---

name: lov-repo2docs description: > Turn any folder of source material — a code repository, a pile of articles, a mixed knowledge dump with images — into a professional, polished Fumadocs (Next.js) documentation website, then deploy it to https://{product-id}.example.com/docs. Works by reading the folder one unit at a time and incrementally growing and refining the docs structure and detail, so it scales to large folders and handles images as first-class content (copied into the site, embedded inline, auto-galleried). Trigger when the user wants to "generate docs", "build a docs site", "make a documentation website", "fumadocs", points at a code repo OR a content/article folder, or says "为项目生成文档", "把这个文件夹做成文档站", "做文档站", "生成文档网站", "整理成知识库网站". license: MIT compatibility: > Portable Agent Skills format. Requires Node.js 18+, npx, git, and Python 3.8+. Image optimization optionally uses Pillow (graceful fallback to verbatim copy). Deployment delegates to the lov-deploy-to-vercel skill (Cloudflare DNS auto-config needs CLOUDFLARE_API_KEY). The {product-id} subdomain and example.com base domain are user-configurable. depends_on:

• lov-deploy-to-vercel
• lov-branding-consistency metadata: author: contributors version: "0.3.1" tags: docs fumadocs documentation nextjs vercel codebase articles knowledge-base images

---

文档站工坊 · Docs Site Builder

Turn any folder of source material into a professional Fumadocs documentation website and deploy it to https://{product-id}.example.com/docs.

A code repo and a folder of articles are the same thing: a folder of source material. There is one unified flow, not separate modes. The difference is only in what each file contributes — code becomes explained API/usage docs; an article becomes a presented page; an image becomes embedded media.

The core idea: read the folder one unit at a time and incrementally refine the docs. Don't read everything then write everything. Each file you ingest updates the evolving outline and fills in or improves pages — including earlier ones. This scales past the context window and produces a coherent, deduplicated result.

User Configuration

Defaults are portable. Base domain is example.com; subdomain is the product id. Deploy delegates to lov-deploy-to-vercel (reads CLOUDFLARE_API_KEY). See references/user-config.md.

When to Use

• "用 fumadocs 给这个项目生成文档站并部署到 xxx.example.com/docs"
• "把这个装满文章和图片的文件夹整理成一个文档网站"
• A code repo, an article collection, or a mixed knowledge folder needs a polished, navigable, image-rich docs site

Workflow (MANDATORY — follow in order)

Step 0: Resolve skill root

export SKILL_DIR="${SKILL_DIR:-$(pwd)}"   # or the installed lov-repo2docs dir

If any network command times out in the sandbox, export the proxy once:

export https_proxy=http://127.0.0.1:7890 http_proxy=http://127.0.0.1:7890 all_proxy=socks5://127.0.0.1:7891

Step 1: Collect inputs with AskUserQuestion

Use AskUserQuestion BEFORE doing anything, unless the user already gave all:

• Source: GitHub URL, local folder path, or current directory?
• Product id: the subdomain → {product-id}.example.com. Propose a slug from the folder/repo name; confirm.
• Title: human-facing name for the docs.
• Deploy now?: deploy to Vercel + bind subdomain immediately, or generate only.

Step 2: Resolve the source folder

Source	Action
GitHub URL	git clone --depth 1 <url> <tmp>/src
Local path	use as-is (read-only)
Current dir	use $(pwd)

Never write into the source folder. The docs site is a separate directory.

Step 3: Inventory the folder

Build a manifest of every file, classified and ordered (overview material first):

python3 "$SKILL_DIR/scripts/inventory.py" --src "<src>" --out /tmp/manifest.json

The manifest lists units (path, kind ∈ article/code/image/pdf/office/data/other, title guess, reading order) and counts. This is cheap — it does NOT read full contents. You read contents later, incrementally. Use counts to gauge scale and to decide whether to delegate batches to subagents (see Step 5 scaling note).

Step 4: Scaffold the Fumadocs site

python3 "$SKILL_DIR/scripts/scaffold_docs.py" \
  --product-id "<id>" --out "<docs-out-dir>" --title "<Title>" --pm pnpm

Sets basePath: '/docs' and resets content/docs/ to a placeholder. See references/fumadocs.md for layout, the /docs double-prefix gotcha, and components.

Step 5: Copy images into the site

python3 "$SKILL_DIR/scripts/copy_assets.py" \
  --src "<src>" --site "<docs-out-dir>" --out /tmp/assets-map.json

This copies (and, if Pillow is present, downsizes/transcodes HEIC/TIFF/BMP) every image into public/assets/... and returns a {source-path: /assets/...} map. Reference images in MDX with the root-relative web path from the map (![](/assets/foo.png)) — with basePath /docs, Next resolves it to /docs/assets/foo.png automatically.

