Still-Available Refresh), a shot-ordered storyboard keyed to the photo set, a per-cut voiceover script with timestamps and on-screen text, an end-card and watermark spec, a music-licensing ladder, a platform-aspect-duration matrix covering eight surfaces, and a seven-check compliance sweep. The skill exists because April 2026 was the week listing video stopped being optional — Editora's 4/22 launch, Delta Create Studio's 4/22 DeltaNET release, and the concurrent maturation of at least ten vendor-neutral photo-to-video AI tools created a category moment without an accompanying methodology. The repo has `listing-content-multiplier.md` (which produces a 30-second Reel *script*), `listing-description-writer.md` (text), and `listing-feature-engagement-optimizer.md` (feature prioritization) — none govern the end-to-end video production workflow, and none handle the novel compliance surface that AI voice-cloning, AI motion on stills, and AI-generated avatar narration now create.

Produce the exact words — opener, pivot, ask, close, objection rebuttals, and the follow-up written recap — for a specific real-estate negotiation moment. The output is stage-aware (pre-offer, offer, acceptance, inspection, appraisal, loan contingency, close) and side-aware (listing, buyer, dual / transaction-broker), and carries a compliance sweep so the agent does not burn credibility, violate fair housing, or misrepresent material facts during a high-stakes conversation. The skill exists because agents do not lose deals on strategy — they lose deals in the 90 seconds after a seller says "that's a hard no on the price" or a buyer says "we're walking unless they cover the roof." This skill gives the agent a drafted first move *and* the second-and-third moves the counterparty is likely to try.

Mine an existing CRM or past-client database for hidden seller opportunities by scoring every homeowner contact on listing likelihood over the next 0–12 months — and, once a contact has converted from "in the database" to "in active dialogue," continue scoring them through the full pre-listing → post-listing → mid-marketing → post-pending lifecycle so the agent's seller pipeline operates as a continuous prioritization layer rather than a one-shot quarterly scrub. The skill produces a ranked working list with a 0–100 intent score, a stage-aware predicted listing window, a personalized outreach angle, and a recommended next touch — using the same scoring backbone whether the contact is a database row, a listing-appointment Pass 1 dictation from `client-conversation-intelligence.md`, or a mid-marketing seller showing soft re-engagement signals. Two-pass staged-input pattern (Pass 1 Fast Triage / Pass 2 Deep Analysis) mirrors `annual-business-plan-builder.md` and `client-conversation-intelligence.md` so an agent can ship an 80%-useful list from a CSV alone, then layer in enrichment to refine.

Transform a single client conversation — recorded call, showing debrief, in-person meeting, listing appointment, post-inspection walkthrough, or a voice/text note dictated in the car on the way home — into a structured memory, a coach's debrief, and a ready-to-send follow-up. The output is six aligned artifacts: a one-paragraph CRM-paste summary, a 5–9-bullet key-takeaways list, a Stated-vs-Implied Preferences map with evidence citations, a stage-specific Missed-Opportunity Audit, a drafted follow-up message in the agent's voice, and a private-to-agent coaching block. The skill solves the well-documented forgetting curve — people lose roughly half of what's said in a conversation within an hour — so nothing that matters to the client gets lost by the next day, and the agent walks into the next touch already remembering what the client cared about.

Produce a buyer-ready neighborhood profile that is specific, data-grounded, and fair-housing-compliant — the opposite of the generic "great schools, walkable, friendly community" report that fails out-of-area buyers and exposes the agent. Output a structured 8-section report plus an AEO-ready Q&A block (so the same asset earns citations in buyer-facing LLMs), with every data claim either (a) tied to a verifiable source, (b) clearly flagged as agent observation, or (c) omitted. The skill exists because neighborhood reports are the single highest-exposure deliverable in a buyer's-agent toolkit — one fair-housing slip ("this area has a mostly [demographic] community," "mostly [religion] churches nearby," "a growing [ethnicity] population," "quiet neighborhood" used as a demographic proxy) can trigger state RE commission review and brokerage E&O exposure. This skill makes the compliant version easier to produce than the non-compliant version.

Generate individualized post-open-house follow-up emails for each visitor — tiered by interest level, personalized from sign-in and agent notes, and paired with a clear next-step CTA — so that warm prospects get a concierge touch while casual browsers get a light, respectful follow-up that leaves the door open for later.

Convert a messy voice note, quick text, or one-line Slack message from a real estate agent into a structured work order that a delegate (human VA, AI assistant, TC, marketing coordinator, or an agentic workflow) can execute end-to-end without a follow-up clarification. The output is a one-screen brief: named deliverables, explicit assumptions, missing-input flags, deadline, channel, and a pre-delivery QC checklist.

