lingzao

Lingzao

Lingzao helps agents research public creator content from Xiaohongshu, Douyin, and WeChat official-account articles.

Use this skill when the user asks to:

• •Search Xiaohongshu notes by keyword.
• •Get Xiaohongshu search suggestions or popular recommendations.
• •Search public creators by keyword.
• •Look up a creator profile.
• •Read a creator's recent public posts.
• •Get recent post copy, subtitles, covers, metrics, and commercial signals from a creator profile.
• •Get details for a Xiaohongshu or Douyin post.
• •Get top-level public comments for one Xiaohongshu or Douyin post.
• •Get one WeChat official-account article's public detail and text.
• •Get public metrics for one WeChat official-account article.
• •Get related public WeChat official-account articles.
• •Extract spoken copy or transcript from a public short-video link.
• •Generate creator image assets from a prompt when the user explicitly asks to make an image.

Agent Playbooks

For higher-level creator strategy tasks, use the playbooks in <skill_root>/playbooks/ before answering. They turn Lingzao's public-content tools into creator workflows instead of isolated lookups.

Use these playbooks when relevant:

• •lingzao-progressive-interaction-map.md: route vague user inputs, homepage

links, note links, drafts, and reference-image requests with light questions.

• •search-credit-notice.md: explain basic vs deep search scope before paid

lookups and avoid silently expanding credit usage.

• •atian-creator-judgment-framework.md: apply A Tian's account-stage,

memory-anchor, content-mainline, and bottleneck judgment.

• •creator-case-general-analysis-framework.md: analyze any creator case across

tracks by identifying the account archetype, memory anchor, new narrative, proof system, audience desire, content engine, format engine, comment demand, commercial entry, hidden resources, learnable parts, non-copyable parts, and user-fit tests.

• •beginner-account-start-and-topic-radar.md: handle zero-to-one creator

questions, topic discovery, keyword trees, and low-follower viral references.

• •keyword-insight-report-template.md: create scoped keyword insight reports

from a main keyword plus confirmed related/dropdown terms, with clear credit estimates before expanding.

• •keyword-to-publishable-content-package.md: turn a keyword or vague topic

into publishable Xiaohongshu content packages with selected references, topic angles, titles, cover copy, graphic-note structure, spoken scripts, Vlog storyboards, body direction, and publishing keywords.

• •mother-content-cross-platform-distribution.md: turn one topic, draft,

note breakdown, product update, screenshot, transcript, or oral idea into a one-stop cross-platform distribution package. When users say "一条龙", "全平台同步", "分发包", or "一个模板发多个平台", start with the basic Xiaohongshu + Moments + WeChat public-account package, then offer optional expansion to podcast, X, Knowledge Planet, Bilibili, video account/Douyin, Xiaohongshu image package, or knowledge-base/SOP.

• •pre-publish-readiness-check.md: before posting, ask whether the content is

already finished and then check content clarity, image/page readiness, cover recognition, title clickability, first 3 lines or first 3 seconds, and natural keyword embedding.

• •audience-persona-fit-check.md: before titles, keywords, account operation,

or content-package decisions, infer or ask who the content is for, who will click, who will not click, and which audience/city/life-stage keywords should shape the output.

• •xhs-title-design-check.md: design or diagnose Xiaohongshu titles after the

user sends a topic, draft, cover copy, reference note, or content package; default to 3 strongest titles with keyword anchor and click reason instead of a 10-title pool.

• •xhs-profile-bio-design.md: write or diagnose Xiaohongshu 100-character

profile bios and homepage introductions that clarify who the account is for, what it shares, why to follow, and how it connects to nickname, pinned notes, account stage, audience keywords, city keywords, and light commercial paths.

• •benchmark-account-discovery-quality-gate.md: find or judge benchmark

accounts with a default quality gate: still updating, recent high-performing works, track/audience fit, stage fit, and clear learnable parts; stale accounts should be marked as historical references, not main benchmarks. User-facing results should show direct creator profile links and the specific recent high-interaction works, not raw creator IDs. The first discovery round should return up to 5 strong accounts, not 10-20 accounts; expand only after the user confirms follower range, stage, city, audience, format, or asks for more. Include follower count, total liked count, latest update, recent 30-day hit works with note metrics, content format, and why each account is worth learning; sort visible recommendations by follower count from high to low when available.

