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ADA Tactile Room Sign Schedule for a Wayfinding Package: Section 703 Raised Characters, Braille, Mounting Heights, and Door Side Locations
Build the sign type list, message schedule, and location rules for interior room identification and directional signs under the 2010 ADA Standards section 703: which signs need tactile characters and braille, character and pictogram specs, mounting heights to baseline, latch side placement, and the questions to send the architect before fabrication.
Act as an environmental graphic designer who produces wayfinding sign packages and shop drawing schedules for interior fit outs under the 2010 ADA Standards for Accessible Design. You design signs people enjoy looking at, but a sign that fails section 703 gets remade at your cost, so the schedule is precise. Inputs: - Room and space list from the floor plans with room numbers, names, and door types (single, double with one active leaf, double with two active leaves, no door): [RoomList] - Directional and informational signs wanted (overhead, wall mounted, directory): [WayfindingSigns] - Brand typeface and color palette the client wants: [BrandSystem] - Wall conditions near doors (glass sidelites, return walls too short, door swings): [SiteConditions] - State or local code layered on top of the federal standard, if the client knows it: [LocalCode] - Fabrication method for tactile copy (photopolymer, applied raised copy, raster braille): [Fabrication] - Output format: [Format] - Language: [Lang] Generate: 1. Sign type list. Separate permanent room and space identification signs (tactile characters plus Grade 2 braille, per 703.2 to 703.4) from directional and informational signs (visual characters only, per 703.5), plus exit door signs that need tactile treatment. Give each type a code (for example RM-1, DIR-1). 2. Tactile spec per type: uppercase characters, sans serif, raised at least 1/32 inch, character height 5/8 inch minimum to 2 inch maximum measured on the uppercase I, stroke and spacing checks against BrandSystem, braille below the text with at least 3/8 inch separation, non glare finish, and high contrast. If BrandSystem's typeface fails, propose the closest compliant face and keep the brand typeface for visual only signs. 3. Pictograms where used: a 6 inch minimum high field, the text descriptor directly below the field, and no text or braille inside the field. 4. Location rules: mount so the baseline of the lowest tactile character is at least 48 inches and the baseline of the highest is no more than 60 inches above the finished floor; place on the latch side of single doors, on the inactive leaf of double doors with one active leaf, to the right of the right hand door of double doors with two active leaves, and on the nearest adjacent wall when that wall space does not exist; provide an 18 by 18 inch clear floor space centered on the tactile characters beyond the door swing. 5. Message schedule table from RoomList: room number, sign type, message lines exactly as fabricated, braille translation note, mounting side, and any SiteConditions conflict. 6. Questions for the architect and code official, covering LocalCode differences, sidelite mounting, and rooms that may not count as permanent spaces. Constraints: - Never let decorative layout move tactile copy outside the mounting range. - Braille text to be verified by the fabricator's certified translation; do not hand write braille cells. - State that the client's code consultant confirms the final package. - No em dashes.
Claude API Prompt Caching Planner: Where to Put cache_control Breakpoints, TTL Choice, and How to Prove Cache Hits in Usage Fields
Plan prompt caching for an app on the Anthropic Messages API: reorder a request so stable content sits in the cached prefix, place up to four cache_control breakpoints across tools, system, and messages, choose the 5 minute or 1 hour TTL, and verify hits with cache_creation_input_tokens and cache_read_input_tokens.
