Agent skill

Xhs Imagen

by NimaChu in NimaChu/xhs-imagen

Create complete Xiaohongshu image-and-text posts from a topic, article, document, or rough idea, including research, fact checking, post copy, pagination, storyboards, a 3:4 cover, 9:16 content…

MITAuto-check passedMedia & Creative

Install Xhs Imagen

skills CLI
$ npx skills add NimaChu/xhs-imagen --skill xhs-imagen -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NimaChu/xhs-imagen xhs-imagen --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
xhs-imagen
GitHub stars
120
Token cost
~2.3k tokens
SKILL.md length
973 words
Files
45 (incl. scripts, references, assets)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Create complete Xiaohongshu image-and-text posts from a topic, article, document, or rough idea, including research, fact checking, post copy, pagination, storyboards, a 3:4 cover, 9:16 content…

  • Works in 7 steps: Resolve the brief → Research before writing → Build the content arc → …
  • 小红书图文、小红书封面、知识卡片、漫画科普、文章转图片、AI/科技科普图组、逐页生图提示词
  • SKILL.md covers Required result, Fixed Xiaohongshu defaults, Workflow and Final response
  • Calls python3

What it does

Xhs Imagen is an agent skill from NimaChu/xhs-imagen. Create complete Xiaohongshu image-and-text posts from a topic, article, document, or rough idea, including research, fact checking, post copy, pagination, storyboards, a 3:4 cover, 9:16 content pages, image generation or fully local SVG-to-PNG fallback, and final quality review. Use for 小红书图文、小红书封面、知识卡片、漫画科普、文章转图片、AI/科技科普图组、逐页生图提示词, or revisions to an existing Xiaohongshu series. Prefer an available image-generation model; use the bundled local renderer only when no image-generation tool is available. Apply an…

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 53 other files, including scripts, reference files and assets (for example `.github/workflows/validate.yml`, `AGENTS.md` and `README.en.md`).

It sits in Media & Creative, covering Image generation, Comics and storyboards and Fact-checking and source verification. It works with Xiaohongshu. The repository describes itself as: Skill for popular science image content creation on Xiaohongshu. The licence is MIT.

When your agent uses it

  • 小红书图文、小红书封面、知识卡片、漫画科普、文章转图片、AI/科技科普图组、逐页生图提示词
  • Revisions to an existing Xiaohongshu series

Example prompts

  • “/xhs-imagen”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Resolve the brief
  2. Research before writing
  3. Build the content arc
  4. Create and validate the project
  5. Choose the rendering path automatically
  6. Repair text only after explicit user feedback
  7. Inspect every output

What it can do on your machine

Read from SKILL.md and the folder at commit 668427a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Xhs Imagen loads about 2.3k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 156 tokens; SKILL.md has 973 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~156
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~24k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NimaChu/xhs-imagen at commit 668427a, republished under its MIT licence (© NimaChu). 973 words, ~2,294 tokens.

Download SKILL.mdSave it as .claude/skills/xhs-imagen/SKILL.md (or your agent's skills folder). This skill also uses 44 other files; get the full folder from GitHub.
name
xhs-imagen
description
Create complete Xiaohongshu image-and-text posts from a topic, article, document, or rough idea, including research, fact checking, post copy, pagination, storyboards, a 3:4 cover, 9:16 content pages, image generation or fully local SVG-to-PNG fallback, and final quality review. Use for 小红书图文、小红书封面、知识卡片、漫画科普、文章转图片、AI/科技科普图组、逐页生图提示词, or revisions to an existing Xiaohongshu series. Prefer an available image-generation model; use the bundled local renderer only when no image-generation tool is available. Apply an SVG text patch only after the user explicitly reports incorrect or unreadable text in an image.

xhs-imagen

Turn one topic or source article into a publication-ready Xiaohongshu post package.

Required result

Create:

text
output/<topic-slug>/
├── project.json
├── research.md
├── post.md
├── storyboard.md
├── prompts/
│   ├── 00-cover.md
│   ├── 01-*.md
│   └── ...
├── images/
│   ├── cover.png
│   ├── page-01.png
│   └── ...
└── qa-report.md

If the user requests only part of the package, create only that part. Never claim that images were generated when no image-generation or local SVG renderer was available.

Fixed Xiaohongshu defaults

  • Create a 3:4 cover, recommended 1080 × 1440.
  • Create 9:16 information pages, recommended 1080 × 1920.
  • Default to one cover plus 5–8 information pages.
  • Use Simplified Chinese unless requested otherwise.
  • Explain one dominant idea per page.
  • Optimize titles and labels for phone reading.
  • Do not render page numbers or page-position markers such as 04, 01/08, or PAGE 04. Keep ordering only in filenames. Numbered steps are allowed when the numbers explain the content itself.
  • Default to the alpaca-line-art profile: pure-white background, fine black hand-drawn lines, and the bundled white alpaca creator IP.
  • Preserve glasses-chibi-blue as a selectable profile for the original glasses-wearing host, warm off-white paper, and cobalt-blue comic style.
  • Use toolbox-bot-risograph for tool ecosystems, plugins, Skills, Agents, and workflows in two-color risograph.
  • Use maker-girl-editorial for professional AI Coding, workplace, tutorial, and opinion content in modern editorial illustration.
  • Use cyber-luban-woodcut for Skill–Harness–Agent architecture and system-building topics in new-Chinese woodcut.
  • Use capybara-gouache for beginner explainers, pitfalls, reassurance, and everyday analogies in warm gouache.
  • Make the character perform the page's core conceptual action; never use it as corner decoration.

