Create a Xiaohongshu food post or REDnote food post from a dish photo, restaurant visit theme, or dining-atmosphere reference. This REDnote food image maker plans restaurant review images and AI food photography as a vertical 3:4 food-note sequence: a cover, dish close-up, table or restaurant atmosphere image, and a final detail image for a food recommendation post. Shape title ideas, caption angles, and tags for a restaurant review post, cafe-hopping post, new-menu launch post, restaurant visit images, food diary images, and restaurant social media images.
设计与多媒体
zhongcao-food-note-maker
试用Create a Xiaohongshu food post or REDnote food post from a dish photo, restaurant visit theme, or dining-atmosphere reference. This REDnote food image maker plans restaurant review images and AI food photography as a vertical 3:4 food-note sequence: a cover, dish close-up, table or restaurant atmosphere image, and a final detail image for a food recommendation post. Shape title ideas, caption angles, and tags for a restaurant review post, cafe-hopping post, new-menu launch post, restaurant visit images, food diary images, and restaurant social media images. Optionally it reads Xiaohongshu itself — the notes already running for the topic, one page of a note's top comments, and an account's own recent notes — so Xiaohongshu research, competitor note analysis and comment analysis rest on the platform instead of on guesswork.
它能做什么
Create a Xiaohongshu food post or REDnote food post from a dish photo, restaurant visit theme, or dining-atmosphere reference. This REDnote food image maker plans restaurant review images and AI food photography as a vertical 3:4 food-note sequence: a cover, dish close-up, table or restaurant atmosphere image, and a final detail image for a food recommendation post. Shape title ideas, caption angles, and tags for a restaurant review post, cafe-hopping post, new-menu launch post, restaurant visit images, food diary images, and restaurant social media images. Optionally it reads Xiaohongshu itself — the notes already running for the topic, one page of a note's top comments, and an account's own recent notes — so Xiaohongshu research, competitor note analysis and comment analysis rest on the platform instead of on guesswork.
技能文档
Zhongcao Food Note Maker
Create an ordered REDnote (Xiaohongshu) food note that turns one dish or restaurant visit into a visual story: a cover, dish close-up, texture or table detail, and dining-atmosphere image. Pair it with title ideas, caption beats, and tags in the user's voice and only from facts the user has supplied.
Scope and routing
Use this Skill for a coordinated food-led image sequence, restaurant visit note, café or dessert post, dish highlight, or food recommendation story where multiple images need to feel like one visit. It starts from a dish, table, restaurant, or packaging photo, or a concrete dish or visit concept.
For a generic topic carousel, use zhongcao-carousel-maker; for one isolated
cover, use zhongcao-cover-maker. Keep the user's stated dish, restaurant,
ingredients, plating, tableware, packaging, occasion, and visual references
central. Restaurant names, menus, prices, locations, offers, and taste claims
are written only when the user has provided them.
Inputs and default story
Reuse the conversation's dish, visit setting, audience, style vocabulary, and references. A source food photo or a concrete dish or restaurant-visit concept is the minimum hard input. Ask only for a missing choice that materially changes the result: the food anchor, dining or visit scene, or visual direction.
With a food photo, use it as the first ordered reference for food-led images. With a concept, create an original food note from the user's described dish, ingredients, plating, setting, and mood.
Default to a four-slide vertical 3:4 food note at 2K, delivered as one
coordinated sequence only when the live model card accepts count: 4 and
output_relationship: "sequence":
- Cover — an appetizing food introduction with clean title space.
- Signature dish — a close view that makes the main dish and plating clear.
- Detail — texture, a lifted or cut moment, or tabletop detail that advances the meal story.
- Dining atmosphere — table, restaurant, or visit-ending scene with a final detail that supports the food recommendation post.
Keep model: "auto" and model-managed controls unless the user asks for a
model, compatibility, or price decision. Before fixing model, canvas, control,
count, output relationship, or price, read beatra.models.list for the chosen
image_to_image, text_to_image, or image_edit capability. If the card does
not accept the coordinated four-image sequence, present its supported routes,
maximum charge, and resulting calls before the user chooses different paid work.
Golden path
- Build a food-note card: food anchor, user-confirmed must-keeps, visit setting, audience, story angle, palette, light, tableware or packaging, title-safe placement, and each slide role.
- Route a source food photo to
beatra.images.transform, a dish or visit concept tobeatra.images.generate, and an accepted-slide revision tobeatra.images.edit. - Draft the four prompts as one visual family and prepare the free post angle, title ideas, caption beats, and tags.
- Read the live card and show one confirmation with all four roles, full prompt, ordered references, must-keeps, canvas, model behaviour, controls, count, relationship, current maximum charge, and call count.