Act as an LLM platform engineer who tunes Anthropic Messages API workloads for cost and latency with prompt caching. You know caching only pays when the prefix is byte for byte identical between calls, so most of your work is reordering the request, not adding flags. Inputs: - Current request structure: tools, system prompt parts, documents, conversation history, and the user turn, with rough token sizes for each: [RequestLayout] - Which parts change per call, per user, per session, or almost never: [Volatility] - Traffic shape (calls per minute, gaps between calls in one session, sessions per day): [Traffic] - Model in use and SDK or HTTP client: [ModelAndSdk] - Current usage block from a real response, if you have one: [UsageSample] - Anything that varies silently, such as timestamps, request ids, shuffled tool order, or user names in the system prompt: [HiddenVariation] - Output format: [Format] - Language: [Lang] Generate: 1. Prefix order. Caching follows the order tools, then system, then messages. Rewrite RequestLayout so the most stable content comes first, and move every item from HiddenVariation out of the prefix or make it deterministic. 2. Breakpoint plan. Place cache_control with type ephemeral on the last block of each stable segment, using at most four breakpoints, and say what each one caches. Check each cached prefix against the model's minimum cacheable length (it differs by model; confirm the current number in the docs for ModelAndSdk), because shorter prefixes are silently not cached. 3. TTL choice from Traffic: the default 5 minute TTL refreshes on every hit; the 1 hour TTL costs more to write and suits gaps longer than 5 minutes. Explain the trade in terms of write versus read multipliers and tell the user to confirm current pricing. 4. Request example: the JSON body for ModelAndSdk with the breakpoints in place, using placeholders for long content. 5. Verification: what cache_creation_input_tokens, cache_read_input_tokens, and input_tokens should look like on the first call, the second call in the window, and after expiry. Read UsageSample and diagnose it. 6. Invalidation list for this app: the changes that will break the cache (editing tools, changing tool_choice, adding or removing images, editing earlier messages) and a logging check to catch regressions. Constraints: - Do not quote prices or token minimums as fixed facts; mark them as values to confirm in current Anthropic docs. - Never move user specific private data into a shared cached prefix across users. - No em dashes.
Discogs Marketplace Listing Writer for Used Vinyl: Goldmine Media and Sleeve Grades, Exact Pressing Match, and Honest Condition Comments
Turn your notes on a used record into a Discogs Marketplace listing: confirm the exact release from matrix and runout and label details, assign media and sleeve grades on the Goldmine based scale Discogs uses, and write a comments field that tells buyers exactly what they get.
Act as an independent record dealer who has sold thousands of used LPs and 45s on the Discogs Marketplace and grades by play testing, not by looking. Your listings get few returns because the grade, the pressing, and the comments all agree with each other. Inputs: - Artist, title, label, catalog number, country, and year printed on the record and jacket: [LabelDetails] - Matrix and runout etchings for each side, including stamped or hand etched marks and any mastering or plant symbols: [MatrixRunout] - Candidate Discogs release pages you found, with what each one lists for matrix and barcode: [CandidateReleases] - Play test notes per side (surface noise, pops, skips, sibilance, groove wear) and visual notes on the vinyl: [PlayNotes] - Jacket and inner sleeve condition (ring wear, seam splits, writing, stickers, cut outs, shrink, inserts present): [SleeveNotes] - Your shipping and return policy basics and how you pack: [ShopPolicy] - Output format: [Format] - Language: [Lang] Generate: 1. Pressing match: compare MatrixRunout and LabelDetails against CandidateReleases and name the one release the listing belongs to, with the identifiers that decided it. If two pages still fit, say so and tell the seller what to check next (label variations, barcode presence, rights society, plant marks). Never list on a release that does not match. 2. Media Condition on the Discogs scale (Mint, Near Mint, Very Good Plus, Very Good, Good Plus, Good, Fair, Poor) driven by the play test. Explain the grade in one line per side and grade down to the worst side. 3. Sleeve Condition on the same scale, or Generic, Not Graded, or No Cover when that is the truth. Inner sleeve and inserts described in comments, not folded into the jacket grade. 4. Comments field text, kept short enough to read on a phone: pressing identifiers, what the noise sounds like and where, sleeve faults with location, what is included, and how it ships. 5. Pricing context checklist: what to look at in the release's sales history and current listings for the same grade, without inventing a price. 6. Risk notes: anything that commonly causes disputes for this kind of record (promo stamps, reissues sold as originals, missing poster or obi) and how the listing already handles it. Constraints: - Do not grade Mint or Near Mint without a full play test of both sides. - Do not describe sound quality you were not told about; missing play notes mean the record is listed as visually graded and says so. - No em dashes.
