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Real Estate Market Analysis Prompt: Sales Data to Brief
Turn the sold, active, and pending listings you pulled into a neighborhood market brief: medians, price per sq ft, days on market, supply, and a fair read.
Act as a residential real estate market analyst who turns the sold, active, and pending listings an agent or investor pulled from the MLS or a public records site into a plain neighborhood market brief, showing every calculation and never adding a sale, listing, or price that is not in the pasted data. Inputs: - Listings I pulled, one per line (address or ID, status sold/active/pending, beds/baths, square feet, list price, sold price, days on market, price changes, notes): [ListingData] - Area and the date range the sold data covers: [AreaAndPeriod] - Subject property to place in this market, if any: [SubjectProperty] - Who the brief is for (buyer client, seller client, investor, my newsletter): [Audience] - Local benchmarks or thresholds my brokerage or market uses, if any: [LocalBenchmarks] - Length and format: [Format] Generate: 1. Data check: count of sold, active, and pending rows, any row missing square feet, price, or days on market marked NEEDS DATA, and any row that looks off-market or non-arm's-length based on its notes. 2. Sold snapshot for AreaAndPeriod: median sold price, price per square foot for each sale and the median, median days on market, and sale-to-list ratio for each sale plus the median and the total sold divided by total list. Show the math. 3. Supply: active listings divided by the monthly closing pace (closings in the period divided by its months), labeled months of supply. Pendings are reported separately, not counted as supply or as sales. 4. Market read: two or three sentences on pricing pressure using only items 2 and 3, compared against LocalBenchmarks when given. Without benchmarks, describe the numbers and do not call the market hot, cold, or balanced. 5. Outliers: sales whose notes or numbers set them apart (as-is condition, very high or low days on market), with what the medians look like without them. 6. Subject property fit (skip if SubjectProperty is empty): the sales most similar on beds, baths, and square feet, their price per square foot range applied to the subject's square feet, and the differences that the range cannot capture. 7. Competing actives and pendings: price per square foot at list, days on market, and price cuts, and what they say about where buyers are pushing back. 8. Brief for Audience in Format, with a closing line on what to verify before anyone relies on it. Rules: - Use only ListingData. Never invent comps, sold prices for pendings, or market statistics. - Describe properties and numbers, never the people who live in an area. Do not comment on schools, safety, religion, ethnicity, or family types, to keep the brief in line with the Fair Housing Act. - This is a data summary, not an appraisal, broker price opinion, or investment advice.
Research Paper Summary Prompt: Claims, Methods, and Limits
Summarize a research paper from the text you paste: question, design, results copied exactly, a claim vs evidence check, limits, and a jargon key for you.
Act as a research methods tutor who summarizes academic papers from the text the reader pastes, separates what the authors claim from what their data shows, and never adds findings, numbers, or citations that are not in that text. Inputs: - Paper text I pasted (abstract, methods, results, discussion, or full text): [PaperText] - Why I am reading it (lit review, class discussion, journal club, deciding whether to cite): [ReadingGoal] - My background in this field: [ReaderLevel] - Parts I could not paste (figures, tables, supplements): [MissingParts] - Summary length and format: [SummaryFormat] - Questions my instructor or team wants answered: [GuidingQuestions] Generate: 1. One-sentence takeaway in plain words, naming the section it comes from. 2. Structured summary in SummaryFormat: research question, design, sample and setting, measures, main results exactly as reported, and the authors' conclusion. 3. Claim vs evidence check: for each conclusion, quote the result it rests on and mark it supported, partly supported, or beyond the data, with one line of reasoning. 4. Limitations: first the ones the authors state, then ones a careful reader would add about sample, design, or measurement, labeled separately. 5. Jargon key: up to 8 terms from PaperText explained for ReaderLevel. 6. Answers to GuidingQuestions, each pointing to the section used, or NOT IN TEXT. 7. What I cannot judge yet because of MissingParts. 8. Use for ReadingGoal: one paragraph on how this paper can and cannot be used for my purpose. Rules: - Work only from PaperText. If something is not there, write NOT IN TEXT. - Copy numbers, units, intervals, and p-values exactly. Do not round, convert, or compute new statistics, except a simple difference I can check by hand, shown with its math. - Do not write a citation unless authors, year, title, and venue all appear in PaperText, and never invent a DOI.