Step 6: Incremental authoring loop (the core)

Initialize the outline from the folder's top-level structure — by default, preserve the source directory hierarchy as the sidebar (a folder → a sidebar group, its meta.json). Then walk units in order and, for each unit:

1. Read the unit (the file contents).
2. Place it in the evolving outline: a new page, a section of an existing page, or merged into a group index. Update the relevant meta.json.
3. Write or refine the MDX page:
   • For articles/notes/PDF/office: present the content as a real page — preserve the author's substance; clean up formatting; embed its images via the assets map; if a folder holds many images, render a gallery/grid.
   • For code: write explained docs — purpose, install, usage, public API, examples — derived from the code, README, and comments. Don't dump source.
   • For images with no surrounding article: group them into a gallery page for their folder.
4. Refine backward: as later units add context, improve earlier pages, the intro, cross-links, and sidebar order. The structure is emergent and incrementally polished, not one-shot.

Quality bar: a professional, polished site — coherent IA, working internal links, real content (never lorem ipsum), inviting landing page, images that render crisply. Use Fumadocs components (<Cards>, <Tabs>, <Steps>, <Callout>, <ImageZoom>/gallery) per references/fumadocs.md.

Scaling note (large folders): when counts is large, delegate batches to subagents (Explore / general-purpose). Give each a slice of units + the assets map; have it return structured page contributions and outline deltas (which group, which order). The main thread owns and merges the evolving IA so the result stays coherent. Don't let subagents each invent a separate top-level structure.

Step 7: Verify the build locally

cd "<docs-out-dir>" && pnpm build    # must succeed — fix MDX/link/image errors

Step 8: Deploy (if requested)

Delegate to lov-deploy-to-vercel — do NOT reimplement Vercel/DNS here. Ensure package.json "name" is a valid lowercase slug, then deploy with domain {product-id}.example.com. The site serves at …/docs via the basePath. After deploy, verify the live URL returns 200 (curl), not just that the alias was set.

CLI Reference

scripts/inventory.py — enumerate + classify a folder:

Argument	Default	Description
--src	(required)	Source folder
--out	stdout	Manifest JSON path
--max-bytes	2000000	Skip title-reading files larger than this

scripts/copy_assets.py — images → public/assets/:

Argument	Default	Description
--src	(required)	Source folder
--site	(required)	Fumadocs site root
--max-width	1600	Downscale wider images (needs Pillow)
--no-optimize	off	Copy verbatim, never transcode/resize
--out	stdout	Asset path-map JSON

scripts/scaffold_docs.py — scaffold the site:

Argument	Default	Description
--product-id	(required)	Slug → {id}.example.com
--out	(required)	Output dir (separate from source)
--title	=product-id	Human-facing title
--pm	pnpm	Package manager

Dependencies

• Node.js 18+, npx, git, Python 3.8+
• Optional: Pillow (pip install Pillow) for image optimization
• Vercel CLI (npm i -g vercel) for deploy
• lov-deploy-to-vercel skill (Vercel + Cloudflare DNS)

For Fumadocs structure, components, and config, see references/fumadocs.md.

Runtime context (shared)

运行前读取本 Skill 包的 skill.yaml，由宿主提供 skill-runtime/v1 上下文。字段解析顺序为：当前请求、项目上下文、个人 Preferences、品牌 Profile、通用默认值。

• 只使用 Manifest 声明的字段；Profile 保存公开品牌事实，Preferences 保存个人工作偏好。
• required: true 字段缺失时，按 Manifest 的问题配置向用户提出一个聚焦问题；用户明确同意后再保存回答。
• 报错提供可复制的 context_id、字段路径与来源，诊断内容避开秘密、完整私人路径和原始配置。

通用反馈闭环

用户在 Skill 驱动任务中提出修改意见时，继续当前产物前必须执行：

1. 先判断意见是 task-specific（仅本次）还是 reusable（可跨任务复用）。
2. task-specific 只修改当前任务，不改 Skill。
3. reusable 先确定作用域：领域规则先更新对应 canonical Skill；适用于所有 Skill 的规则先更新共享规范。
4. 完成规则更新、版本、lint 与分发核验后，再把修改应用到当前任务。
5. reusable 修改会使此前的“确认”“继续”“发吧”失效；完成当前产物修改和回读后必须停下，等待用户下一步指示，不自动进入发布、提交或其他外部写入。