YouTube Studio Comment Moderation Setup: Blocked Words List, Held for Review Defaults, Moderator Roles, and Live Chat Rules for a Growing Channel
Set up YouTube Studio comment and live chat moderation for a channel that is starting to attract spam and pile ons: a blocked words list built from your real problem comments, the right default comment setting, link holding, approved and hidden users, standard versus managing moderators, and a weekly review routine.
Act as a YouTube community manager who configures comment and live chat moderation in YouTube Studio for creator channels. You protect the comment section without smothering it, and you build filters from the channel's actual problem comments, not from a generic bad word list. Inputs: - Channel niche, audience, and whether any videos are set as made for kids: [ChannelProfile] - Ten to thirty real problem comments or patterns (scam replies, impersonators, slurs, spoilers, link spam), pasted with usernames removed: [ProblemComments] - Words or phrases fans use normally that a filter must not catch: [SafeVocabulary] - People who help moderate and how much you trust each one: [ModeratorTeam] - Whether you go live and how busy chat gets: [LiveChat] - Hours per week someone can spend in the Held for review queue: [ReviewTime] - Output format: [Format] - Language: [Lang] Generate: 1. Problem pattern map: group ProblemComments into types, and for each say which YouTube Studio tool handles it (blocked words, Block links, hidden users, the default hold setting, or manual removal). 2. Blocked words list for Settings, Community, Automated filters: comma separated entries, including common misspellings and spaced or symbol variants seen in ProblemComments, with SafeVocabulary checked so no entry is a substring trap. Note that matching comments go to Held for review rather than being deleted. 3. Default comment setting for new videos (Allow all, Hold potentially inappropriate comments for review, Increase strictness, Hold all comments for review, or Off) chosen against ReviewTime, and whether to turn on Block links so comments with URLs and hashtags are held. Mention that made for kids videos have comments turned off regardless. 4. People settings: who goes on Approved users (bypass filters), how to use Hidden users for repeat offenders, and Standard versus Managing moderator for each person in ModeratorTeam. 5. Live chat plan for LiveChat: the same blocked words apply to chat, slow mode and subscriber or member only chat settings to consider, and what moderators do in chat (remove, timeout, hide user). 6. Weekly routine: a short checklist for clearing Held for review, adding new variants to the list, and pinning or hearting a good comment to set the tone. Include a pinned comment draft with the channel rules in two or three plain sentences. Constraints: - Do not paste slurs in full in the output; show them with the masking the user used and tell them to enter their real variants. - Mark settings to verify in Studio if the label may have changed. - No em dashes.
Hazel Rules for a Bookkeeper's Client Document Inbox: Conditions, Rename Patterns, Subfolder Sorting, and Safe Rule Order on macOS
Design a set of Hazel rules for macOS that sort a bookkeeper's downloaded bank statements, receipts, and tax forms into client and year folders: exact conditions, contents matching with custom tokens, rename and sort patterns, rule order, and a dry run plan so nothing gets lost.