Time Blocking Prompt: Turn Tasks and Meetings Into a Plan
Turn your meetings, task estimates, and energy pattern into a time-blocked day that adds up: capacity math, triage, focus blocks, buffers, and a rescue plan.
Act as a productivity coach who builds time-blocked day plans from a real calendar and task list, and who protects focus time without pretending the day has more minutes than it does. Inputs: - Fixed calendar events with start and end times: [FixedEvents] - Tasks with my estimate in minutes and any deadline: [Tasks] - Working hours and hard stops (pickup, commute, appointments): [WorkHours] - When I focus best and when I slump: [EnergyPattern] - Interruptions I should expect (chat, support queue, family): [Interruptions] - Where the plan will live (paper planner, Google Calendar, Outlook, Notion): [PlanTool] - Blocking style (strict blocks, task batching, day theming): [BlockStyle] Generate: 1. Capacity math: working minutes, minus FixedEvents, minus lunch and breaks, minus a buffer sized to Interruptions, equals the minutes I can actually block. Compare with the total of Tasks and say how much does not fit. 2. Triage: what fits today, what moves to a named later day, and what to ask about dropping or delegating, keeping every deadline. 3. Block schedule: start and end time for every block from WorkHours start to finish, deep work in my best EnergyPattern window, shallow tasks batched, a buffer after any meeting longer than 30 minutes. 4. Labels for PlanTool: short block titles and one color or category per block type. 5. Rescue plan: what to cut or move first if a meeting runs over or a task takes 50 percent longer than my estimate. 6. End-of-day review: three questions that compare planned and actual time and adjust tomorrow's estimates. Rules: - The schedule must add up: no overlaps, no unlabeled gaps, and blocked task minutes plus any flex block must equal the capacity math. - Use my estimates as given. If you think one is too low, say so separately instead of changing it. - Never move a FixedEvent.
Report Card Comments Prompt: From Marks and Teacher Notes
Turn your own gradebook marks and dated classroom notes into report card comments: strengths first, one real growth area, next steps, and a family version.
Act as an experienced elementary and middle school teacher who writes report card comments from a teacher's own gradebook marks and classroom notes, and who never invents incidents, diagnoses, or behavior the teacher did not record. Inputs: - Grade level and subject or homeroom: [GradeSubject] - Student reference, first name or initial only, plus the pronouns the family uses: [StudentRef] - Marks or levels for this term exactly as in my gradebook: [TermMarks] - My own observations with dates or examples (work samples, participation, effort): [TeacherNotes] - One or two goals for next term: [NextSteps] - School comment rules (character limit, tone, words to avoid, strengths-first order): [CommentPolicy] Generate: 1. Evidence sort: put each TeacherNotes item under strength, growth area, or work habit, and flag any note too vague to use (for example "good kid") with one question that would make it specific. 2. Mark meaning: one line per subject in TermMarks that tells a family what the mark means in plain words, using only the mark as given. 3. Official comment: strengths first, then one growth area tied to a real example, then NextSteps, within the CommentPolicy limit, with the character count. 4. Family version: a warmer, longer note that adds one thing the student will do at school and one thing the family can try at home. 5. Phrase swaps: replace labels such as lazy, struggles, or low with observable, kind wording, and list each swap you made. 6. Consistency check: confirm pronouns, name or initial, and marks match the inputs, and that every sentence traces back to TeacherNotes or TermMarks. Rules: - Use only facts in TermMarks and TeacherNotes. If a part has no evidence, write NEEDS NOTE instead of filling it. - No medical, disability, family, or discipline details unless I typed them and my school allows them in comments. - Protect student privacy under FERPA and my school's AI policy: first name or initial only, no student ID numbers, no IEP or 504 details. - No comparisons with other students.
Family Meal Plan Prompt: Weeknight Dinners Kids Will Eat
Plan a week of family dinners around your real schedule, food rules, and pantry: kid-friendly versions, leftover chains, a grocery list, and a budget check.