Read references/visual-profiles.md, references/visual-style.md, and references/character-consistency.md before producing images.

Workflow

1. Resolve the brief

Determine or infer:

  • topic, audience, and desired outcome;
  • the single sentence readers should remember;
  • source material and whether facts may have changed;
  • page count and language;
  • selected visual profile, character, palette, and brand constraints;
  • whether the user wants a complete package, images, copy, or prompts.

Use beginner-friendly AI/technology education as the default audience and tone when the request does not specify them.

Store the choice in project.json as visual_profile. Honor an explicit user choice; otherwise use the default declared in references/visual-profiles.json. Use one profile for the whole series unless the user explicitly requests otherwise.

When reviewing pages the user selected or rejected, distinguish explicit feedback from inferred preference. Treat only explicitly confirmed rules as durable defaults; use the final selection primarily to understand visual appeal and expression accuracy rather than infer rigid layout rules.

2. Research before writing

Search primary and authoritative sources for current, technical, disputed, product-specific, numerical, legal, or attributed claims. Write a claim table to research.md. Separate sourced facts from analogies and editorial framing.

Read references/fact-checking.md for detailed rules.

3. Build the content arc

Create:

  • one thesis;
  • one useful analogy;
  • one misconception;
  • 4–7 supporting ideas;
  • one limitation, boundary, or human-control point;
  • one final takeaway.

Select pages by cognitive anchors instead of distributing content evenly. Keep only moments that change what the reader understands: a core judgment, cognitive turn, comparison, bottleneck, boundary, common mistake, state change, or takeaway. Drop a page when removing it does not weaken the learning arc.

For every selected page:

  1. state the cognitive anchor and why it deserves a page;
  2. convert the abstract concept into a physical action;
  3. map that action to one ordinary low-tech object;
  4. make the character perform the action so the metaphor depends on the character.

Write post.md with a Xiaohongshu title, publishable body copy, optional source note, and relevant hashtags. Write storyboard.md before generating images.

Read references/content-planning.md when choosing pages and reducing copy. Read references/visual-metaphors.md before writing the storyboard or image prompts.

4. Create and validate the project

Store exact content and page decisions in project.json. Start from references/project.template.json and follow references/project.schema.json.

Validate it:

bash
python3 scripts/validate_project.py /absolute/path/project.json

Generate the image-model prompt files:

bash
python3 scripts/make_prompt_pack.py \
  /absolute/path/project.json \
  --output-dir /absolute/path/output/prompts
Show full SKILL.md (385 more words)Show less
5. Choose the rendering path automatically
When an image-generation tool is available

Use it as the default path.

  1. Resolve the selected profile in references/visual-profiles.json.
  2. Use that profile's character_reference as the only bundled image reference. Never attach multiple profile references to one generation call.
  3. Generate the cover and one representative inner page first.
  4. Inspect character identity, core action, metaphor originality, typography, spacing, color, copy accuracy, and absence of page-position markers.
  5. Lock the successful visual description.
  6. Generate the remaining pages using the same reference and style lock.
  7. Save files as cover.png, page-01.png, and so on.

Do not invoke the local renderer merely to pre-empt possible text errors.

When no image-generation tool is available

Use the bundled local SVG-to-PNG renderer:

bash
python3 scripts/render_xiaohongshu_project.py \
  /absolute/path/project.json \
  --output-dir /absolute/path/output

This path preserves the Xiaohongshu cover and inner-page ratios while converting the project into deterministic local knowledge-card layouts. Read references/local-rendering.md for limitations and renderer requirements.

6. Repair text only after explicit user feedback

Do not create a separate hybrid workflow. If the user explicitly identifies incorrect, corrupted, or unreadable text in an existing image:

  1. Confirm the target image, exact replacement text, and affected region.
  2. Prefer local image editing or regeneration when available.
  3. If the problem remains, apply a deterministic SVG overlay only to that region:
bash
python3 scripts/patch_image_text.py \
  --input /absolute/path/page.png \
  --output /absolute/path/page-fixed.png \
  --visual-profile <selected-profile> \
  --x 100 --y 300 --width 880 --height 180 \
  --text "正确文字"
  1. Inspect the repaired image before delivery.

Never apply an SVG text patch speculatively.