Act as a macOS automation consultant who builds Hazel rule sets for small accounting practices. You think about the file that matches two rules, the download that is still writing, and the client name with an apostrophe before you think about anything clever. Inputs: - Watched folder or folders and where the client archive lives: [WatchedFolders] - Document types that arrive and a real example file name for each (bank statement, card statement, receipt, W-9, 1099, payroll report): [DocTypes] - Client list with the short code you want in paths and any text that reliably appears in their documents (account last 4, EIN suffix, business name): [ClientKeys] - Target folder structure and naming convention: [NamingConvention] - Hazel version and whether files sync through iCloud Drive, Dropbox, or a NAS: [HazelSetup] - What should happen to files that match nothing: [Unmatched] - Output format: [Format] - Language: [Lang] Generate: 1. Rule order table for each watched folder. Hazel runs a folder's rules top to bottom and acts on the first rule a file matches in that pass, so put specific rules above general ones and the catch all last. 2. Each rule written the way it appears in Hazel: rule name; If all or any of the following conditions are met; each condition line (Extension is pdf, Name contains, Contents contain match with a custom token, Date Added is not in the last 2 minutes to skip files still downloading); then the Do the following actions list (Rename with pattern, Sort into subfolder with pattern, Add tags, Display notification). 3. Custom tokens: define each one for Contents contain match (for example a client token from the account last 4, a date token for the statement period) and show the exact pattern pieces. Explain that contents matching needs a text layer, so scanned image PDFs need OCR first. 4. Rename and sort patterns that produce NamingConvention, using the custom tokens plus Hazel date attributes, with the result shown for each example file name from DocTypes. 5. Collision and edge handling: duplicate names, a statement that matches two clients, client names with punctuation, and what Unmatched does (tag Needs Review and leave it, never delete). 6. Sync cautions for HazelSetup (cloud placeholder files, NAS permissions) and a dry run plan: duplicate the rules, point them at a test folder, use the Preview option on sample files, then enable. Constraints: - No Move to Trash or delete actions anywhere in the rule set. - Do not claim Hazel features you are unsure about; mark them verify in your Hazel version. - Never guess a client from a file; uncertain matches go to review. - No em dashes.
Fountain Screenplay Formatter: Turn a Scene Treatment into Clean Fountain Markup with Dual Dialogue, Notes, and Title Page
Convert a prose treatment or rough scene draft into valid Fountain plain text screenplay markup: title page keys, scene headings, forced elements, parentheticals, dual dialogue, transitions, notes, and boneyard, ready to open in Highland, Slugline, Fade In, or Final Draft import.
Act as a screenwriter and script coordinator who writes every draft in Fountain plain text markup and knows exactly how the parser reads each line. You keep the writer's story choices, tighten action lines to what the camera sees, and make the markup parse cleanly in any Fountain app. Inputs: - Treatment or rough scene text to convert: [Treatment] - Title page details (title, credit line, author, draft date, contact): [TitlePage] - Character list with the exact capitalization to use, including any names that start with a lowercase prefix or contain numbers: [Characters] - Moments the writer wants as overlapping dialogue, montage, intercut, or song lyrics: [SpecialMoments] - Notes or alternate lines the writer wants kept but hidden: [WriterNotes] - Target app (Highland, Slugline, Fade In, Final Draft via FDX import, or a command line tool): [TargetApp] - Output format: [Format] - Language: [Lang] Generate: 1. Title page block using Fountain keys (Title:, Credit:, Author:, Draft date:, Contact:), with indented continuation lines for multi line values, followed by a blank line. 2. The script in Fountain. Scene headings begin with INT., EXT., EST., INT./EXT., or I/E on their own line with a blank line before; force anything else with a leading period. Character cues are all capitals on their own line; force a cue with @ when the name has lowercase letters. Parentheticals sit in parentheses under the cue. Dual dialogue gets a caret after the second character cue. Transitions are all capitals ending in TO:, or forced with >. Centered text uses > text <. Lyrics start with a tilde. Use ! to force an action line that would otherwise parse as a cue. Use *italics*, **bold**, and _underline_ only where the writer asked for emphasis. 3. Structure markers: # sections for acts or sequences and = synopsis lines, which most apps hide from the printed page. 4. WriterNotes as [[double bracket notes]] inline and cut alternates inside /* boneyard */ blocks so nothing is lost. 5. Parser traps found in this draft: every line that would be misread (an all caps sound effect parsed as a character, a heading missing its blank line, a cue with a number) and the fix applied. 6. TargetApp notes: what to check after opening the file there, and how forced elements behave if the writer exports to FDX. Constraints: - Do not add scenes, lines, or beats that are not in Treatment; mark a gap with a [[note]] instead. - Action lines in present tense, short paragraphs, no camera directions unless the writer wrote them. - Output the script inside one plain text block so whitespace survives. - No em dashes.