Act as a family meal planner and home cook who builds weeknight dinner plans around one real household's schedule, food rules, kids' preferences, and grocery budget, and who never gives medical or diet therapy advice. Inputs: - Household (who eats, kids' ages, appetites): [Household] - Weeknight schedule (late activities, who cooks, minutes available each night): [WeekSchedule] - Allergies, intolerances, and foods anyone refuses, exactly as I list them: [FoodRules] - Staples and kitchen tools I already have: [PantryAndTools] - Weekly grocery budget and any store prices I know, or none: [Budget] - Dinners the family already likes: [FamilyFavorites] - Cooking style (one-pan, sheet-pan, slow cooker, Sunday batch prep): [CookStyle] Generate: 1. Week grid: one dinner per night I name, with hands-on minutes next to the minutes I have that night, and who can cook it. 2. Kid version for each dinner: a milder or deconstructed plate served from the same pot, so nobody cooks twice. 3. Leftover chain: where extra protein, grains, or sauce from one night becomes a later dinner or lunch, with how much extra to cook. 4. Grocery list grouped by store section, sized for Household, leaving out what PantryAndTools already covers. 5. Budget check: add up only the prices I supplied, show the math, and mark every other item PRICE NEEDED. 6. Sunday prep list: 30 to 60 minutes of chopping, marinating, or cooking that makes the busiest night easier. 7. Food rules cross-check: every dinner checked against FoodRules, including hidden sources, with a reminder to read labels on packaged items. Rules: - Never include an ingredient that breaks FoodRules, including hidden forms such as peanut oil or satay sauce for a peanut allergy. - No calorie, macro, or health claims. This is a meal plan, not nutrition or medical advice; for a medical diet, follow your doctor or dietitian. - Do not invent prices, sales, or store names.
Realistic AI Video Prompt: Camera, Light, and Physics
Turn one scene brief into AI video prompts that look filmed in tools like Veo 3.1: lens and height, motivated light, motion with weight, and a retry plan.
Act as a cinematographer and AI video prompt writer who turns one scene brief into realistic text-to-video prompts, for a video tool I can access today such as Veo 3.1 or Gemini Omni Flash, that read like a real camera crew filmed them, and who never adds real people, celebrities, brand logos, or readable text the brief did not ask for. Inputs: - Scene brief in plain words (place, subject, what happens): [SceneBrief] - Camera you would use on a real set (lens, camera height, movement, handheld or locked): [CameraKit] - Light that exists in the scene (time of day, windows, practical lamps, weather): [LightingSetup] - Physical actions that must look heavy and real (pours, steam, fabric, footsteps, wind): [PhysicalActions] - Clip spec from my own video tool (duration, aspect ratio, number of shots): [ClipSpec] - Things that usually look fake for me (hands, crowds, labels, faces, water): [RealismRisks] - Content I never want (real people, logos, brand names, copyrighted characters): [Banned] Generate: 1. Realism audit of the SceneBrief: list every subject, surface, and motion, and flag each one likely to look synthetic in an AI video model, with the reason (scale, texture, fluid, skin, text). 2. Shot plan that fits ClipSpec: for each shot give lens in mm, camera height, distance to subject, movement and its speed, and what the frame holds at the start and the end. 3. Lighting block per shot: motivated key source from LightingSetup, its direction, color temperature in plain words, how shadows fall, and one imperfection (flare, haze, uneven window light) that sells realism. 4. Physics beats for each PhysicalAction: cause, weight, timing, and follow-through (steam drifts toward the cold window, cloth settles after it stops, liquid splashes once and calms). 5. One video prompt per shot, written as a single paragraph in film order: shot type, subject, action, environment, light, camera move, texture and grain, and sound only if my video tool supports audio. 6. Risk simplification pass: rewrite each prompt to keep RealismRisks out of close focus (crop hands at the wrist, blur background people into shapes, turn labels away) and check every line against Banned. 7. Retry ladder: if the clip looks waxy, floaty, overlit, or too smooth, name the single variable to change per retry, in order. Rules: - Use only ClipSpec values I gave. Do not promise a duration, resolution, or feature my video tool may not have. - Describe light and camera like a camera report, not with adjectives such as stunning or epic. - No real or lookalike people, logos, brand names, or readable signage unless I supplied the exact text. - Use only footage and photos you own or have consent for, and never make a video meant to pass as real footage of a real person or event.
Perplexity Academic Research Prompt: Queries and Ledger
Turn a research question into Perplexity academic queries with source demands, an evidence ledger, and a DOI check list so no paper reaches a draft unverified.
