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Backyard Beekeeper Varroa Mite Plan: Alcohol Wash Sampling, Mites per 100 Bees, Label Directed Treatment Choice by Season and Temperature, and a Hive Record Log
Build a season by season varroa plan for a few backyard hives: when and how to run an alcohol wash or sugar roll, how to convert the count to mites per 100 bees, which treatment fits the current brood status, temperature, and honey supers according to the product label, how to rotate modes of action, and a simple record log so every hive has a sample and treatment history.
Act as a county beekeeping club mentor who teaches first and second year beekeepers to monitor varroa mites and choose treatments by reading the label, not by copying what a neighbor used. Inputs: - Hives: count, hive type, colony strength, and queen status for each: [HiveList] - Location and the typical daytime highs and lows for the coming weeks: [ClimateWindow] - Current season and brood status (heavy brood, reduced, broodless or nearly broodless): [BroodStatus] - Honey supers on or off, and when the next harvest is planned: [HoneySupers] - Recent mite counts with the method used (alcohol wash, sugar roll, sticky board) and number of bees sampled: [MiteCounts] - Treatments the beekeeper owns or is willing to buy, and any used in the past year: [TreatmentOptions] - The action threshold the beekeeper follows and where it comes from (club guidance, extension service, a published varroa guide): [ActionThreshold] - Output format: [Format] Generate: 1. Sampling method: step by step alcohol wash or sugar roll with about half a cup of nurse bees from a brood frame, never the queen, and how to count and convert to mites per 100 bees. Show the math on each MiteCounts entry. 2. Hive by hive status: compare each converted count with ActionThreshold and label each hive monitor, treat soon, or treat now. 3. Treatment fit table for the hives that need action: for each product in TreatmentOptions, whether it fits BroodStatus, ClimateWindow temperatures, and HoneySupers based on its label, with every specific temperature range, duration, and supers rule marked READ THE LABEL so the beekeeper confirms it on the package in hand. 4. Rotation note: group TreatmentOptions by active ingredient and recommend not relying on the same mode of action back to back. 5. A dated plan for the next eight weeks: sample dates, treatment start and end, post treatment wash to confirm it worked, and when supers can go back on per the label. 6. Safety and equipment list: protective gear the label requires (for example gloves and a respirator for oxalic acid vaporization), and storage of treatments. 7. A hive record log template with columns for date, hive, method, bees sampled, mites counted, mites per 100, action, product, lot number, and notes. Constraints: - The label is the law for pesticide use. Never recommend off label doses, homemade mixes, or treating with supers on unless the label allows it. - Do not invent thresholds; use ActionThreshold. No em dashes.
Material 3 Color Scheme Builder: Seed Color to Tonal Palettes, Light and Dark Color Roles, Contrast Levels, and Token Exports for Compose, Flutter, and CSS
Turn a brand color into a complete Material Design 3 color scheme the way Material Theme Builder does: pick the seed and scheme variant, map primary, secondary, tertiary, error, surface, and surface container roles for light and dark, check on color contrast pairs, handle a brand color that clashes with its generated tone, and export tokens for Jetpack Compose, Flutter ThemeData, and CSS custom properties.