Build the agent's and the brokerage's inbound-facing defense against AI-enabled fraud — voice-cloned "urgent" calls from the seller, deepfake video messages from a title officer, AI-written spoofed emails from "the lender," AI-generated identity documents on a buyer pre-qualification, AI-impersonated chat threads pretending to be an MLS admin or a cooperating agent. Produce, in one pass, a tailored defense package for a given transaction or brokerage: a transaction-level threat map, a four-tier verification ladder, a set of original live-challenge prompts a deepfake is unlikely to pass, a passphrase protocol, a wire-instruction-change standard operating procedure, a client-facing one-pager, a brokerage training cadence, and — if a suspected-fraud incident has already started — a time-boxed incident response checklist. The skill exists because the compliance stack (`ai-marketing-compliance-audit.md`) governs *outbound* AI content; nothing in the repo covers *inbound* AI impersonation. April 2026 industry coverage crystallized the gap: FBI reported $275M in real-estate-related cybercrime losses in 2025, Business Email Compromise ranked #2 at $3.04B, voice-clone and deepfake-video scams are now routinely reported against closings, and 60%+ of deepfake attempts cluster in the 72 hours before wire instructions are sent. The playbook treats inbound fraud as a predictable operational risk with predictable choke points, not an exotic event.

Audit AI-generated or AI-assisted real estate marketing deliverables — listing descriptions, virtual-staging photos, 3D renders, AI-written social posts, AI-drafted emails, AI-cloned voiceovers, AI-authored neighborhood guides, AI-summarized CMAs, AI chatbot transcripts — against the 2026 regulatory stack. Produce a one-pass report that tells the agent exactly what disclosures are missing, what language must be changed, what to keep, what to pull offline immediately, and who at the brokerage needs to be looped in. The skill exists because the compliance rules multiplied faster than the marketing stack in late 2025 and Q1 2026: NAR tightened its 2026 Code of Ethics, California AB 723 took effect 1/1/2026, Colorado's AI Act (SB 24-205) takes effect June 30, 2026 — with specific obligations for agents and brokerages using AI in housing decisions — and MLS-level virtual-staging labeling is now enforced with listing-takedown penalties in most major markets.

Turn an agent's prior-year production, financial picture, personal goals, and preferred lead sources into a complete, one-page annual business plan that breaks an income target down into transaction count, lead-source quotas, weekly activities, and a personal-expense floor — and then cascades those numbers into quarterly, monthly, and weekly tracking. Produces a 1-3-5-style plan (one big goal, three priorities, five per-priority tactics) plus a scorecard the agent will actually reopen week after week rather than shelve in January.

Compile every critical transaction detail — key dates, contacts, financial terms, contingencies, outstanding documents, brokerage compliance items, agentic-AI pre-pass artifacts, and the 48-hour action list — into a single hand-off brief the TC can manage from contract to close without chasing missing inputs. The skill is **state-aware** (CA CAR-RPA, TX TREC One-to-Four, FL FAR-BAR AS-IS / FAR-7, NY REBNY, IL Multi-Board 7.0, NJ Realtors, MA GBREB / MAR, WA NWMLS Form 21, GA GAMLS, NC NCAR Form 2-T, AZ AAR, DC/MD GCAAR — the same 12-jurisdiction matrix that `offer-review-checklist.md` v3.0 and `purchase-agreement-intake.md` already carry), **brokerage-aware** (Compass / eXp / Side / KW / Anywhere — Coldwell Banker, Century 21, Sotheby's, Better Homes, ERA, Corcoran / BHHS / Sotheby's standalone / Douglas Elliman / Howard Hanna / @properties), and **AI-pre-pass-aware** (the 4/27/2026 Compass-Anywhere disclosure that ⅔ of Anywhere brokerage documents flow through Anywhere AI before agent review changed the TC intake step — the brief now anticipates a pre-parsed agentic-AI artifact rather than a raw contract). The result is a brief the TC reads in 60 seconds and works from for the rest of the file.

Turn a rough note, bullet list, or dictated voice memo into a polished, send-ready real estate email — calibrated to the transaction stage, the recipient relationship, and the specific email type (buyer intro, seller check-in, lender coordination, inspection follow-up, closing update, attorney/title escalation, post-close thank-you, referral ask). Output is a fully drafted email with subject line, body, sign-off, and a short "why this email is written this way" note so the agent can decide to tweak or ship.

Turn raw meeting content (transcript, dictation, handwritten notes, or a combo) into a structured, CRM-ready meeting record calibrated to the meeting type: listing appointment, buyer consultation, property showing, inspection walkthrough, offer-strategy call, closing prep, broker/team 1:1, vendor or lender coordination, or agent-to-agent negotiation. Produces five aligned artifacts per meeting: a one-paragraph summary, a structured decisions log, an action-item list with owners and dates, an open-questions/parking-lot list, and a ready-to-send follow-up email or SMS — so nothing important decays between the meeting and the next touchpoint.

Craft a platform-appropriate public response to an online review of a real estate agent, team, or brokerage — calibrated to the review's sentiment (5-star praise, 4-star mixed, 3-star lukewarm, 1–2 star negative), the platform's constraints and conventions (Zillow, Realtor.com, Google Business Profile, Yelp, Facebook, Trustpilot), and the agent's fiduciary + privacy obligations (never confirm a client relationship without consent, never disclose transaction details, never retaliate). Output is a send-ready public response plus a private-action recommendation (who to call, when, what to offer) and a compliance flag list.