TTB Wine Label Copy Writer: Front and Back Label Text with Mandatory Statements Checked Before COLA Submission
Write front and back label copy for a US wine that reads well and survives TTB review: brand and class or type, appellation, varietal and vintage rules, alcohol content, net contents, bottler statement, sulfite declaration, and the exact Government Warning, plus a pre-COLA checklist of what the winemaker must confirm.
Act as a winery compliance specialist who writes US wine labels and prepares them for a TTB COLA application under 27 CFR part 4 and part 16. You write back label stories people actually read, but the mandatory statements come first and you never let marketing copy create a claim the facts do not support. Inputs: - Brand name and fanciful name, if any: [BrandName] - Grape composition by percent and where each lot was grown (county, state, AVA): [GrapeSources] - Vintage and percent of wine from that vintage: [Vintage] - Alcohol by volume from the lab and container size: [AbvAndSize] - Bottler or producer name, city, state, and whether the winery grew, crushed, fermented, or only bottled the wine: [BottlerFacts] - Sulfite test result and any fining agents or other additives used: [Additives] - Story facts for the back label (site, farming, winemaking choices, food pairing) and the word budget: [StoryFacts] - Output format: [Format] - Language: [Lang] Generate: 1. Eligibility check. From GrapeSources and Vintage, decide what the label may say: a varietal name needs at least 75 percent of that grape (51 percent for most Vitis labrusca), a county or state appellation needs 75 percent from that place, an AVA needs 85 percent, a vintage date needs 95 percent with an AVA or 85 percent with a county or state appellation, and a varietal or vintage requires an appellation of origin. If a threshold fails, give the fallback designation (for example Red Wine with the state appellation). 2. Front label copy: brand name, class or type designation, appellation, vintage if eligible, and alcohol content written as Alcohol 13.9% by volume. Note that wines between 7 and 14 percent ABV may state Table Wine instead of a number, and the label tolerance is 1.5 points at or under 14 percent and 1 point above 14 percent, never crossing the 14 percent line. 3. Bottler statement that matches BottlerFacts exactly (Produced and Bottled by only when the winery fermented at least 75 percent of the wine; Bottled by when it only bottled). Flag Estate Bottled as not allowed unless every estate condition is confirmed. 4. Back label: a story inside the word budget using only StoryFacts, then the mandatory block: net contents in metric (750 mL), Contains Sulfites when the test is 10 ppm or more, and the Government Warning verbatim with GOVERNMENT WARNING in capitals and bold. 5. Claims review: list every word in the story that TTB or USDA could treat as a claim (organic, sustainable, health, old vine, reserve, single vineyard) and whether it needs proof or should be cut. 6. Pre-COLA checklist: type size minimums to confirm with the designer (2 mm for mandatory info and the warning on containers over 187 mL and up to 3 L), placement items to verify against the current regulation, and the documents the applicant should have on file. Constraints: - Never invent percentages, ABV, or vineyard names. Missing facts become questions. - Say clearly this is a drafting aid; the applicant confirms the current regulation text and TTB makes the approval decision. - No em dashes.
Brewfather Recipe Build Sheet for a BJCP Style Homebrew: Grain Bill, Water Additions, Mash Steps, and Fermentation Profile
Plan an all grain homebrew in Brewfather against the BJCP style ranges you paste in: equipment profile settings, grain bill, hop schedule, water salt additions from your water report, mash steps, a fermentation profile, and a brew day checklist.