Act as a product designer who maintains a Material Design 3 theme for an Android and web product and has shipped brand colors through Material Theme Builder without breaking contrast in dark mode. Inputs: - Brand colors as hex values with the role each should play (main brand, accent, alert): [BrandColors] - Scheme variant preference (Tonal Spot, Fidelity, Content, Vibrant, Expressive, Neutral, Monochrome) or no preference: [SchemeVariant] - Platforms and how the theme is consumed (Jetpack Compose, Flutter, CSS custom properties, Figma variables): [Platforms] - Contrast level support needed (standard, medium, high) and whether dynamic color from wallpaper is allowed on Android: [ContrastNeeds] - Components that must look on brand (top app bar, FAB, filled buttons, chips, navigation bar): [KeyComponents] - Output format: [Format] Generate: 1. Seed decision: which BrandColors value becomes the seed and which become custom or extended colors, and which SchemeVariant keeps the brand recognizable. Fidelity and Content keep the seed closer to its source color; say what Tonal Spot would shift. 2. Tonal palette notes: the key tones Material 3 uses for each role in light and dark (for example primary from tone 40 in light and tone 80 in dark), and where the exact brand hex will land or not land. Mark computed hex values as values to read from Material Theme Builder, not guessed. 3. A color role table for light and dark: primary, onPrimary, primaryContainer, onPrimaryContainer, secondary and tertiary sets, error set, surface, onSurface, onSurfaceVariant, outline, outlineVariant, and surfaceContainerLowest to surfaceContainerHighest, with what each is used for in KeyComponents. 4. Contrast checks for the pairs that matter: text on primary, text on primaryContainer, onSurfaceVariant on surface, outline against surface, with the WCAG ratio target for each and how the contrast levels in ContrastNeeds change them. 5. Brand clash fixes: when the exact brand hex must appear (logo button, FAB), use a custom color with harmonize turned on or off, and document the tradeoff. 6. Token export skeletons for each item in Platforms: Compose lightColorScheme and darkColorScheme, Flutter ColorScheme with ThemeData useMaterial3, and CSS variables named --md-sys-color-*. 7. A QA checklist for dynamic color on Android 12 and later, if allowed, and for screenshots in both modes. Constraints: - Do not invent computed hex values or contrast ratios. Use [hex from Theme Builder] placeholders where a tool must calculate. - No em dashes.
OpenAI Batch API Job Planner: JSONL Request Files with custom_id, Structured Output Schemas, Status Polling, and Result Joins for Bulk Classification
Plan a bulk LLM job on the OpenAI Batch API instead of looping over the regular endpoint: one JSONL line per record with a stable custom_id, a strict JSON schema for the answer, file upload with purpose batch, the batch create call with a 24h completion window, status polling, and a script that joins output and error files back to your source rows by custom_id and retries only the failures.
Act as an ML platform engineer who runs large offline classification and extraction jobs on the OpenAI Batch API and has debugged batches that failed validation or came back with results nobody could match to their source rows. Inputs: - The task in one sentence and the labels or fields each record should produce: [TaskSpec] - Source data shape: file type, row count, the primary key column, and two sample rows: [SourceRows] - Model and endpoint the team has chosen (for example /v1/chat/completions or /v1/responses): [ModelEndpoint] - The system instructions and any few shot examples, as they work today on single calls: [CurrentPrompt] - Language and runtime for the scripts (Python with the openai SDK, Node, plain curl): [Runtime] - Deadline and how results will be stored (CSV, database table, warehouse): [ResultStore] - Output format: [Format] Generate: 1. Job design: why Batch fits or does not fit the deadline in ResultStore, how to split SourceRows into batch files that stay under the current per batch request and file size limits (mark the exact limits VERIFY in the docs), and the rule that each input file targets one model and one endpoint. 2. A JSON schema for the answer derived from TaskSpec, with strict mode, required fields, enums for labels, and additionalProperties false. 3. One complete example JSONL line with custom_id built from the SourceRows primary key, method POST, url matching ModelEndpoint, and a body containing the model, CurrentPrompt, the record, and the schema in the response format field for that endpoint. 4. Script steps in Runtime: build the JSONL, validate every line parses and custom_id values are unique, upload the file with purpose batch, create the batch with completion_window 24h and metadata, and poll status through validating, in_progress, finalizing, and completed or failed, expired, cancelled. 5. Result join: download output_file_id and error_file_id, parse each line, match by custom_id because output order is not guaranteed, parse the schema answer, and write to ResultStore with columns for status, error code, and raw text on parse failure. 6. Retry plan: build a new JSONL from only failed or expired custom_ids, and a spot check of a small random sample by a human before trusting the labels. Constraints: - Do not state prices, discounts, rate limits, or turnaround times as facts. Tell the user where to check them. - Never put API keys in files or examples; read them from an environment variable. No em dashes.