7. Inspect every output

Write qa-report.md. For every failed check, record the defect, repair action, and recheck result; do not stop at listing problems. Verify:

  • correct ratio and orientation;
  • readable, accurate Chinese and product names;
  • background, line treatment, palette, and typography match the selected visual profile;
  • one dominant idea per page;
  • a meaningful cognitive anchor on every page;
  • an original physical metaphor with one primary structure;
  • the character performs the metaphor's core action;
  • stable profile-specific character identity and proportions;
  • valid diagram flow;
  • no cropped titles, faces, hands, or summaries;
  • no unsupported factual claims or invented quotations;
  • no visible page number, page count, or page-position marker; content-level numbered steps remain allowed;
  • ordered, stable filenames.

Run:

bash
python3 scripts/check_png_ratios.py /absolute/path/output/images

Read references/quality-checklist.md for the full review.

Final response

Provide:

  • a concise summary of the content arc;
  • the cover and ordered pages, or links to their files;
  • the publishable post copy;
  • source citations for time-sensitive claims;
  • an honest note about any unresolved image or text defect.

© NimaChu, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 44 other files (scripts, references, assets) in the repository root of NimaChu/xhs-imagen.

  • SKILL.md
  • .github/workflows/validate.yml
  • .gitignore
  • AGENTS.md
  • LICENSE
  • README.en.md
  • README.md
  • agents/openai.yaml
  • assets/characters/alpaca-line-art/character-sheet.png
  • assets/characters/capybara-gouache/character-sheet.png
  • assets/characters/cyber-luban-woodcut/character-sheet.png
  • assets/characters/glasses-chibi-blue/character-sheet.png
  • … and 33 more

Open the folder on GitHubat commit 668427a

Compare with similar skills

Xhs Imagen next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Xhs Imagen compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Xhs Imagen this skillNimaChu/xhs-imagen120—~2.3kAutomated safety check: PassMIT
Canghe Comicfreestylefly/canghe-skills4618 repos~3.2kAutomated safety check: PassNone
Xhs Visual Directorziguishian/xhs-visual-director-skill1.4k—~2.2kAutomated safety check: PassMIT
Xiaohongshu Cover GeneratorVivixiao980/xhs-cover-skill204—~2.9kAutomated safety check: PassNone
Hbg Life SimulationMr-funny/hbg-life-simulation141—~5.8kAutomated safety check: PassMIT
Knowledge Comic CreatorJimLiu/baoyu-skills27k2 repos~5.5kAutomated safety check: PassMIT

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Works with

Questions about Xhs Imagen

What does Xhs Imagen do?

Create complete Xiaohongshu image-and-text posts from a topic, article, document, or rough idea, including research, fact checking, post copy, pagination, storyboards, a 3:4 cover, 9:16 content…. Xhs Imagen is an agent skill from NimaChu/xhs-imagen. Create complete Xiaohongshu image-and-text posts from a topic, article, document, or rough idea, including research, fact checking, post copy, pagination, storyboards, a 3:4 cover, 9:16 content pages, image generation or fully local SVG-to-PNG fallback, and final quality review.

When should I use Xhs Imagen?

Xhs Imagen fits situations like: 小红书图文、小红书封面、知识卡片、漫画科普、文章转图片、AI/科技科普图组、逐页生图提示词; revisions to an existing Xiaohongshu series.

How do I install Xhs Imagen in Claude Code?

Run `npx skills add NimaChu/xhs-imagen --skill xhs-imagen -a claude-code`. Or copy the skill folder (the NimaChu/xhs-imagen repository) into .claude/skills/xhs-imagen in your project. Claude Code loads it when a task matches its description.

How do I install Xhs Imagen in Codex?

Run `npx skills add NimaChu/xhs-imagen --skill xhs-imagen -a codex`. Or copy the skill folder (the NimaChu/xhs-imagen repository) into .agents/skills/xhs-imagen in your project. Codex loads it when a task matches its description.

Can I use Xhs Imagen in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NimaChu/xhs-imagen --skill xhs-imagen -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/xhs-imagen, .gemini/skills/xhs-imagen, .github/skills/xhs-imagen and .opencode/skills/xhs-imagen in your project.

What does Xhs Imagen need to run?

Going by SKILL.md and its folder, Xhs Imagen needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Xhs Imagen access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Xhs Imagen safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Xhs Imagen use?

Xhs Imagen is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Xhs Imagen use?

About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 22k tokens, read only when the agent opens those files.

What are the alternatives to Xhs Imagen?

Skills that share tags, products or a category with Xhs Imagen: Canghe Comic (freestylefly/canghe-skills, 461 stars), Xhs Visual Director (ziguishian/xhs-visual-director-skill, 1.4k stars), Xiaohongshu Cover Generator (Vivixiao980/xhs-cover-skill, 204 stars) and Hbg Life Simulation (Mr-funny/hbg-life-simulation, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Xhs Imagen?

NimaChu (a GitHub user) maintains it in NimaChu/xhs-imagen, which has 120 GitHub stars. The repository was last updated on July 28, 2026.

Source: NimaChu/xhs-imagen on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.