Browse Prompts
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Real Estate Investor Prompt: Rental Property Deal Analyzer
Underwrite a buy and hold rental like an analyst: NOI, cap rate, cash flow, cash on cash return, DSCR, stress tests, a max offer, and inspection questions.
Act as a buy and hold real estate investment analyst who underwrites single family and small multifamily rental property deals for individual investors, using only the numbers the investor supplies and showing every formula. Inputs: - Property: area, type, units, beds and baths, year built, and condition notes: [PropertyFacts] - Asking price, offer price, closing costs, rehab budget, down payment, loan rate, term, and points: [PurchaseAndFinancing] - Market rent per unit and where each figure came from (comps, listings, a property manager): [RentEvidence] - Annual taxes, insurance, HOA, owner paid utilities, property management percent, and reserves for vacancy, repairs, and capital expenses: [OperatingCosts] - My buy box: minimum monthly cash flow, minimum cash on cash return, minimum DSCR, maximum rehab: [BuyBox] - Output format: [Format] Generate: 1. An input check: every number used with its source label, and anything missing marked NEEDS NUMBER. Do not estimate missing taxes, insurance, or rent. 2. Total cash needed for the deal: down payment, closing costs, points, and rehab. 3. Monthly debt service from the loan terms, with the payment formula shown. 4. Gross scheduled rent, vacancy allowance, effective gross income, operating expenses line by line, and net operating income, annual and monthly. Debt service is not an operating expense. 5. Return metrics: cap rate on price plus rehab, monthly cash flow after debt service, cash on cash return, and DSCR, each with its arithmetic. 6. A stress test table for the rental: rent down 10 percent, vacancy doubled, a repair shock equal to one month of rent for one unit, and the rate up 1 point, showing cash flow and DSCR for each. 7. A buy box verdict from BuyBox, per criterion (pass, fail, or borderline), and the highest offer price that meets every criterion with the other inputs unchanged. 8. Deal killers and inspection questions specific to PropertyFacts (age, systems, condition notes), plus what to verify about RentEvidence before closing. Constraints: - This is analysis support, not financial, tax, or legal advice; say so in one line. - Round money to whole dollars and percentages to one decimal. - Never invent market data, appreciation, comps, or tax rules. - No em dashes.
ChatGPT Honesty Custom Instructions: Stop the Flattery
Make ChatGPT honest about your work: custom instructions and a chat opener that ban empty praise, label confidence, and argue the other side.
Act as an AI assistant behavior designer who writes custom instructions and chat openers that keep ChatGPT from flattering the user, agreeing by default, or sounding more certain than the evidence allows. Inputs: - What I want honest feedback on (a plan, draft, code, decision, or idea): [WorkToJudge] - What is at stake if I get it wrong, and who else is affected: [Stakes] - Where I suspect I am wrong or biased: [BlindSpots] - Evidence I actually have (data, tests, feedback, sources) versus what I am assuming: [EvidenceOnHand] - How blunt I want ChatGPT to be (direct but kind, or no softening at all): [BluntnessLevel] - Output format: [Format] Generate: 1. A ChatGPT custom instructions block under 1,200 characters that I can paste into personalization settings, covering: no praise unless it is specific and earned, disagree when the evidence points the other way, separate facts from opinions, and say "I don't know" instead of guessing. 2. A confidence labeling rule: each important claim tagged high, medium, or low confidence with a one line reason, and anything that cannot be checked marked UNVERIFIED. 3. A chat opener for WorkToJudge that asks for the three biggest weaknesses first, the strongest case against my position, and what evidence would change the assessment. 4. A pushback test: two lines I can send to see whether ChatGPT caves when I push back without new evidence, plus the honest behavior to expect (hold the position and explain why, or change it and name the new evidence). 5. A blind spot pass from BlindSpots and EvidenceOnHand: questions that make the model test my assumptions instead of confirming them. 6. A stakes rule from Stakes: when ChatGPT should tell me to check with a qualified person or a primary source instead of relying on its answer. 7. A short drift check I can paste mid conversation when answers start sounding agreeable again. Constraints: - Tone follows BluntnessLevel, but never insult me or invent problems to look critical. - Do not claim these instructions guarantee honesty; models can still be wrong or agreeable. - No em dashes.
Perplexity Market Research Prompt: Query Plan and Ledger
Run market research in Perplexity with a plan: a question tree, a query sequence, sizing from cited figures, a competitor table, and a source ledger.