• •self-account-peer-horizontal-diagnosis.md: compare the user's own account

with same-track, same-stage, or same-follower-range peer accounts when the user explicitly asks for peer comparison, such as "横向对比", "同级账号", "对标账号", "找 5-15w 粉账号和我比", or "和同赛道账号比我差在哪里". Generic own-account concerns such as "看看我现在的问题" or "我是不是说话太快" should stay on self-account-diagnosis-report-template.md unless the user also asks to compare against peers. It combines own-account diagnosis, active benchmark selection, peer-account tables, title/cover/opening/speech/content-system comparison, top gaps, 30-day adjustment plans, and a human next-step loop.

• •single-note-breakdown-workflow.md: break down one Xiaohongshu/Douyin note

link by title, cover, outline/script, shooting/editing layer when visible, comment demand, viral mechanism, learnable parts, non-copyable parts, and adaptation into the user's own graphic note, spoken script, Vlog storyboard, or knowledge-base card. User phrases such as "完整分析这条笔记", "深度拆解", "拆细一点", "拍摄手法", "分镜", or "剪辑节奏" should trigger the deeper breakdown instead of a short summary.

• •publishing-keyword-design-check.md: design the final 10 Xiaohongshu

publishing keywords for a finished draft and check whether title, cover copy, opening lines, and keyword field carry the keywords naturally.

• •track-difficulty-judgment-library.md: judge common tracks such as female

growth, career, good products, local life, health, fashion, and AI tools.

• •monetization-path-judgment-library.md: answer whether a track or account

can monetize through ads, courses, community, consulting, lead generation, products, stores, or enterprise conversion.

• •self-account-diagnosis-report-template.md: structure own-account diagnosis

reports, follow-up actions, and a human closing with "人情味" that turns sharp diagnosis into one small next experiment instead of ending at a cold action list. Own-account diagnosis should also include a share-worthy conclusion card, action advice, and psychological reassurance.

• •comparable-account-breakdown-report-template.md: decide whether another

account is worth learning from, what can be learned, and what cannot be copied.

• •draft-rewrite-and-benchmark-workflow.md: rewrite drafts, adapt viral

formulas, and review multiple content ideas without only polishing sentences.

• •reference-image-graphic-note-workflow.md: turn reference images into

Xiaohongshu 4-page or 7-page graphic-note packages.

• •visual-generation-and-cover-workflow.md: route Xiaohongshu covers, graphic

notes, WeChat image packs, no-person knowledge cards, and product/ecommerce visuals into image generation or ready-to-use prompt packages.

• •image-generation-execution-workflow.md: when image generation is available,

turn the visual route into actual images, run a visual-director quality gate, and repair ugly/crowded/generic generations instead of leaving ordinary users with raw prompts.

• •image-generation-agent-integration-guide.md: model-agnostic rules for

domestic Agent wrappers, including stable generation input/output fields, good-vs-bad image standards, reference-image usage, known generation bugs, friendly failure handling, and A Tian's example-collection homework.

• •visual-reference-style-library.md: classify A Tian's internal visual

reference folders into travel/food covers, WeChat article images, AI-person infographics, Lingzao no-person knowledge cards, product conversion images, face-led keyword video covers, interaction prompt covers, and text-dense screenshot graphic notes, and room-as-identity lifestyle covers.

• •post-publish-data-review-workflow.md: review published Xiaohongshu notes

from note links, backend screenshots, scripts, covers, and 24h/48h/7d data.

• •content-knowledge-base-workflow.md: turn saved notes, public creator links,

keyword results, viral examples, and creator distillation requests into user-owned topic, title, cover, structure, account-reference, creator-research, and publishing-review libraries.

• •retention-and-follow-up-loop.md: end useful outputs with one concrete next

step such as published-note data review, reusable reference-search templates, draft feedback, or a post-diagnosis small experiment with a return loop. It also defines the SOP for not letting the user's words drop on the floor: acknowledge resistance, lower the next action, and ask one concrete next-step question. Dense outputs should offer Word, HTML/webpage preview, or knowledge-base-ready packaging instead of leaving users with a wall of chat text. When users say the diagnosis is accurate but they lack action, route to a post-diagnosis activation package instead of adding more pressure.

• •product-judgment-and-feedback-loop.md: judge where users are really stuck,

explain Lingzao in human language, build content/sales narratives, turn user feedback into product iteration, and decide which requests are worth building versus noise.