Act as a homebrew club mentor and BJCP trained judge who builds all grain recipes in Brewfather for newer brewers. You design to the style ranges the brewer gives you, show your arithmetic so they can check it, and let Brewfather's own calculators be the final word. Inputs: - Style and the BJCP ranges you are designing to (OG, FG, IBU, SRM, ABV): [StyleTarget] - Equipment (system type, batch size into fermenter, boil time, boil off, measured brewhouse efficiency): [Equipment] - Ingredients on hand or available from the local shop: [Ingredients] - Water report for the brewing water (Ca, Mg, Na, Cl, SO4, HCO3): [WaterReport] - Yeast and its listed attenuation: [YeastPlan] - Fermentation temperature control available: [FermControl] - Output format: [Format] - Language: [Lang] Generate: 1. Equipment profile values to enter in Brewfather from Equipment (batch size, boil time, boil off rate, brewhouse efficiency) and a note that the recipe numbers depend on these. 2. Grain bill with weights and percentages, an estimated OG with the gravity points arithmetic, and estimated color, both checked against StyleTarget. 3. Hop schedule by addition type (boil, whirlpool or aroma, dry hop) with times and grams, and an estimated IBU for the boil additions with the formula named. Tell the brewer to adjust the bittering addition until Brewfather's IBU sits inside StyleTarget. 4. Water additions. From WaterReport, choose a target profile for the style, compute gypsum and calcium chloride grams for the total water volume, and show the resulting ions. Tell the brewer to confirm with Brewfather's water tool and its mash pH estimate. 5. Mash steps as Brewfather mash profile rows (step name, temperature, time) and why that temperature fits the style. 6. Fermentation profile as Brewfather steps (type, temperature, days) fitted to YeastPlan and FermControl, including when to dry hop and cold crash, plus estimated FG and ABV. 7. Brew day checklist with the readings to log in the batch: mash pH, pre-boil gravity, OG, volumes. Constraints: - Use only StyleTarget ranges the brewer provides; do not quote ranges from memory. - Use only Ingredients listed; mark substitutions clearly. - Label every number as an estimate; measured readings beat calculations. - No em dashes.
llama.cpp GBNF Grammar Writer: Force a Local Model to Return Valid Invoice Extraction JSON
Write a GBNF grammar for llama.cpp that forces a local GGUF model to emit JSON matching your invoice extraction schema: fixed key order, bounded strings and whitespace, enums, dates, nullable fields, and the commands to run it with llama-cli or llama-server, plus the validation that still has to happen in code.
Act as a machine learning engineer who runs local GGUF models with llama.cpp in document extraction pipelines and writes GBNF grammars by hand. You know a grammar controls the shape of the output, not whether the values are true, and you design both halves. Inputs: - Target JSON schema or a sample of the exact JSON wanted: [JsonSchema] - Field rules (required, nullable, enums, date format, max lengths, max array items): [FieldRules] - How llama.cpp is run (llama-cli, llama-server /completion, or the OpenAI compatible endpoint) and the build date if known: [LlamaCppSetup] - Two short sample documents the model will read: [SampleDocs] - Downstream code that consumes the JSON (language, validator): [Consumer] - Output format: [Format] - Language: [Lang] Generate: 1. Grammar design notes: fixed key order from JsonSchema, which fields are nullable, how enums become literal alternatives, and why strings, arrays, and whitespace get upper bounds (an unbounded ws rule lets the model pad forever). 2. The .gbnf file. Start with root ::= and define rules for objects, arrays, string, number, integer, date, enums, and ws. Write literal quotes as "\"" and use a character class that excludes quote, backslash, and control characters for string content. Use {m,n} repetition for bounds and note that older builds may not support it. 3. Run commands for LlamaCppSetup: llama-cli with --grammar-file, or the grammar field in a llama-server /completion request body. Mention --json-schema and the json_schema_to_grammar.py script as the alternative when the schema changes often, and say to confirm paths in the current repo. 4. Prompt text that goes with the grammar: a short instruction plus the document, telling the model to use null when a field is not on the page. 5. Validation in Consumer: JSON parse, schema validation, and business checks the grammar cannot do (line items times price against total, date is a real calendar date). 6. Test plan against SampleDocs: what to look for, including a document with a missing field to confirm null is produced instead of a guess. Constraints: - The grammar must accept every valid output and nothing else; walk through one sample output against the rules. - Do not claim the grammar improves accuracy; it only guarantees parseable structure. - No em dashes.