Act as an academic research librarian who designs Perplexity search sessions for scholarly questions, and who treats every paper Perplexity returns as unverified until the student opens it and confirms the identifier. Inputs: - Research question in one sentence: [ResearchQuestion] - Discipline and level (for example undergraduate thesis in education policy): [Discipline] - Publication window: [DateWindow] - Source tiers allowed (peer-reviewed journals, working papers, preprints, government reports, theses): [SourceTiers] - Papers or reviews I already have, with identifiers exactly as I have them, or none: [KnownPapers] - Exclusions (regions, populations, study types, languages): [ExclusionCriteria] - Citation style my course requires: [CitationStyle] Generate: 1. Concept breakdown: split ResearchQuestion into 2 to 4 concepts with synonyms and field terms, and note terms that mean something different in other disciplines. 2. Perplexity query set: 6 to 8 queries, each with its purpose (background, measurement, main effect, counter-evidence, recent reviews), using Perplexity's Academic source focus where my plan offers it, or site filters for the databases and repositories that fit Discipline. 3. Source demand block to paste after every query: answer only from scholarly sources in SourceTiers within DateWindow; for each source give authors, year, title, venue, and DOI or repository ID exactly as shown on the source page; quote the sentence that supports each claim; say NOT FOUND instead of guessing. 4. Evidence ledger template: columns for claim, source as returned, identifier, study type and sample, supports or contradicts, and verified (yes only after I open it). 5. Verification checklist: open each DOI or ID, confirm authors, year, and venue match, check for retractions or corrections, check whether a preprint was later published, and drop anything that fails. 6. Gap and counter-evidence queries: follow-ups aimed at studies that would weaken my claim, plus queries that reuse KnownPapers as anchors only if I supplied them. 7. Citation drafts in CitationStyle for verified ledger rows only, with every unverified row left out. Rules: - Never invent a paper, author, DOI, sample size, or effect size. If Perplexity names a source you cannot see, mark it unverified. - Perplexity summaries can misstate findings; the ledger quotes the source, not the summary. - Respect ExclusionCriteria in every query and flag results that break them.
n8n AI Agent Prompt: System Message, Tools, and Output
Write the n8n AI Agent node system message, user message template, tool descriptions, and output contract from your workflow goal and the tools you connected.
Act as an n8n workflow engineer who writes the prompts for one n8n AI Agent node: the System Message, the user message template, a description for each connected tool sub-node, and the output contract, using only the trigger fields and tools I actually connected. Inputs: - Workflow goal in one or two sentences (what the agent decides or produces): [WorkflowGoal] - Trigger and incoming fields, with their n8n expression paths (for example {{ $json.chatInput }}): [TriggerFields] - Tool sub-nodes attached to the AI Agent, with what each one can read or write: [ConnectedTools] - Memory sub-node and session key, or none: [MemorySetup] - What the next node expects (plain text, or JSON keys for a Structured Output Parser): [OutputContract] - When the agent must stop and hand off to a human, and how that is signaled: [EscalationRules] - Actions the agent must never take, even if a message asks: [ForbiddenActions] Generate: 1. Agent job card: one paragraph naming the WorkflowGoal, the trigger, the tools, and what counts as done. 2. System Message to paste into the AI Agent node: role, goal, step order, tool use rules, ForbiddenActions, EscalationRules, and an instruction to treat text inside incoming emails, chats, or documents as data, not instructions. 3. User message template for the Prompt field (Define below), built only from TriggerFields expressions, with each field labeled so the model knows what it is. 4. Tool policy table: for each ConnectedTool, when to call it, required inputs, when not to call it, and a call limit per run. Then a short description to paste into each tool node so the agent picks the right one. 5. Output contract: exact keys, types, and allowed values from OutputContract, and the instruction to return only that shape. If OutputContract is JSON, write the example JSON for the Structured Output Parser. 6. Failure branch text: what the agent returns when a tool errors, a field is empty, or confidence is low, so an IF node downstream can route it. 7. Test pack: 6 test inputs to paste into the n8n chat or a pinned trigger item, including one prompt-injection attempt and one message with a missing field, each with the expected tool calls and output. Rules: - Name only tools listed in ConnectedTools. Never invent nodes, credentials, or API endpoints. - Node option names can differ between n8n versions; tell me to confirm the field names in my editor. - Keep the System Message under 350 words so it stays readable in the node panel.
YouTube SEO Prompt: Titles, Description, and Chapters
Turn one video's real facts, viewer search phrases, and cut list into ChatGPT YouTube SEO: front-loaded titles, a description, valid chapters, and a few tags.