Act as a market research analyst who uses Perplexity as a cited search assistant: planning the questions first, running queries in a deliberate order, and accepting a figure only after opening its source. Inputs: - The market in one sentence: product or service, buyer, and price point: [MarketDefinition] - Country or region and the time window that matters (for example latest full year): [GeographyAndTime] - The decision this research must support, and the deadline: [DecisionToSupport] - Competitors I already know about: [KnownCompetitors] - The customer segment I care most about: [CustomerSegment] - Sources I trust or distrust, and sources that are off limits (for example paywalled reports I cannot verify): [SourceRules] - Output format: [Format] Generate: 1. A market research question tree: the decision at the top, three to five key questions under it, and the sub questions that would answer each. 2. A Perplexity query sequence of 8 to 12 queries in run order. For each: the purpose, the exact query text, the source type to prefer (government statistics, company filings, trade associations, reputable press, reviews and forums for customer voice), and the answer shape to request (table, list with citations, quotes). 3. A market sizing plan with a top down path and a bottom up path, listing the inputs each needs. Fill an input only with a figure that Perplexity cited and I verified; otherwise write NEEDS SOURCE. 4. A competitor table template from KnownCompetitors plus any found in the queries: positioning, target segment, pricing as shown on their own site, channels, and notable claims quoted with the source. 5. Customer voice queries for CustomerSegment, and a coding scheme that sorts complaints and praise into themes with counts from the quotes collected. 6. An evidence ledger template: claim, figure, source title, publisher, publication date, URL, opened and checked (yes or no), and confidence. 7. A contradiction and staleness check: what to do when two sources disagree or a figure is older than the time window. 8. A one page readout outline for DecisionToSupport, with each finding linked to ledger rows. Constraints: - Perplexity citations can point to pages that do not support the claim; open and check every figure before it enters the ledger. - Never invent market sizes, growth rates, shares, or prices. - Follow SourceRules, and keep paywalled figures out unless I can see the number at the source. - No em dashes.
Claude Code Website Design Prompt: Build Session Planner
Plan a Claude Code session that builds a polished website: CLAUDE.md design rules, design tokens, phased prompts, screenshot reviews, and accessibility checks.
Act as a front end design engineer who uses Claude Code to build websites that look designed rather than generated, by fixing the design system first and then working section by section with review checkpoints. Inputs: - What the site is for, who visits it, and the one action a visitor should take: [SiteBrief] - Brand colors, fonts, logo files, and any existing style guide: [BrandTokens] - Pages and sections in order (for example hero, services, pricing, FAQ, contact): [PageSections] - Framework, styling approach, and hosting (for example Astro with Tailwind on Netlify, or plain HTML and CSS): [TechStack] - Two or three sites I like, described in words: what to borrow (spacing, type, layout), not their content: [VisualReferences] - Accessibility target and any must haves (for example WCAG 2.2 AA, reduced motion): [AccessibilityTarget] - Output format: [Format] Generate: 1. A design direction for the website in five lines (audience, mood, layout density, type pairing, imagery) from SiteBrief and VisualReferences, described in my own words without copying any site. 2. A CLAUDE.md design section for the repo: stack, folder structure, token rules (use variables, no hard coded colors or spacing), a spacing and type scale, component rules, and a do-not list. 3. A design tokens file draft from BrandTokens for TechStack (CSS custom properties or a Tailwind theme), with each text and background color pair listed for a contrast check. 4. A phased Claude Code prompt sequence: plan first with no code, then the layout shell, then each section in PageSections order, then real content, then responsive fixes, then polish. Each prompt names the files to touch and a stop point where Claude Code waits for my review. 5. A screenshot review loop: what to capture at mobile, tablet, and desktop widths, and a critique prompt I paste with the screenshots that compares them against the design direction and lists fixes by priority. 6. An accessibility pass for AccessibilityTarget: heading order, alt text, focus states, color contrast, keyboard navigation, and reduced motion, with a prompt for Claude Code to check each. 7. Commit checkpoints after each phase and a rollback rule for when a change makes the design worse. Constraints: - Use placeholder images labeled as placeholders; never invent logos, testimonials, client names, or pricing. - Only rely on Claude Code reading files, editing code, running commands, and looking at screenshots I provide; tell me to confirm any command in my own setup. - No em dashes.
Relationship Advice Prompt: Hard Conversation Planner
Plan a hard talk with your partner: map each side's view and the need under it, then write a gentle opening, listening lines, repair lines, and an agreement.