Genealogy Research Log and Proof Summary: Genealogical Proof Standard Analysis, Evidence Explained Style Citations, and GEDCOM Ready Source Records for a Conflicting Ancestor
Sort out an ancestor whose records disagree on birth date, birthplace, or parents: log every search including the negative ones, classify each source and piece of evidence, resolve the conflict with a written proof summary that follows the Genealogical Proof Standard, cite each record in Evidence Explained style, and get source and citation entries ready to paste into a family tree program that exports GEDCOM.
Act as a professional genealogist who writes client reports and proof summaries for conflicting evidence cases and follows the Genealogical Proof Standard rather than accepting the first family tree that agrees with a hunch. Inputs: - Research question in one sentence, naming the person, the fact in question, and the place and time: [ResearchQuestion] - Records found, each with repository or website, collection, image or film number, and the exact wording of the relevant entry: [RecordsFound] - Searches that found nothing, with what was searched and how: [NegativeSearches] - Family details already known (spouse, children, neighbors, migration): [KnownContext] - Family tree software or GEDCOM version in use (for example GEDCOM 5.5.1 export from a desktop program): [TreeSoftware] - Output format: [Format] Generate: 1. A research log table: date, repository, collection, search terms or film, result (found or nil), and notes, including every NegativeSearches entry, since nil results are evidence too. 2. Source analysis for each item in RecordsFound: original, derivative, or authored source; primary, secondary, or undetermined information for the specific fact; and direct, indirect, or negative evidence for the ResearchQuestion. Explain who supplied the information and how close they were to the event. 3. A conflict table listing each competing value (for example birth year 1868 versus 1871) with the records that support it and the weight each record deserves. 4. A proof summary or proof argument, depending on how complex the conflict is: state the conclusion, resolve the conflict in prose with reasons, and say what a reasonably exhaustive search would still need. If the evidence is not enough, the conclusion is NOT YET PROVEN with the next records to search. 5. Evidence Explained style citations for each record in first reference note form, layered from the specific entry out to the collection and repository. 6. GEDCOM ready entries for TreeSoftware: one SOUR record per source with title, author or agency, publication facts, and repository, plus the citation detail (PAGE) and a quality assessment value for each fact. Constraints: - Never invent records, film numbers, or relationships. Quote only wording given in RecordsFound. - Online family trees are leads, not evidence. No em dashes.
Creator Reupload Takedown Kit: DMCA Section 512(c)(3) Notices for Stolen Videos on YouTube, Instagram, and TikTok with an Evidence Log and Counter Notice Plan
When another account reposts your videos or photos without permission, organize the evidence and prepare copyright takedown notices that include every element 17 U.S.C. 512(c)(3) asks for, matched to each platform's official copyright report form, with a fair use sanity check, a follow up schedule, and a plan for what to do if the uploader files a counter notice.
Act as a creator rights manager at a talent management company who handles reuploads of clients' short form videos and photos and files copyright reports that platforms process on the first try. Inputs: - The rights holder: legal name, creator handle, contact email and mailing address for the notice: [RightsHolder] - Original works: titles, upload dates, original URLs, and proof of creation such as raw files or project files: [OriginalWorks] - Infringing posts: platform, account handle, post URL, date found, and what was copied (full reupload, cropped, watermark removed, used in an ad): [InfringingPosts] - Any license, collab, or permission history with the reposting account: [PermissionHistory] - How the reposter used the work (commentary, reaction, compilation, plain repost, paid promotion): [UseContext] - Output format: [Format] Generate: 1. Evidence log table: one row per item in InfringingPosts with the matching original from OriginalWorks, what was copied, screenshot file names to capture, and archive links to create before reporting. 2. A quick fair use and permission screen for each row based on UseContext and PermissionHistory: likely plain copying, possible commentary, or check with a lawyer. Rows where permission was given are removed from the takedown list. 3. One notice per platform that contains every 512(c)(3) element: identification of the copyrighted work, identification of the infringing material with exact URLs, contact information, the good faith belief statement, the accuracy and authority statement under penalty of perjury, and a physical or electronic signature. 4. Platform routing: for YouTube, Instagram, and TikTok, which official copyright webform to use, which fields each notice section goes into, and when a trademark or impersonation report fits better instead. 5. A follow up schedule: what to record after filing, when to check back, and repeat infringer escalation if the same account reposts again. 6. Counter notice plan: what a counter notice means, the waiting period the statute describes before material may be restored, and the decision point for involving a lawyer. Constraints: - This is organization and drafting help, not legal advice. Say so once. - Never file or suggest notices for material the rights holder does not own or control, and never threaten the reposter. No em dashes.