• •xhs-operation-task-tree.md: route Lingzao users by concrete Xiaohongshu

operation tasks instead of course lists, covering homepage diagnosis, benchmark discovery, viral-note adaptation, topic generation, content production, cover/image work, pre-publish checks, post-publish review, acquisition paths, and knowledge-base automation.

Keep public wording focused on creator-content research and workflow support. Do not promise viral growth, guaranteed monetization, full monitoring, raw data export, or copying another creator's content.

Install And Paid Capability Entry

Lingzao is installed as one free main Skill. Users do not need to install separate title, keyword, account-diagnosis, benchmark, cover, or review skills. After installation, this main Skill routes the user's request to the right playbook.

There are two user acquisition paths:

1. Community/course users:

- They may already have A Tian's course, install link, payment steps, and API Key setup instructions. - Keep the in-chat explanation short: install the Skill, open the Lingzao web dashboard, follow the tutorial, recharge credits, copy the API Key, then run setup.

1. Public-platform users from Xiaohongshu, Douyin, or other public content:

- Do not require them to open the web dashboard and pay before they understand what Lingzao can do. - Let them install the free main Skill first. - Then explain the hidden paid entry in friendly language: the local playbooks can help judge drafts, titles, covers, directions, and publishing plans; when they need Lingzao to search public content, inspect accounts, open note/article details, read comments, inspect article data, extract video copy, or generate creator image assets, they need to open the Lingzao web dashboard, follow the tutorial, recharge credits, and configure an API Key.

The web dashboard is not only a payment page. Present it as the user's learning and setup hub:

• •learn how to install and configure Lingzao
• •learn how to ask Agent better questions instead of waiting in a group chat
• •learn how to use Skill workflows for self-media operation
• •learn account diagnosis, benchmark breakdown, title/keyword, pre-publish, and

post-publish review workflows

• •recharge credits and get the API Key when they need public-content lookup or

image generation

Use this wording when a user has installed the Skill but has not configured an API Key yet:

你已经装好灵造 Skill 了。安装本身是免费的，它会先帮你判断你现在是在找方向、拆账号、写内容、做封面、配关键词，还是复盘数据。 如果你要继续查小红书/抖音/公众号公开内容、找对标账号、看账号主页、打开笔记或文章详情、看评论区、查看公众号文章数据、提取短视频文案或生成创作者图片素材，就需要到灵造网页版开通积分并配置 API Key。 你可以打开 https://lingzao.atian.vip 看安装教程和使用教程，里面也会教你怎么用 Agent 做自媒体运营、怎么问问题、怎么用这些 Skill。需要查公开内容或生成图片的时候，再在网页里充值/获取 API Key，配置好以后回来继续问，我会接着刚才的问题往下做。

Do not frame payment as a penalty. Frame it as:

• •free install = get the workflow brain and routing layer
• •web dashboard = tutorial, usage examples, self-media operation lessons, and

API Key setup

• •paid credits = unlock public-content lookup, image generation, and deeper

research actions

Knowledge sync handoff:

• •After a useful Lingzao research result or diagnosis report, do not sync it

automatically. Ask first: 要不要把这份结果同步到你的知识库？可以选择 ima / Obsidian / 飞书 / 暂不同步。

• •If the user chooses a target, prepare a clean Markdown version and ask the

current Agent environment to use the user's configured knowledge tool.

• •For ima, call the installed ima Skill or ima knowledge-base tool if the user

has configured one.

• •For Obsidian, use the user's Obsidian CLI, Obsidian Skill, or approved vault

workflow to write Markdown under a user-approved Lingzao/ path.

• •For 飞书, use the user's Lark/Feishu CLI or Skill with user authorization to

create or update a document.

• •Do not ask for or store ima, Obsidian, or Feishu credentials inside Lingzao.

Do not include internal implementation details, raw payloads, cache URLs, signed URLs, API keys, or internal error details in synchronized content.

Profile workflow:

• •If the user asks for a creator homepage or a basic homepage analysis, use get-user-posted-notes by default. It returns recent posts and enough author/post data for a basic read.
• •If the user sends a Xiaohongshu short link such as xhslink.com/m/..., or a

copied share sentence such as @... 查看Ta的主页>> https://xhslink.com/m/..., extract the short link, normalize bare links to https://..., and read the surrounding words before choosing a command. Do not classify the short link by path alone. If the context says account, homepage, creator, profile, benchmark, account diagnosis, homepage diagnosis, Ta的主页, or recent posts, treat it as a creator-homepage request and call get-user-posted-notes --url "https://<short link>".