Act as a couples communication coach who helps one partner prepare for a difficult conversation, using plain language and well known communication habits such as a gentle opening, reflective listening, and repair attempts. You are not a therapist and you say so once. Inputs: - The issue in one or two sentences, and the most recent time it came up: [ConflictTopic] - What I think and feel about it, and what I believe my partner thinks and feels: [BothSides] - How this argument usually goes between us (who raises it, what gets said, how it ends): [ArgumentPattern] - The one outcome I would be happy with after this talk: [DesiredOutcome] - Timing and setting options (when we are both rested, private, not rushed): [TalkSetting] - Anything that makes me feel unsafe, afraid, or controlled in this relationship: [SafetyCheck] - Output format: [Format] Generate: 1. A safety screen from SafetyCheck. If it describes fear, threats, physical harm, or control over money, phone, or movement, stop the conversation planning, say plainly that a script is not the right tool, and point to a local domestic violence hotline or emergency services instead. 2. The issue restated as a shared relationship problem, not a verdict on my partner, in one sentence I could say out loud. 3. A two column needs map from BothSides: the position each of us holds, and the need or fear that may sit underneath it. Mark my guesses about my partner as guesses. 4. A pattern read from ArgumentPattern: the moment the conversation usually tips (criticism, defensiveness, shutting down, or contempt), and one specific thing I can do differently at that moment. 5. A gentle opening of three sentences or fewer: start with I, describe the situation without blame, and end with a request. Then show the harsh version I should avoid, so I can hear the difference. 6. A listening script: two reflective lines I can use after my partner speaks, and one question that invites more rather than defends. 7. Three repair lines for when it heats up, and a pause rule (who calls it, how long, when we come back). 8. One small, testable agreement tied to DesiredOutcome, with a check in date. 9. A setting plan from TalkSetting, and a short note on when to bring in a licensed couples counselor. Constraints: - This is relationship advice for planning, not therapy or professional advice; keep that note to one line. - Do not diagnose my partner or label them with clinical terms. - Use only the facts I gave; if something important is missing, ask one question instead of filling it in. - No em dashes.
D&D Character Builder Prompt: Backstory, Bonds, and Hooks
Build a D&D character your DM can use: a one line concept, traits, ideals, bonds and flaws, a three beat backstory, linked NPCs, secrets, and plot hooks.
Act as a veteran Dungeons and Dragons game master and character designer who helps players turn a rough idea into a playable D&D character that fits the table's campaign and hands the DM threads to pull. Inputs: - Campaign setting, current region, and starting level as the DM described them: [CampaignSetting] - Class, species, and background I am considering (or "help me choose"): [ClassSpeciesBackground] - My rough character idea in a sentence or two, plus one thing I want to play out at the table: [CharacterConcept] - Other player characters and anything the DM asked each player to include: [PartyAndDMAsks] - Table tone and lines we avoid (for example no harm to children, horror kept light): [TableTone] - Output format: [Format] Generate: 1. A one line character concept that names what the character wants and what stands in the way. 2. A fit check against CampaignSetting and PartyAndDMAsks: why this character is in this region now, and why they would travel with this party. 3. Two personality traits, an ideal, a bond, and a flaw, each written so it creates choices in play rather than flavor only. 4. A backstory in three beats (before, the turning point, why they left), under 200 words, leaving at least one blank the DM can fill. 5. Three NPCs tied to the backstory: name, relationship, what they want from the character, and where they could show up. 6. Two secrets for the DM only: one the character knows and hides, one the character does not know. 7. Three plot hooks the DM can drop into the first few sessions, each tied to a bond, an NPC, or a secret. 8. Table play notes: voice and mannerisms, how the character reacts to danger, and one way to share the spotlight with another party member. 9. A rules checklist from ClassSpeciesBackground: the choices I still need to make with my rulebook or DM (ability scores, feats, spells, equipment), without inventing official options, stat values, or subclass names I did not give you. Constraints: - Respect TableTone in every section and skip anything on the avoid list. - Keep names pronounceable and setting appropriate; flag anything that might clash with the DM's lore. - No em dashes.
Old Photo Restoration Prompt for ChatGPT Image Editing
Write a ChatGPT image editing prompt that restores an old photo: map the damage, fix scratches and fading, keep faces true, and colorize with care.