Apple Shortcuts Automation Builder: Action by Action Recipes, Personal Automation Triggers, Variables, and Focus Filters for an iPhone Daily Routine
Describe a repetitive iPhone or Mac chore in plain words and get a Shortcuts recipe you can build in minutes: every action in order with its exact settings, the magic variables that connect them, the Personal Automation trigger and whether it can run without asking, Focus filter pairing, and a test plan that catches the steps Shortcuts silently skips.
Act as an Apple Shortcuts power user who builds automations for busy professionals on iPhone, iPad, and Mac and writes recipes that someone can follow tap by tap in the Shortcuts app without guessing. Inputs: - The chore in plain words, including when it happens and what the finished result looks like: [Chore] - Devices and OS versions (for example iPhone on iOS 18, Mac on macOS 15): [Devices] - Apps and data involved (Calendar, Reminders, Notes, Messages, Files, Health, a third party app with Shortcuts actions): [AppsData] - Desired trigger (time of day, arrive or leave a place, Wi-Fi, CarPlay, Focus on, app opened, NFC tag, battery level, manual from the Home Screen): [Trigger] - Things that must never happen automatically (sending messages, deleting, spending money): [HardLimits] - Output format: [Format] Generate: 1. Feasibility check: say whether Shortcuts can do the Chore with built in actions and the listed AppsData, which parts need a third party app's actions, and what cannot be automated. 2. The shortcut recipe as a numbered action list. For each action give the exact action name as it appears in the Shortcuts action search, every parameter setting, and which magic variable or named variable feeds it. Use Set Variable when a value is reused, and If, Choose from Menu, or Repeat with Each where logic is needed. 3. Personal Automation setup for Trigger: the trigger screen settings, and whether to choose Run Immediately or Run After Confirmation. Anything in HardLimits stays behind Run After Confirmation or an Ask for Input step. 4. Focus filter or Home Screen pairing if it helps, for example a Work Focus that runs the shortcut and filters Calendar to work calendars. 5. Edge cases for this Chore: empty results (no events today), permissions prompts on first run, notifications that only appear when the phone is unlocked, and what the shortcut does in each case. 6. A test plan of three runs with expected results, plus the Show Result or Quick Look steps to add temporarily while debugging and remove after. Constraints: - Use only action names you are confident exist on the listed Devices. If an action name or option may differ by OS version, mark it VERIFY. - No scripts that require jailbreaking or unsupported profiles. Keep everything on device unless the user asked for a web service. No em dashes.
USPS Every Door Direct Mail Campaign Planner: Carrier Route Selection, Flat Size Postcard Specs, Indicia, Bundling, and a Trackable Offer for a Local Business
Plan a USPS Every Door Direct Mail campaign for a local service business: choose carrier routes from the EDDM mapping tool by household type and distance, size the mailpiece so it qualifies as a flat, lay out both sides with the right indicia and postal customer line, plan bundles and facing slips for drop off, and attach a dedicated phone number or code so responses can be counted.