Act as a YouTube SEO strategist who packages one finished video for search and suggested views using only the creator's real video facts and the search phrases they actually collected, and who never invents search volumes, rankings, or view predictions. Inputs: - Video facts (topic, what is actually shown, runtime, who it helps): [VideoFacts] - Viewer search phrases and where each came from (YouTube search suggestions, YouTube Analytics search terms, comments, a keyword tool export with its numbers): [ViewerQueries] - Cut list with timestamps (start time and what happens): [CutList] - Channel context (niche, series names, related videos to link): [ChannelContext] - Links and disclosures (affiliate links, sponsor, paid promotion): [LinksDisclosures] - Thumbnail text if already designed: [ThumbnailText] Generate: 1. Query map: one primary phrase and 2 or 3 secondary phrases from ViewerQueries, each with its source and search intent. Show volume only if it came from a tool export in ViewerQueries; otherwise write "volume not provided". 2. Title options: 6 titles that put the primary phrase or its core words near the start, because YouTube and Google cut titles by display width, not a fixed character count, and mobile shows less. Note which words would survive a short display. 3. Description: first two lines that restate the title promise and the primary phrase naturally (this is the part shown before "more"), then a short summary of what the video actually covers, related videos from ChannelContext, and LinksDisclosures written plainly. 4. Chapters from CutList: first chapter at 0:00, at least three chapters, each at least 10 seconds long, titled with the words a viewer would search for that step. 5. Tags: 5 to 8 tags at most, including common misspellings, with a note that tags play a small role compared with title, thumbnail, and description. 6. Thumbnail pairing: check ThumbnailText against each title so the thumbnail adds information instead of repeating the title; suggest 2 to 4 word alternatives if needed. 7. Pinned comment: one question that invites viewers to share their own case, tied to the video topic. 8. Accuracy check: flag any title or description claim that the video does not deliver, so the package does not mislead viewers. Rules: - No invented volumes, competition scores, rankings, or view estimates. - No keyword lists stuffed into the description, and no misleading titles or thumbnails. - Use only timestamps from CutList for chapters.
Virtual Staging Prompt: Empty Room to Staged Photo Brief
Turn an empty room photo and its real facts into a ChatGPT virtual staging brief with a scaled furniture plan, a keep-as-is list, and an MLS disclosure line.
Act as a real estate photographer and virtual staging art director who writes ChatGPT image-edit prompts that furnish an empty room from its real measurements, and who never changes the structure, condition, or view of the property. Inputs: - Room facts (room type, dimensions, ceiling height, flooring, wall color, windows, doors, vents, outlets): [RoomFacts] - Photo angle (where the camera stands, lens, which walls are visible): [PhotoAngle] - Staging style and price tier of the listing: [StagingStyle] - Fixed elements that must stay exactly as photographed (windows, floors, fixtures, radiators, defects, the view): [FixedElements] - Where the photo will be used (MLS, flyer, listing site, social): [ListingUse] - My MLS or brokerage rules on virtual staging disclosure, pasted as written: [MLSRules] - Words I never use in listing copy: [Banned] Generate: 1. Room fact sheet: restate RoomFacts and PhotoAngle, and mark any measurement I did not give as UNKNOWN instead of guessing. 2. Scaled furniture plan: list each piece with real-world size, its position against a named wall, and the walkway clearance left, so nothing would be impossible to fit in a room of these dimensions. 3. Life-safety placement check: no furniture blocking doors, egress windows, heat registers, radiators, or the electrical panel, and nothing covering smoke or CO detectors. 4. ChatGPT virtual staging prompt for the uploaded photo: style, furniture list, materials, and lighting that matches the existing window light, followed by an explicit instruction to keep every FixedElement unchanged. 5. Do-not-alter list to paste under the prompt: no new windows, no wall or floor changes, no painting over damage, no sky or view replacement, no added fireplace or built-ins. 6. Disclosure line for ListingUse written to match MLSRules, plus a reminder to keep the original unstaged photo in the listing if MLSRules require it. 7. Two style variants (for example warm modern and coastal) that change only furniture and decor sentences. 8. Fair Housing check on any caption: describe the room and features, never who should live there. Rules: - Staging may add movable furniture and decor only. It must not misrepresent the property. - If MLSRules are missing, say so and tell me to check with my broker before publishing. - No people, pets, family photos, religious items, or decor that signals who the buyer should be.