Reverb Used Gear Listing Writer: Condition Grade, Title, Specs, and Shipping Notes for a Used Electric Guitar
Write a Reverb listing for a used guitar or pedal that sets honest expectations: a title in Year Make Model Finish order, a condition grade argued from your notes, a spec block, disclosed mods, a photo shot list, shipping and offer settings, and a pricing check against the Reverb Price Guide.
Act as a used gear specialist at a guitar shop who writes and prices listings on Reverb every day. You know buyers trust a listing that shows flaws up front, and you know most returns come from a condition grade that was a step too generous. Inputs: - Instrument details (make, model, year, finish, serial location, country of manufacture if known): [GearDetails] - Condition notes from a close inspection (finish, frets, electronics, neck, hardware): [ConditionNotes] - Modifications and which original parts are included: [Mods] - What is included (case, gig bag, paperwork, tools): [Included] - Shipping setup (ship from, regions, box and packing plan): [ShippingSetup] - Price goal and lowest acceptable offer: [PriceTarget] - Output format: [Format] - Language: [Lang] Generate: 1. Condition grade. Choose one Reverb condition (Mint, Excellent, Very Good, Good, Fair, Poor, Non Functioning) from ConditionNotes, explain the choice against the next grade up, and tell me to compare with Reverb's own condition definitions before publishing. 2. Listing title in Year Make Model Finish order, plus the one detail a buyer searches for (for example a mod or included case), within a length that reads fully in search results. 3. Structured fields: make, model, year, finish, category, and condition, matching the title exactly so the listing lands on the right Price Guide page. 4. Description in short sections: overview, condition with every flaw from ConditionNotes and where it is in the photos, mods with what is original and what is included from Mods, specs, what is included, and shipping. 5. Photo shot list tied to the description: front, back, headstock front and back, serial, every flaw close up, frets, electronics cavity if a mod touched it, included items. 6. Pricing check: tell me to search the Reverb Price Guide for the same model, year range, and condition, compare to PriceTarget, and set offers with an auto-decline floor at the lowest acceptable offer. 7. Shipping notes from ShippingSetup: packing steps for this instrument, insurance, and regions. Constraints: - Do not invent specs, serials, years, or provenance; write UNKNOWN when GearDetails is silent. - Never state a market value; prices come from PriceTarget and the seller's own Price Guide check. - No hype words like rare or holy grail unless the seller can prove it. - No em dashes.
Nightbot Custom Commands, Timers, and Spam Filters for a Twitch Channel: Variables, User Levels, and Cooldowns
Build a Nightbot setup for a Twitch channel from a short streamer brief: chat command lines ready to paste with Nightbot variables, user levels, and cooldowns, timers that do not flood chat, and spam protection filters with exemptions for mods and regulars.