• •If a Xiaohongshu short link has no context, ask whether the user wants creator

homepage recent posts or one-post detail before spending credits. If the context says this note, comments, copy, transcript, one-post breakdown, or is a normal note share sentence with a title snippet plus 前往【小红书】一探究竟吧, treat it as a one-post candidate, not a homepage. One-post words such as 这条 or 这篇 take priority over generic diagnosis wording. Do not default to get-note-detail; first confirm it is a single post and ask for the final note URL or note_id plus whether it is 图文 or 视频 when needed.

• •Only add get-user-info when the user specifically needs full profile-level stats such as bio, follower count, following count, total likes, total collections, or total note count.
• •Use analyze-user-profile for Xiaohongshu deeper homepage copy/script/subtitle analysis, recent post text, covers, commercial signals, or product-note signals. For Douyin spoken copy or transcript text, use extract-video-copy on specific video URLs.
• •Do not call get-user-info and get-user-posted-notes as a fixed pair unless the user asks for both profile-level stats and recent-post analysis.
• •Do not force a full account diagnosis when the homepage has too few public

posts. Route by visible sample size: - 0 posts: no account diagnosis; switch to beginner start/account setup guidance. - 1-2 posts: homepage first impression plus single-post feedback only. - 3-5 posts: starter-account mini diagnosis. - 6-9 posts: light account analysis. - 10+ posts: standard account analysis can be offered. - 20+ posts: standard deep diagnosis can use analyze-user-profile --limit 20 after credit confirmation. - 40+ posts: deep diagnosis, creator distillation, or knowledge-base distillation can use --limit 40 after credit confirmation.

Post drill-down workflow:

• •Xiaohongshu list-style commands (search-notes, get-user-posted-notes,

analyze-user-profile) return xhs_note_type on each note item when Lingzao can identify whether it is 图文 or 视频.

• •When continuing from one of those note items to get-note-detail, pass the

returned xhs_note_type directly as --xhs-note-type; do not infer the type from the URL.

• •If a Xiaohongshu note item has no xhs_note_type, ask the user whether it is

图文 or 视频 before calling get-note-detail. get-note-comments can still be called without this type.

Setup

Resolve this SKILL.md directory as <skill_root>, then run setup once:

bash "<skill_root>/scripts/setup.sh" --base-url "https://your-lingzao-domain.com"

Environment variables override saved config:

export LINGZAO_API_KEY="lgz_xxx"
export LINGZAO_BASE_URL="https://your-lingzao-domain.com"

Check the connection:

~/.lingzao/bin/lingzao doctor

Before using Lingzao commands, check whether the skill has an update:

~/.lingzao/bin/lingzao check-version

If an update is available, stop the current Lingzao operation and update the skill first. Do not continue using an outdated Lingzao Skill for search, profile, subtitle, or extraction work.

To update the skill, rerun the installer. For npx skills, try:

npx skills add https://assets-tian.midao.site/skills/lingzao --skill lingzao -g --copy

Updating keeps the saved API config in ~/.lingzao/config.json; no API key setup is needed again.

If ~/.lingzao/bin/lingzao is missing or points to the wrong directory, repair the command wrapper:

bash ~/.agents/skills/lingzao/scripts/setup.sh --skip-doctor

If ~/.agents/skills/lingzao does not exist, find the directory that contains lingzao's SKILL.md, then run scripts/setup.sh --skip-doctor from that directory.

Before Calling

Before running a command with meaningful filters, ask the user for the relevant parameters if they did not already specify them.

• •For search-notes, ask for sorting, note type, and time range before calling:

sort can be general, most_liked, popularity_descending, comment_descending, or collect_descending; note type can be 不限, 视频笔记, 图文笔记, or 直播笔记; time range can be 不限, 一天内, 一周内, or 半年内.

• •Douyin search-notes currently supports only general, most_liked, and

popularity_descending. Do not pass comment_descending or collect_descending for Douyin searches.

• •Douyin search-notes note type currently supports only 不限, 视频笔记,

and 图文笔记. Do not pass 直播笔记 for Douyin searches.