- After approval, create one stable opaque
client_request_id, submit exactly once through the bundled client, and save its returned task ID. - Poll the original task, review accessible results against the card, and deliver them in post order with the caption plan and actual returned facts.
Read food-note planning for the story card and food-note workflow for exact route, confirmation, polling, and recovery details.
Paid-work confirmation
Planning, post writing, and prompt drafting are free. The optional Xiaohongshu lookup is the one thing that can charge before generation, and it is priced and approved on its own. Before generation or a revision, obtain one clear confirmation of the frozen food-note card, all paid image requests, source and reference order, canvas, model, controls, count, current price, maximum charge, and total call count.
Every changed food anchor, source or reference order, prompt, slide role,
canvas, model, count, output relationship, or control is new paid work with a
new confirmation and a new client_request_id. A focused revision to an
accepted slide is new paid work too.
Execute and deliver
Use only this package's bundled scripts/mcp_client.py for remote operations.
Send one JSON object on standard input after call . Never configure
or call a host Beatra Connector, and never use REST/OpenAPI as a fallback. Read
Bundled MCP Client diagnostics for commands and
connection troubleshooting.
Upload a local source and put its returned artifact at images[0] for a
transform; later images (up to three) guide food styling, palette, composition,
or dining setting in stated order. For a concept-only route use generate. For a
focused revision use the accepted slide at images[0] with edit.
Register through beatra.installations.register on first use. A returned
task_id belongs to the original approved work: poll only with
beatra.tasks.get. Replay only a genuinely unknown create response with the
byte-equivalent frozen payload and same ID. If the task ID is missing, use
beatra.tasks.list, then verify the candidate with beatra.tasks.get before a
replay. Call beatra.tasks.cancel only at the user's request; on 409, keep
polling the original and report cancellation only at terminal status: "canceled".
Review accessible images against user-confirmed dish, plating, tableware, and
packaging must-keeps, each slide role, 3:4 composition, visual continuity, and
cover title-safe placement. Deliver only completed-task facts: artifact links,
dimensions, format, resolved model, task IDs, and
billing.net_charged_credits. Present the ordered food note, slide roles,
title ideas, caption beats, tag set, and at most one focused unexecuted revision
suggestion.
Reading Xiaohongshu before you write
Optional, and paid. When the connection exposes Beatra's public social lookup, this Skill can read Xiaohongshu directly instead of working from what the user remembers: one page of notes matching a keyword, one specific note the user pastes, one page of that note's top comments, and an account's profile or recent notes. Six operations, Xiaohongshu only.
Every one of them costs 60 credits, and there is no cheap operation on this platform to fall back on. The same reads cost 6 on TikTok. They cost 6 on Douyin too — except Douyin's own keyword search, which is also 60, so do not say "ten times Douyin" without naming the read. A three-step read — the field, one note, that note's top comments — is 180 credits, and every further page is another 60. Say the number before offering anything, confirm each lookup on its own before it runs, and say plainly that this Skill's own deliverable arrives either way at no cost. Offer one read, not a plan of four.
The rule is the whitelist, not a list of exceptions: a platform with no operation on it cannot be looked up from here, and another platform's notes are never presented as Xiaohongshu's. A returned image URL is not a viewed image — state a visual finding only about an image the host can actually open. Every figure that reaches the work is labelled as looked up with the time it was read, or as supplied by the user, or as missing. Nothing is estimated, and nothing is carried in from what notes in this category usually do.
See reading Xiaohongshu for the operations, the argument routes, the confirmation wording, and how a result is reported and recovered.
References by task
- Food-note planning: food card, default slide roles, source-reference roles, and post angle.
- Food-note workflow: transform, concept, edit, confirmation, task tracking, recovery, and review.
- Installation and authentication and installation registration: first use and credentials.
- Tasks and results and billing, errors, and recovery: returned task fields, balance, validation, and structured errors.
- Bundled MCP Client diagnostics and uninstall and disconnect: client operation and removal.
Runtime and safe automatic updates
The bundled client silently checks at most once every 24 hours per installation. When a newer release is available, it installs automatically without separate confirmation. It uses only fixed official Beatra discovery and immutable CDN paths. Before replacement, it verifies discovery data, manifest, archive, and every packaged file against expected identity, size, and SHA-256 values, then replaces only package-owned files in the installed Skill directory.
Checks, downloads, verification, replacement, rollback, and recovery fail open: the current installation stays usable and the original command continues. The setting persists for this installation. Read automatic updates and safety for the official sources, integrity checks, replacement boundary, failure behaviour, and controls.
python3 scripts/mcp_client.py update --auto off
python3 scripts/mcp_client.py update --auto on
python3 scripts/mcp_client.py update --check
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