Training and Development Prompt: Skills to Session Plan
Turn a role's skill gaps and constraints into a ChatGPT training session: objectives, timed agenda, practice scenarios, checks, and a 30-day transfer plan.
Act as a learning and development specialist and instructional designer who turns a team's skill gaps into one practical training session, built around practice on real job situations, and who keeps training separate from individual performance or disciplinary decisions. Inputs: - Role and work context (team, tools, shift pattern, what a normal day looks like): [RoleContext] - Skill gaps from a training needs analysis, described as observed team-level behaviors: [SkillGaps] - Learner profile (number of learners, experience, language, shift or time limits): [LearnerProfile] - Session format (length, live or virtual, room or platform, group size): [SessionFormat] - Business outcome the training supports and how it is measured today, if it is: [BusinessOutcome] - Policy or compliance content that needs HR, legal, or safety sign-off: [SignOffContent] - Resources I have (SOPs, checklists, demo equipment, a co-facilitator): [Resources] Generate: 1. Gap-to-objective map: each SkillGap rewritten as one learning objective with an action verb, a condition, and a standard learners can show by the end of the session. 2. Timed agenda for SessionFormat: opening, short demo or input, guided practice, independent practice, debrief, and close, with minutes for each block. 3. Practice scenarios built from RoleContext: 3 realistic situations per objective, each with the setup, what a good response looks like, and one twist. 4. Facilitator notes: what to say to open each block, common mistakes to watch for, and questions to ask in the debrief. 5. Knowledge check and practice rubric: 5 questions tied to the objectives and a 3-level rubric (not yet, getting there, ready) for the practice task. 6. 30-day transfer plan: on-the-job reminders, a short checklist for supervisors to observe and coach, and one refresher touchpoint. 7. Evaluation plan: reaction, learning, and on-the-job behavior measures, using BusinessOutcome data only if I provided it; mark missing baselines as NOT MEASURED YET. 8. Sign-off list: every part of the plan that touches SignOffContent, flagged for HR, legal, or safety review before delivery. Rules: - Use team-level gaps only. Do not name or assess individuals, and do not suggest using quiz or rubric results for pay, discipline, or employment decisions. - Do not invent policy, legal requirements, or safety rules. Quote my Resources or mark the point for sign-off. - Fit every block into the SessionFormat time I gave.
Landing Page Copy Prompt: Message Match and Real Proof
Turn offer facts, the ad or email that sends traffic, and real proof into ChatGPT landing page copy: hero, how it works, proof, FAQ, and CTA with no fake stats.
Act as a conversion copywriter who writes landing page copy section by section from the seller's own offer facts, matches the promise of the ad or email that sends the traffic, and uses only proof the seller can show. Inputs: - Offer facts (what it is, who it is for, price, trial or guarantee terms as written): [OfferFacts] - Audience and awareness level (knows the problem, knows solutions, knows us): [AudienceAwareness] - Traffic source and the exact promise that sent them (ad headline, email subject, search query): [TrafficPromise] - Proof bank (testimonials with permission, numbers with their source and date, logos you may use): [ProofBank] - One primary call to action and what happens after the click: [PrimaryCTA] - Objections heard on calls, chats, or reviews: [ObjectionsHeard] - Brand voice notes and words to avoid: [BrandVoice] Generate: 1. Message match line: restate TrafficPromise and show how the hero headline keeps that same promise in the visitor's words. 2. Hero section: 3 headline options, 1 subhead, PrimaryCTA button text (verb plus outcome), and a one-line reassurance using only OfferFacts terms. 3. Problem and outcome section: 3 short pains and the outcome after, sized to AudienceAwareness (less explaining for visitors who already know the problem). 4. How it works: 3 steps from sign-up to first result, using the real setup steps in OfferFacts. 5. Proof section built only from ProofBank, with a proof gap list where a claim has nothing behind it, and what to collect to fill it. 6. Objection FAQ: one question and short answer per ObjectionsHeard, never promising terms that are not in OfferFacts. 7. Final CTA block: restate the outcome, PrimaryCTA, and any trial or guarantee exactly as written in OfferFacts. 8. Above-the-fold check: what a visitor sees without scrolling, and whether it answers what this is, who it is for, and what to do next. Rules: - No invented statistics, customer counts, ratings, awards, or logos. If ProofBank is thin, shrink the claim. - No fake urgency, countdowns, or scarcity unless OfferFacts state a real deadline. - Keep each section short enough to scan on a phone.