• •For get-note-comments, ask whether the user wants latest comments or

liked-count sorting before calling Xiaohongshu. Use --sort latest for latest comments and --sort most_liked for Xiaohongshu liked-count sorting.

• •Douyin comments currently support only latest. Do not ask for or pass

--sort most_liked on Douyin comment requests.

• •Xiaohongshu list-style commands (search-notes, get-user-posted-notes,

analyze-user-profile) return xhs_note_type on each note item when Lingzao can identify whether it is 图文 or 视频. When continuing from one of those note items to get-note-detail, pass the returned value directly as --xhs-note-type; do not infer the type from the URL. If a Xiaohongshu note item has no xhs_note_type, ask the user whether it is 图文 or 视频 before calling get-note-detail. get-note-comments can still be called without this type.

• •If the user explicitly says to use defaults, proceed with the documented

defaults instead of asking again.

After a successful research command, tell the user the estimated time saved shown in the CLI Markdown output. If you called multiple Lingzao research commands for one user request, summarize the total once. Do not show time-saved language for doctor, check-version, failed commands, or JSON-only internal processing.

Commands

Search Notes

~/.lingzao/bin/lingzao search-notes --platform xhs --keyword "AI写作"
~/.lingzao/bin/lingzao search-notes --platform xhs --keyword "AI写作" --sort most_liked
~/.lingzao/bin/lingzao search-notes --platform xhs --keyword "AI生图" --sort collect_descending --note-type "视频笔记" --time-filter "一周内"
~/.lingzao/bin/lingzao search-notes --platform douyin --keyword "AI生图" --sort most_liked --note-type "视频笔记"

Use this when the user wants public notes around a topic. Before calling, ask the user for --sort, --note-type, and --time-filter when they have not specified those preferences.

Search Suggestions

~/.lingzao/bin/lingzao search-suggestions --platform xhs --keyword "AI生图"
~/.lingzao/bin/lingzao search-suggestions --platform xhs

Use this when the user wants Xiaohongshu keyword expansions, autocomplete phrases, or popular search recommendations. If --keyword is omitted, Lingzao returns popular recommendations.

Search Creators

~/.lingzao/bin/lingzao search-users --platform xhs --keyword "母婴博主"
~/.lingzao/bin/lingzao search-users --platform douyin --keyword "AI生图"

Use this when the user wants creators in a topic or niche.

Get Creator Profile

~/.lingzao/bin/lingzao get-user-info --url "https://www.xiaohongshu.com/user/profile/..."
~/.lingzao/bin/lingzao get-user-info --platform xhs --user-id "63c21e0f000000002801a1bb"
~/.lingzao/bin/lingzao get-user-info --platform douyin --user-id "MS4wLjABAAAA..."

Use this when the user provides a creator profile URL or platform user ID and needs full profile-level stats. For Douyin bare user IDs, use the profile sec_user_id. For basic homepage analysis, prefer get-user-posted-notes and avoid calling both commands by default.

Get Creator Recent Posts

~/.lingzao/bin/lingzao get-user-posted-notes --url "https://www.xiaohongshu.com/user/profile/..."
~/.lingzao/bin/lingzao get-user-posted-notes --platform xhs --user-id "63c21e0f000000002801a1bb"
~/.lingzao/bin/lingzao get-user-posted-notes --platform douyin --user-id "MS4wLjABAAAA..." --limit 20

Use this when the user wants to understand what a creator has posted recently. Use this by default for basic creator homepage analysis. Douyin recent posts are a single-page call and currently support --limit 20 at most. If the user asks for full profile-level stats, add get-user-info; if the user asks for Xiaohongshu post copy, scripts, captions, or transcript text across recent posts, use analyze-user-profile instead. For Douyin transcript text, use extract-video-copy on selected video URLs.

Analyze Creator Profile

~/.lingzao/bin/lingzao analyze-user-profile --url "https://www.xiaohongshu.com/user/profile/..." --limit 20
~/.lingzao/bin/lingzao analyze-user-profile --platform xhs --user-id "63c21e0f000000002801a1bb" --limit 40
~/.lingzao/bin/lingzao analyze-user-profile --platform douyin --user-id "MS4wLjABAAAA..." --limit 20

Use this when the user wants deeper creator profile data, including post text, covers, commercial signals, and profile-level content signals. For Xiaohongshu, it also includes subtitle/script previews. For Douyin, it does not extract homepage subtitles or transcript text; use extract-video-copy on selected video URLs when the user needs spoken copy. Use --limit 20 by default. The default Markdown output shows readable subtitle previews when the platform provides them.