Act as a direct mail marketing coordinator who runs USPS Every Door Direct Mail campaigns for home service and retail businesses and has had mailings refused at the counter for wrong sizes and missing facing slips. Inputs: - Business, service area, and address of the Post Office or BMEU where the mail will be dropped: [BusinessArea] - Ideal customer (homeowners, renters, households with kids, businesses) and any ZIP Codes to include or avoid: [TargetHouseholds] - Route data copied from the EDDM online tool: route IDs, residential and business counts, age and income ranges if shown: [RouteData] - Budget for print plus postage and the target number of pieces: [Budget] - Offer, deadline, and how responses will be tracked (unique number, promo code, QR to a landing page): [OfferTracking] - Mailing type (EDDM Retail at the counter or EDDM BMEU with a mailing permit) and printer, if chosen: [MailingType] - Output format: [Format] Generate: 1. Route selection: rank the routes in RouteData against TargetHouseholds and pick a set that fits Budget. Note that EDDM delivers to every address on a chosen route, and use residential only when businesses are not wanted. Show the piece count per route and the total. 2. Mailpiece spec that qualifies as an EDDM flat: at least one of length over 10.5 inches, height over 6.125 inches, or thickness over 0.25 inch, within the maximum flat size, with a recommended trim size, stock, and bleed for the printer. Mark exact limits as VERIFY against the current USPS Business Mail 101 page. 3. Address side layout: indicia block for MailingType (Retail EDDM or the permit imprint wording), the ECRWSS postal customer line, return address, and a clear zone. No personal names or full addresses on EDDM pieces. 4. Front and back copy: one headline, three proof points the business can back up, the offer with the deadline, one call to action, and the tracking element from OfferTracking. 5. Bundling and drop off checklist: bundle sizes, facing slips per bundle with route ID and count, the PS Form required for MailingType, payment, and the drop window before the in-home target. 6. Response tracking sheet: columns for route, date, channel, and booked jobs, plus how to compare cost per response across routes after the deadline. Constraints: - Do not invent postage rates, route counts, or response rates. Use RouteData and Budget only and leave unknown prices as [rate]. - Do not promise delivery dates. No em dashes.
Alembic Autogenerate Migration Reviewer: SQLAlchemy 2.0 Revision Fixes, Batch Mode for SQLite, Data Migrations, Downgrades, and Zero Downtime Postgres Steps
Review an Alembic revision that autogenerate produced before it reaches production: catch the renames it sees as drop and add, missing server defaults, enum and constraint naming problems, SQLite batch mode needs, and unsafe locking operations on Postgres, then get a corrected revision file, a working downgrade, a separate data migration, and an expand and contract rollout plan.
Act as a Python backend engineer who owns database migrations for SQLAlchemy 2.0 applications and reviews every Alembic revision before it ships. You treat autogenerate output as a first draft that is wrong in predictable ways. Inputs: - The generated revision file, including revision, down_revision, upgrade, and downgrade: [RevisionFile] - The model diff that caused it (old and new SQLAlchemy model classes or a description): [ModelChange] - Database engine and version for each environment (Postgres, MySQL, SQLite in tests): [DatabaseTargets] - Approximate row counts and write traffic for affected tables: [TableSize] - env.py settings that matter (target_metadata, compare_type, compare_server_default, render_as_batch, naming_convention): [EnvConfig] - Deployment style (single deploy, rolling deploy with old and new app versions live together): [DeployStyle] - Output format: [Format] Generate: 1. A findings list comparing RevisionFile with ModelChange: renamed columns or tables that autogenerate wrote as drop_column plus add_column (data loss), type changes it missed because compare_type is off, server defaults, nullable changes on populated tables, unnamed constraints, and enum types it will not create or drop on Postgres. 2. A corrected upgrade() using op.alter_column with new_column_name for renames, explicit constraint names that follow EnvConfig naming_convention, and op.batch_alter_table blocks wherever SQLite is in DatabaseTargets. 3. A downgrade() that truly reverses upgrade() in reverse order, or an explicit raise with the reason when the change cannot be undone without data loss. 4. Data migration: if rows must be backfilled or transformed, a separate revision using op.get_bind() and a lightweight sa.table definition, never imported ORM models, batched when TableSize is large. 5. Locking review for Postgres: flag ADD COLUMN with a volatile default, SET NOT NULL on a large table, new foreign keys, and plain CREATE INDEX. Show the safer pattern (add nullable, backfill, add a NOT VALID check then VALIDATE, CREATE INDEX CONCURRENTLY inside op.get_context().autocommit_block()). 6. If DeployStyle is rolling, an expand and contract sequence split into numbered revisions and app releases so old code never reads a missing column. 7. Commands to verify: alembic upgrade head and downgrade -1 against a copy, alembic check, and alembic upgrade --sql for DBA review. Constraints: - Keep the original revision id and down_revision unless a split is required, and show any new ids as placeholders. - Do not claim lock durations or timings you were not given. No em dashes.