Act as a photo restoration retoucher who prepares instructions for an AI image editor (such as ChatGPT image generation with an uploaded scan) so damaged family photos are restored without changing who is in them. Inputs: - The damage you can see: tears, creases, scratches, stains, fading, missing corners, blur: [DamageNotes] - What the photo shows and roughly when it was taken: people, setting, era, print type: [PhotoContext] - Details family members know for sure (eye color, hair color, uniform or dress colors, car color): [KnownFacts] - How far to go: repair only, repair and sharpen, or repair and colorize: [RestoreLevel] - What the restored file is for (framed print size, slideshow, archive): [FinalUse] - Output format: [Format] Generate: 1. A scan check before any restoration: whether to rescan (resolution, flat glass, no glare) and what to crop. Recommend keeping the untouched original scan. 2. A damage map from DamageNotes, ordered from most to least visible, naming the area of the photo where each problem sits. 3. The main restoration prompt, ready to paste with the uploaded photo: repair each item on the damage map; keep the original pose, framing, expressions, facial features, hair, clothing, and background; do not add, remove, or beautify anyone. 4. Colorization rules if RestoreLevel asks for color: use KnownFacts exactly, choose muted period appropriate tones for everything else, and list which colors are guesses. 5. Three short follow up prompts for common misses: a face that changed, over smoothed or plastic looking skin, and invented background detail. 6. A side by side review checklist comparing the result with the original scan: faces, hands, ears, jewelry, patterns, and edges. 7. Output notes for FinalUse: the aspect ratio to request, whether a separate upscaler is needed, and how to label the file as AI restored when sharing with family. Constraints: - Never present the restored photo as historical truth; restored detail is a reconstruction. - Do not ask the model to identify anyone in the photo. - Results vary by tool and plan; tell me to compare every output with the original. - No em dashes.
Resignation Letter Writer: Notice Date and Handover Plan
Write a professional resignation letter with ChatGPT: check your notice dates, keep it short and gracious, plan the handover, and script the manager talk.
Act as an HR business partner who helps employees resign professionally: confirming the notice period, writing a short resignation letter, and planning a clean handover that protects the reference. Inputs: - Job title, team, manager's name, and company: [RoleDetails] - Notice period from my contract or handbook, the date I will hand in notice, and my planned last day: [NoticeTerms] - What I am willing to say about why I am leaving (or nothing): [ReasonToShare] - Open work, recurring duties, accounts, and documents someone must take over: [HandoverItems] - Questions about final pay, unused PTO, benefits end dates, equipment return, and references: [ExitQuestions] - My relationship with my manager and the tone I want (warm, neutral, brief): [ToneAndRelationship] - Output format: [Format] Generate: 1. A notice date check from NoticeTerms: count from the delivery date to the planned last day, compare with the required notice, and flag a shortfall or a last day that falls on a weekend or holiday. Note that how notice is counted depends on the contract. 2. A plan to tell the manager first: in person or video before the letter is sent, with a three line opening script and one line for a counteroffer question. 3. The resignation letter, under 200 words: the decision to resign, the last day, brief thanks, an offer to help with the transition, and nothing from ReasonToShare beyond what I approved. 4. Two variants of the resignation letter: a warmer one and a minimal one. 5. An email subject line and a one line cover note for sending the letter. 6. A handover plan table from HandoverItems: item, current status, proposed new owner, where the documents live, and a due date before the last day. 7. A list of questions for HR from ExitQuestions, worded neutrally. 8. What to leave out of the letter: complaints, comparisons with the new employer, confidential details, and anything I would not want in my file. Constraints: - This is writing help, not legal or employment advice; contracts and local law differ. - Do not invent company policies, notice rules, or payout entitlements. - No em dashes.
AI Prompt for Business Analysts: As-Is To-Be Gap Analysis
Turn workshop notes into a business analyst gap analysis: as-is steps, root causes, a to-be process, a gap table, traced requirements, and user stories.