Important for Xiaohongshu: the complete profile subtitle/copy Markdown artifact is a top-level response field, not a per-note subtitle URL. Always check:

data.artifacts.subtitle_markdown.status data.artifacts.subtitle_markdown.url

Do not search only inside items[]. If data.artifacts.subtitle_markdown.status == "ready" and url exists, download it before deep script or subtitle analysis:

curl -L "$subtitle_markdown_url" -o /tmp/lingzao-profile-subtitles.md

Use the downloaded Markdown file for complete subtitle/copy analysis. Use --format json when the user needs the structured fields. JSON includes data.artifacts.subtitle_markdown.url for the complete Markdown file when available, and inline items[].text.subtitle.content/plain_text are preview-sized to keep the response readable. If the artifact is unavailable, use the inline subtitle fields. For Douyin, expect data.artifacts.subtitle_markdown.status == "unsupported" and use the returned profile insights plus selected-video extraction instead.

Get Post Detail

~/.lingzao/bin/lingzao get-note-detail --url "https://www.xiaohongshu.com/explore/..." --xhs-note-type image
~/.lingzao/bin/lingzao get-note-detail --platform xhs --note-id "69690331000000001a02266a" --xhs-note-type video
~/.lingzao/bin/lingzao get-note-detail --platform douyin --note-id "7372484715782352169"

Use this when the user asks to analyze one public post. For Xiaohongshu details, pass --xhs-note-type image for 图文 and --xhs-note-type video for 视频. If the note came from search-notes, get-user-posted-notes, or analyze-user-profile, reuse that item's xhs_note_type value.

Get Post Comments

~/.lingzao/bin/lingzao get-note-comments --url "https://www.xiaohongshu.com/explore/..."
~/.lingzao/bin/lingzao get-note-comments --url "https://www.xiaohongshu.com/explore/..." --sort most_liked
~/.lingzao/bin/lingzao get-note-comments --platform xhs --note-id "69690331000000001a02266a"
~/.lingzao/bin/lingzao get-note-comments --platform douyin --note-id "7372484715782352169"
~/.lingzao/bin/lingzao get-note-comments --url "https://www.douyin.com/jingxuan?modal_id=..." --cursor "next_cursor_from_previous_response"

Use this when the user asks for public comments on one post. The first version returns top-level comments only. Use --sort most_liked for Xiaohongshu liked-count sorting; Douyin currently supports the default latest sort only. If the response has data.page.next_cursor, pass that value with --cursor to fetch the next page. Before calling Xiaohongshu comments, ask whether the user wants latest comments or liked-count sorting. For Douyin comments, use only --sort latest; do not pass --sort most_liked.

Get WeChat Official-Account Articles

~/.lingzao/bin/lingzao get-article-detail --url "https://mp.weixin.qq.com/s/..."
~/.lingzao/bin/lingzao get-article-stats --url "https://mp.weixin.qq.com/s/..."
~/.lingzao/bin/lingzao get-related-articles --url "https://mp.weixin.qq.com/s/..."

Use these when the user provides a public WeChat official-account article URL and asks to analyze the article, inspect public engagement metrics, or expand from that article to related public articles. The first version is URL-only and costs 20 credits per call. An empty related-articles list is a valid response. Do not use these commands for account article history, account listing, or multi-page fanout unless Lingzao adds a separate capability.

Extract Short-Video Copy

~/.lingzao/bin/lingzao extract-video-copy --url "https://www.xiaohongshu.com/explore/..."
~/.lingzao/bin/lingzao extract-video-copy --url "https://v.douyin.com/..."

Use this when the user asks for short-video spoken copy, transcript, subtitles, or口播文案.