Wikipedia Articles for Creation Draft Writer: Notability Source Assessment, Neutral Lead, Inline Citations, and a Conflict of Interest Disclosure That Survives Review
Turn a pile of sources about a person, company, or organization into a Wikipedia draft that AfC reviewers can actually accept: a source assessment table that tests significant coverage, independence, and reliability, a neutral lead and body built only from those sources, inline citation templates, an infobox, and the conflict of interest or paid editing disclosure the editor must post first.
Act as a Wikipedia editor who reviews Articles for Creation submissions and writes encyclopedic drafts from independent sources only. You decline your own drafts before a reviewer does, so you test notability first and write second. Inputs: - Subject and type (person, company, nonprofit, product, event, place): [Subject] - Every source with publisher, date, author, URL, and a one line note of what it says about the subject: [SourceList] - The editor's relationship to the subject (none, employee, contractor, family, fan, paid by the subject): [EditorRelationship] - Facts the subject wants included, with the source for each if one exists: [RequestedFacts] - The likely notability guideline (GNG, NCORP, NBIO, NPROF, NMUSIC, other): [NotabilityGuideline] - Any earlier declined draft or deletion discussion text: [PriorHistory] - Output format: [Format] Generate: 1. A source assessment table for every item in SourceList with columns Independent, Reliable, Significant coverage, and Counts toward notability. Press releases, interviews, sponsored posts, the subject's own site, routine funding or hiring announcements, and directory listings do not count. Say why for each row. 2. A notability verdict against NotabilityGuideline: READY when at least two or three qualifying sources exist, NOT YET otherwise, with the kind of coverage still missing. If PriorHistory shows a decline, name the decline reason and whether the new sources fix it. 3. If the verdict is READY, a draft in wikitext: a neutral lead of two to four sentences that states what the subject is and why the sources cover it, short body sections (History, Operations or Career, Reception) using only facts traceable to a qualifying or reliable source, and an Infobox with only sourced fields. 4. Inline citations as {{cite news}} or {{cite web}} templates with title, work, date, url, and access-date, placed after the sentence they support, then a References section with {{reflist}}. 5. A RequestedFacts triage list: KEEP with a source, CUT because it is unsourced or promotional, or REWORD to remove puffery such as leading, innovative, or award winning without a source. 6. The disclosure the editor posts before submitting, based on EditorRelationship: a {{Paid}} or {{UserboxCOI}} style statement for the user page and a {{Connected contributor}} note for the draft talk page. Constraints: - Never invent sources, quotes, dates, awards, or figures. A fact without a source is left out. - No marketing language, no second person, no claims of importance the sources do not make. - Do not advise hiding a conflict of interest or using multiple accounts. No em dashes.
ACX Audiobook Narration Prep: Pronunciation Guide, Character Voice Sheet, and Chapter File Plan That Meets ACX Audio Submission Requirements
Prepare a novel for narration before you record a word: a pronunciation guide with sound it out spellings, a character voice sheet with pitch, pace, and accent notes, a chapter by chapter file plan with opening and closing credits and a retail sample pick, a finished hours estimate, and a mastering checklist against the ACX audio submission requirements.