Act as a senior business analyst who runs process improvement workshops and turns messy as-is notes into a gap analysis that sponsors can approve and delivery teams can build from. Inputs: - Process name, trigger, end point, and the business goal behind the change: [ProcessScope] - Current steps as described by the people who do them, including workarounds and spreadsheets: [AsIsNotes] - Pain points, complaints, and any measured numbers (cycle time, error counts, volumes) with their source: [PainPointsAndMetrics] - What good looks like according to the sponsor: [ToBeVision] - Systems, roles, and teams involved: [SystemsAndRoles] - Fixed limits such as budget, deadline, regulation, or systems we cannot replace: [Constraints] - Output format: [Format] Generate: 1. A scope statement for the process: trigger, end point, in scope, out of scope. 2. An as-is step table from AsIsNotes: step number, actor, action, system or tool, handoff, and a workaround flag. Quote the note a step came from when it is ambiguous. 3. A pain point to root cause map: each pain point, the step where it starts, and the likely cause type (process, people, technology, data, or policy). Label each cause as stated by stakeholders or as your hypothesis. 4. A to-be step table from ToBeVision and Constraints, with removed steps listed separately and new steps marked NEW. 5. A gap analysis table: as-is, to-be, gap, gap type, and the change needed to close it. 6. Numbered requirements derived from the gaps: business requirements (BR), functional requirements (FR) written as "The system shall...", and non-functional requirements (NFR), each traced to a gap row. 7. Two or three user stories with Given, When, Then acceptance criteria for the highest value functional requirements. 8. A RACI for the to-be process from SystemsAndRoles. 9. Measures of success: use only metrics from PainPointsAndMetrics as baselines; where none exists, write BASELINE NEEDED and say how to collect it. 10. Risks, assumptions, and open questions for the next workshop, each with an owner role. Constraints: - Do not invent volumes, durations, costs, or savings. - Keep requirements testable and free of product names unless the input names the system. - No em dashes.
YouTube Community Guidelines Strike Appeal Writer: Warning or Strike Status, Upload Freeze and 90 Day Expiry, Policy Section Match, Educational Documentary Context Check, Timestamped Appeal Text, and a Fix Plan
Respond to a YouTube Community Guidelines warning or strike without guessing: read what the notice means for your channel today, match the flagged timestamps to the named policy, check whether educational or documentary context is visible in the video itself, decide whether to appeal or fix, and write a short factual appeal for YouTube Studio.
Act as a YouTube channel operations manager who handles Community Guidelines enforcement for creators: reading the warning or strike notice, deciding whether to appeal or fix, and writing a short, factual appeal in YouTube Studio. Inputs: - The notice pasted exactly: warning or strike, the policy named, the removed video title, and the date: [ViolationNotice] - Channel standing: active strikes with dates, prior warnings, policy training taken, and monetization status: [StrikeHistory] - What the video shows and says at the moments most likely flagged, with timestamps: [FlaggedContent] - Context visible in the video itself: title, description, opening framing, on screen notes, and narration: [ContextOnScreen] - Your honest read: was this a misread, or did the content cross the line: [CreatorPosition] - Output format: [Format] Generate: 1. A status read from ViolationNotice and StrikeHistory: warning or strike, the restriction in effect now (for a strike, the upload and live stream freeze), when each active strike expires after 90 days, and how close the channel is to three strikes in 90 days and termination. 2. A policy match: summarize the named Community Guidelines policy, list the specific behaviors it covers that could apply to FlaggedContent, and rank which timestamps most likely triggered the review. 3. A context check: whether the educational, documentary, scientific, or artistic context YouTube weighs is visible in the video itself from ContextOnScreen, not only in the creator's intent, and what is missing. 4. An appeal or fix decision with reasons, tied to CreatorPosition: appeal when the content fits that context or was misread; accept and fix when it crossed the line. Note that each warning or strike gets one appeal, and that a warning can instead expire through the policy training. 5. The appeal text: polite, factual, first person, naming the policy, the timestamps, and the context already in the video; no threats, no comparisons to other channels, no new promises the video does not keep; sized to fit the Studio appeal form. 6. A fix plan if not appealing or if the appeal is rejected: what to edit, why reuploading removed content can bring another strike, and framing changes for future uploads. 7. A channel safety checklist: older videos with the same pattern to review, scheduled premieres or streams to pause, and notes for editors and moderators. Constraints: - Use only the notice, history, and timestamps given; if a detail is missing, write NOT IN NOTICE. - Enforcement rules change; tell the user to confirm current penalties on the YouTube Help Center. This is not legal advice. - No em dashes.
Microsoft Bookings Booking Page Setup Planner: Service Durations and Buffer Times, Staff Availability, Scheduling Policy Lead Times, Custom Booking Questions, Reminder Emails, and a Break It Test Plan
Plan a Microsoft Bookings page before you click through the settings: turn your services into durations with buffer times, map staff hours, pick lead times and time increments, write custom booking questions that keep sensitive details out of the form, size reminders to your no show pattern, and test it with bookings designed to break it.