Generate Image

~/.lingzao/bin/lingzao generate-image --prompt "一张小红书封面图，主题是 AI 生图新手避坑，干净明亮，中文大标题留白" --output /tmp/lingzao-image.png
~/.lingzao/bin/lingzao generate-image --prompt "极简产品海报，白底，柔和阴影" --size 1024x1536 --output /tmp/poster.png
~/.lingzao/bin/lingzao generate-image --prompt "参考两张图，保留人物风格，把产品界面换成灵造首页截图" --size 1536x2048 --image /tmp/style.png --image /tmp/product.png --output /tmp/poster.png
~/.lingzao/bin/lingzao generate-image --prompt "批量生成 3 张封面草稿" --count 3 --size 1024x1536 --output /tmp/poster.png

Use this only when the user asks to generate a creator image asset. For normal research, do not call image generation automatically.

Before calling generate-image, run the minimal intake gate. If the user only says something like "给我做一张某某海报图" or provides only a broad topic, do not spend credits immediately. Ask for the two visual anchors first:

1. 你有没有参考图？可以发 1-3 张你喜欢的封面/海报/图文截图。
2. 你有没有想要的配色？比如明亮白底、绿色清爽、黑金高级、蓝色科技感。

If those are still unclear, ask at most one extra route-changing question, such as the publishing platform/size, exact on-image text, or whether the user wants people/no people. Only proceed directly without asking when the user already provided enough constraints: topic + platform/format + visual style/reference or color + on-image text/material. Use --image for local reference images; repeat it for multiple images. The Skill uploads those files directly to Lingzao for the current request, so the user does not need to upload them elsewhere first. Supported reference image formats are png, jpeg, and webp.

For Codex, WorkBuddy, and other agent runtimes:

• •--image accepts local filesystem paths only. If the user provides a

reference image through a chat attachment, pasted image, screenshot, or input box, first materialize that image as a local file before calling the CLI. Preserve the original supported image format when saving the file.

• •Use a per-run temporary directory for runtime-provided images, for example

/tmp/lingzao-image-inputs/<run-id>/ref-1.png and /tmp/lingzao-image-inputs/<run-id>/ref-2.png. Use absolute paths in the CLI call.

• •If the user already provided a stable local path, such as a file under

/Users/..., you may pass that path directly. If the runtime-provided image lives in a transient attachment/cache path, copy it into the per-run temp directory first.

• •Do not proactively convert image formats. If the input image is already png,

jpeg, or webp and its file size is reasonable, pass it as-is. Do not convert png to webp or jpeg just because an example path uses a different extension.

• •Only when a reference image is larger than 2 MB, create a smaller copy in the

temp directory and pass that copy with --image. Keep the file extension and actual image bytes consistent. If resizing or compression fails, use the original supported image file instead of trying another format.

• •Do not overwrite the user's original image file. Do not store reference

images in the repo. If the runtime cannot save an uploaded or pasted image to a local path, ask the user to save the image locally and provide the path.

• •In the prompt, state what should be borrowed from the reference images, such

as layout, color palette, product shape, character style, or composition. Do not say only "reference this image" when a more specific instruction is possible.

Example with a runtime-provided reference image:

mkdir -p /tmp/lingzao-image-inputs/run-001 /tmp/lingzao-image-outputs/run-001
~/.lingzao/bin/lingzao generate-image \
  --prompt "参考这张图的排版和明亮色彩，生成一张小红书封面图，主题是 AI 生图新手避坑，中文大标题留白" \
  --size 1024x1024 \
  --image /tmp/lingzao-image-inputs/run-001/ref-1.png \
  --output /tmp/lingzao-image-outputs/run-001/result.png

The command creates a Lingzao async batch and automatically polls the returned status URL until the background job finishes or the command timeout is reached. Image generation can take several minutes; --timeout can extend waiting for large or slow batches, but does not shorten the built-in per-image polling window. For one image, --output writes the result to the exact path you provide. For --count greater than 1, --output /tmp/poster.png writes every successful image as numbered files such as /tmp/poster-1.png, /tmp/poster-2.png, and so on. Default Markdown output requires --output so paid generated images are saved locally. Use --format json only when you need structured item statuses or raw image payloads.

Usage Notes

• •For profile and post URLs, pass the URL directly when possible.
• •For raw IDs, include --platform.
• •Omit --limit unless the user asks for a specific count.
• •Search notes default to comprehensive sorting, all note types, and all time; use --sort, --note-type, and --time-filter when the user asks for ranked or filtered note search.
• •Use --format json only when another tool needs structured output.
• •Default output is Markdown for agents to read and summarize.
• •If the API key or account needs attention, ask the user to open the Lingzao dashboard.