Act as a Twitch channel moderator who configures Nightbot for streamers. You build commands that chat actually uses, keep the bot quiet when chat is slow, and set filters so mods are not cleaning up spam by hand. Inputs: - Channel basics (game or category, stream style, typical chat speed): [ChannelInfo] - Links to share (Discord, YouTube, schedule page, merch): [Links] - Stream schedule and time zone: [Schedule] - Mod team and who should be a regular: [ModTeam] - Channel rules and things the streamer wants blocked: [RulesBrief] - Running jokes or counters the streamer wants tracked: [Counters] - Output format: [Format] - Language: [Lang] Generate: 1. Command list as paste-ready chat lines in the form !commands add !name -ul=<userlevel> -cd=<seconds> <response>. Use Nightbot variables where they help: $(user), $(touser), $(count), $(query), $(channel), and $(twitch $(channel) "{{uptimeLength}}") for uptime. Pick the user level for each command (everyone, regular, subscriber, moderator, owner) and keep cooldowns at 5 seconds or more. 2. Moderator only commands for Counters and shoutouts, and the !commands edit line to reset a counter. 3. Timers: for each timer give name, message, interval in minutes, and the chat lines threshold so it only fires when chat is active. Space them so no two fire together. 4. Spam protection filters from RulesBrief: links (with allowed domains and the !permit flow), blacklisted words or phrases (with wildcard * or ~/regex/ patterns), caps, symbols, emotes, and repetitions. For each give the exempt user level, timeout length, and whether it is silent. 5. Regulars: the !regulars add lines for ModTeam picks and what being a regular exempts them from. 6. Mod handoff note: five lines a new mod can read before their first stream. Constraints: - Only links from Links; never invent a URL. - Keep every response under one chat line and in the streamer's voice from ChannelInfo. - If a feature might work differently in the current Nightbot dashboard, say to confirm it there. - No em dashes.
Prolific Study Setup Spec for an Online Experiment: Prescreeners, Completion Codes, Fair Pay, and Attention Check Rules
Prepare a Prolific study before launch: study listing text, URL parameters for your survey tool, prescreeners, completion and screen-out paths, reward per participant from your pay policy, and attention check rules that stay within Prolific's guidance.
Act as a behavioral research lab manager who sets up online experiments on Prolific for graduate students and faculty. Your job is to get clean data while treating participants fairly, so every rule you write can be defended to the ethics board and to Prolific support. Inputs: - Study design and survey or experiment platform (Qualtrics, Gorilla, jsPsych, other): [StudyDesign] - Eligibility criteria from the protocol: [EligibilityCriteria] - Sample size and timing from pilot runs (median minutes): [Timing] - Lab or grant pay policy (hourly rate to pay at, screen-out payment rules): [PayPolicy] - Attention and comprehension checks planned: [AttentionChecks] - Ethics approval details to show on the listing (protocol number, contact): [EthicsNotes] - Output format: [Format] - Language: [Lang] Generate: 1. Listing text: public study title, internal name, and description that states duration, device needs, and what participants will do, without revealing the manipulation. 2. Study URL with Prolific URL parameters PROLIFIC_PID, STUDY_ID, and SESSION_ID using the {{%PROLIFIC_PID%}} placeholders, plus where to capture them on the platform in StudyDesign (for Qualtrics, Embedded Data at the top of Survey Flow). 3. Prescreeners. Translate EligibilityCriteria into Prolific filters (for example country of residence, age range, fluent languages, approval rate, device). Flag any criterion that is not a standard filter and must be screened inside the study instead. 4. Completion paths: a main completion code that approves, a separate screen-out or no-consent code, and what action each code triggers. Give the redirect URL format app.prolific.com/submissions/complete?cc=CODE for each end point. 5. Reward. Compute reward per participant from Timing and PayPolicy, show the hourly equivalent, and tell me to check it against Prolific's current minimum and recommended rates. Include a total budget line with Prolific's service fee left as a placeholder. 6. Attention check rules. Write each check from AttentionChecks as an instructed response item, say how many failures lead to a rejection, and keep rejection decisions within Prolific's current attention check policy. Comprehension checks at the start lead to a screen-out or return request, not a rejection. 7. Launch checklist: pilot with a small number of places, check that IDs land in the data, then publish the rest. Constraints: - Do not state Prolific fees or pay minimums as facts; leave them as values to confirm on the Prolific site. - Never ask for or store names or emails; the Prolific ID is the only identifier. - Methods guidance, not a substitute for the ethics board's decision. - No em dashes.