Act as a Microsoft 365 administrator who configures Microsoft Bookings booking pages for small service teams so customers can self book without double bookings, no shows, or missing intake details. Inputs: - Business name, booking page type (shared booking page or personal page), and time zone: [BookingPageBasics] - Services: name, length, price or free, in person or Teams meeting, group size, and prep or cleanup time needed: [ServiceList] - Staff: names, roles, which services each delivers, working hours, and regular days off: [StaffRoster] - Booking rules: shortest notice you accept, how far ahead customers may book, cancellation window, and slot increments: [BookingRules] - Details you must collect before the appointment, and anything that must never go in a booking form: [IntakeNeeds] - Reminder preferences and your current no show pattern: [NoShowHistory] - Output format: [Format] Generate: 1. A booking page decision from BookingPageBasics: shared or personal page, who can book (anyone or only people in your organization), and whether customers may choose a particular staff member. 2. A Bookings service table from ServiceList: default duration, buffer time before and after, price display, location or Teams meeting toggle, maximum attendees for group services, and the staff assigned from StaffRoster. 3. Staff availability: for each person in StaffRoster, business hours or custom hours, blocked days, and a flag for anyone assigned to a service they cannot fit inside their hours. 4. A scheduling policy from BookingRules: minimum lead time, maximum lead time, time increments, and cancellation and reschedule notice, each with one line on why it fits the service table. 5. Custom booking questions from IntakeNeeds: exact question text, answer type, required or optional, and which services show it. Move anything sensitive out of the form into a secure follow up step. 6. Confirmation, reminder, and follow up emails per service: timing and the custom reminder text, sized to NoShowHistory. 7. A break it test plan: five test bookings that try back to back slots, buffer overlap, too short notice, a staff day off, and a full group class, with the expected result for each. 8. A launch checklist: where to link the booking page, what to tell staff, and what to review after the first two weeks. Constraints: - Use only services, staff, and hours from the inputs; mark any missing value NEEDS DECISION instead of guessing. - Bookings menu labels change over time; tell the user to confirm each setting name in their own tenant. - No em dashes.
Western Big Game Draw Application Planner: Preference vs Bonus Point Systems, Hunt Code Odds From State Draw Reports, Point Creep, First and Second Choice Strategy, Deadlines, and a Backup Plan
Plan your elk, deer, and antelope draw applications across western states: understand each state's preference or bonus point system, read draw odds and point creep from the state reports you paste, pick first and second choice hunt codes or points only, line up deadlines and fees, and keep an over the counter or leftover license backup.
Act as a western big game hunt planner who helps resident and nonresident hunters build yearly draw application strategies across states using each state's own draw reports and regulations. Inputs: - States, species, and residency status you are applying with, plus hunting party size: [StatesAndSpecies] - Current point balances by state and species, and the point type each state uses: [PointBalances] - Draw report data you pasted for the hunt codes you are considering: points needed to draw last few years, applicants, licenses, success rate, public land share: [HuntCodeStats] - Goals and limits: trophy vs opportunity, years you can wait, physical limits, dates you can hunt, total budget for fees and travel: [HuntGoals] - Deadlines and fees you have confirmed from this year's state brochures: [StateDeadlines] - Output format: [Format] Generate: 1. A system summary per state from PointBalances and StateDeadlines: preference, bonus, or hybrid, whether points are bought alone or with an application, and any rule that party applications average or use the lowest points. 2. A hunt code table from HuntCodeStats: points needed by year, the trend, and a point creep estimate in points per year, with success rate and public land share beside it. 3. A draw outlook for each code at your points this year and the next three years, marked likely, possible, or unlikely, based only on the pasted trend. 4. A choice plan per state: first choice code (a code you can likely draw or a long term code you build toward), second choice fallback, or points only, explained against HuntGoals. 5. A party check: how applying as a group changes the outlook in each state. 6. A calendar and cost sheet from StateDeadlines: application windows, refund rules for unsuccessful applicants, license and point fees, and the total against the budget in HuntGoals. 7. A backup plan if every draw fails: leftover license or reissue dates and over the counter options to confirm in the current regulations. Constraints: - Use only odds and point data from HuntCodeStats and only fees and dates from StateDeadlines; mark anything missing as CHECK BROCHURE. - Rules change yearly; tell the hunter to confirm every rule in the current regulations before applying. - No promises of drawing a tag. No em dashes.