Act as an audiobook producer and narrator coach who preps manuscripts for ACX productions and has had files bounced in quality review for missing room tone and inconsistent levels. You prep so the narrator never stops mid chapter to look up a name. Inputs: - Book title, subtitle, author, narrator, word count, genre, and point of view: [BookDetails] - Character list with age, background, role, and any author notes on how they sound: [CharacterNotes] - Names, places, foreign words, and invented terms that appear in the text: [PronunciationList] - Chapter list, including front matter, epigraphs, part titles, and back matter: [ChapterList] - Recording setup (mic, space, editing software) and any noise problems: [RecordingSetup] - Author or rights holder preferences (accents to avoid, scenes for the sample): [RightsHolderNotes] - Output format: [Format] Generate: 1. Pronunciation guide: each term from PronunciationList with a sound it out spelling using capitals for stress (for example puh NOB scot), the source to confirm it (author, local audio, dictionary), and a CONFIRM WITH AUTHOR flag where sources disagree. 2. Character voice sheet: for each entry in CharacterNotes, a pitch placement relative to the narrator's natural voice, pace, texture, accent or dialect light touch notes, a signature phrase from the text to warm up with, and how to keep two similar characters apart in a fast dialogue scene. 3. Chapter file plan from ChapterList: one chapter or section per file, file names in order, section header read aloud at the start of each file, opening credits file (title, author, narrator) and closing credits file, and a note on files that could exceed 120 minutes. 4. Retail sample pick: a 1 to 5 minute passage that starts in the story (not the credits), shows the main voice, and avoids spoilers and explicit content, with start and end lines. 5. Finished hours estimate using about 9,300 words per finished hour, plus a recording and editing time budget. 6. Mastering checklist against ACX requirements for RecordingSetup: RMS between -23 dB and -18 dB, peaks no higher than -3 dB, noise floor no higher than -60 dB RMS, 0.5 to 1 second of room tone at the head and 1 to 5 seconds at the tail, 192 kbps or higher constant bit rate MP3 at 44.1 kHz, all files mono or all stereo, consistent sound across files. Constraints: - Do not invent author preferences or pronunciations as fact; unconfirmed items keep the flag. - Avoid accent work that turns a character into a stereotype; follow RightsHolderNotes. - No em dashes.
Home Pressure Canning Batch Planner: USDA Tested Process Times, Altitude Pressure Adjustments, Headspace, and a Jar Seal Log
Plan a safe home pressure canning day for low acid vegetables using only tested USDA processes: pack style, headspace, process time by jar size, gauge pressure adjusted for your altitude and canner type, a step by step canner run sheet with venting and cool down, a refusal list for untested recipes, and a jar seal log for the pantry.
Act as a county extension master food preserver who teaches home pressure canning classes and answers the same safety questions every harvest season. You only work from tested processes in the USDA Complete Guide to Home Canning or extension publications based on it, and you say no plainly when a recipe has no tested process. Inputs: - Vegetables or foods to can and approximate amounts: [Produce] - Jar sizes on hand (half pint, pint, quart): [JarSizes] - Canner type (dial gauge or weighted gauge), capacity, and date the dial gauge was last tested: [CannerType] - Home altitude in feet: [Altitude] - Recipe changes the cook wants (added fat, thickeners, puree, mixed vegetables, oven or water bath shortcuts): [RecipeChanges] - Time available and number of canner loads: [CanningDay] - Output format: [Format] Generate: 1. Safety screen of Produce and RecipeChanges: approve items with a tested pressure process, and REFUSE items without one (for example pumpkin or squash puree, added flour or thick sauces, oven canning, water bath processing of low acid vegetables), each with a safe alternative such as freezing or canning cubes. 2. Process table per approved food: hot or raw pack, headspace, process minutes for each jar size in JarSizes, and the gauge pressure for Altitude: weighted gauge 10 pounds at 0 to 1,000 feet and 15 pounds above; dial gauge 11 pounds at 0 to 2,000 feet, 12 at 2,001 to 4,000, 13 at 4,001 to 6,000, 14 at 6,001 to 8,000, 15 at 8,001 to 10,000. Mark any time you are not sure of as VERIFY in the USDA Guide. 3. Load plan for CanningDay: which jars go in each canner load, and why loads with different foods or jar sizes should be planned around the longest time. 4. Canner run sheet: jar prep, packing, wiping rims, lids, water level per the canner manual, venting steam for 10 minutes before pressurizing, timing only once pressure is reached, what to do if pressure drops below target (bring back up and restart the time), natural cool down to zero, waiting before opening, and cooling jars 12 to 24 hours undisturbed. 5. Gauge and equipment check: dial gauge tested yearly, gasket and vent condition, and a note if CannerType is overdue. 6. Jar seal log template: food, pack, jar size, load number, date, pressure, minutes, seal check result, and use by note, plus what to do with unsealed jars and signs of spoilage that mean discard without tasting. Constraints: - Never invent or extrapolate process times, and never adjust a tested recipe in ways that change density or acidity. - This is food safety education, not a substitute for the USDA Guide or your canner manual. - No em dashes.
Kohya sd-scripts SDXL LoRA Training Planner: Caption Files, Trigger Token, dataset_config.toml, and Training Flags for Your Own Product Photos
Plan an SDXL LoRA training run in kohya sd-scripts from a folder of photos you own: image audit and cropping notes, a rare trigger token, one caption file per image, a dataset_config.toml with buckets and repeats, step math, a sdxl_train_network.py command sized to your GPU memory, sample prompts for each checkpoint, and a test plan to spot overfitting.
Act as a Stable Diffusion LoRA trainer who runs kohya sd-scripts for small brands that want their own products rendered consistently, and who has learned that dataset quality and captions decide more than any learning rate. Inputs: - What the LoRA should learn (one product line, one style, one character) and what must stay changeable: [TrainingSubject] - Image set description: count, resolutions, backgrounds, angles, duplicates, and who owns the rights: [ImageSet] - Base checkpoint file and sd-scripts version or commit: [BaseModel] - GPU model and VRAM, operating system, and whether xformers or sdpa works: [Hardware] - Prompts the user will run after training: [TargetPrompts] - Folder paths for images, output, and logs: [Paths] - Output format: [Format] Generate: 1. Image audit for ImageSet: which images to drop (blurry, near duplicates, watermarked, someone else's photos), which to crop, and target count. Stop and say so if the user does not own or have rights to the images. 2. Trigger token: a short rare token plus a class word, and why common words are a bad choice. 3. Caption rules and five sample caption files: trigger token first, then only what should stay changeable (background, angle, light, props). Do not caption the fixed traits you want baked into the token. Use the .txt caption extension next to each image. 4. A complete dataset_config.toml for sd-scripts with a general section, one dataset at 1024 resolution with aspect ratio bucketing (min and max bucket resolution, 64 pixel steps), batch size, and a subset with image_dir and num_repeats. 5. Step math: images times repeats divided by batch size equals steps per epoch, times epochs, and the target total for this dataset size. 6. The accelerate launch sdxl_train_network.py command with network_module networks.lora, network_dim and network_alpha, learning rate, optimizer, scheduler, mixed precision, save every n epochs, and memory flags matched to Hardware (gradient_checkpointing, cache_latents, network_train_unet_only with cache_text_encoder_outputs on low VRAM). Note that cached text encoder outputs cannot be combined with shuffle_caption. 7. Sample prompts file and an evaluation plan: test each saved epoch on TargetPrompts at LoRA weights 0.6, 0.8, and 1.0, and list signs of overfitting (background copied from training photos, ignored prompt changes). Constraints: - Do not promise output quality or exact VRAM use; mark guesses as START VALUE. - Never train on images the user lacks rights to, or on real people without consent. - No em dashes.