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Feishu Doc Scraper

Free

Extract Feishu documents into Markdown with fidelity.

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Free · Opens the source repo

What Feishu Doc Scraper does

Feishu Doc Scraper is a specialized tool designed to extract content from Feishu (also known as Lark) documents, wiki pages, spreadsheets, and Minutes transcripts into local Markdown format. This skill primarily utilizes the lark-cli API for extraction, ensuring that the content is captured faithfully without any paraphrasing or rewriting by language models. This is particularly important for users who require accurate documentation and archiving of their Feishu content. The tool also includes a fallback mechanism that uses a browser DOM extraction method when the lark-cli API cannot access the content, although this method is intended as a secondary option.

The extraction process is straightforward: users provide a Feishu/Lark URL, and the tool determines the best extraction path based on the type of content. If the lark-cli API is available and authenticated, it will be used to fetch the document directly. In cases where permission issues arise, users can opt to export the document as a .docx file and convert it to Markdown using the provided scripts. This ensures that even if direct API access is not possible, users can still retrieve their content with minimal fidelity loss.

Feishu Doc Scraper is particularly useful for developers and designers who need to archive or convert their documentation into a more accessible format. It is designed to handle complex collections of documents, following reference graphs to ensure all linked content is extracted. This makes it an ideal tool for teams that rely heavily on Feishu for collaboration and documentation, allowing them to maintain a comprehensive and organized knowledge base.

In summary, this skill is focused on providing a reliable method for extracting Feishu content into Markdown, preserving the integrity of the original documents while offering flexibility in handling various types of content. Users can integrate this tool into their workflows for efficient documentation management and archiving.

When to use it

Use this tool when you need to extract documents, wiki pages, or transcripts from Feishu and require accurate Markdown output.

When not to use it

This skill is not suitable for users who do not have access to the lark-cli API or need to extract content from non-Feishu sources.

What you can build with it

Archiving Feishu Wiki Pages

Use the Feishu Doc Scraper to extract entire wiki pages into Markdown for archiving and easy access.

Converting Feishu Minutes to Markdown

Extract transcripts from Feishu Minutes and convert them to Markdown for better documentation and sharing.

Exporting Complex Document Collections

Utilize the tool to recursively extract all linked documents within a Feishu collection, ensuring nothing is missed.

How to install Feishu Doc Scraper

View source

1. Install with the skills CLI

npx skills add daymade/claude-code-skills/feishu-doc-scraper --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by daymade

Feishu Doc Scraper

Extract a Feishu/Lark source into faithful local Markdown. Prefer the lark-cli API — it extracts the body programmatically (no model paraphrasing), follows a collection's reference graph, and reads permission boundaries from error codes instead of guessing. Treat the rendered browser page as a fallback, not the source of truth: in real collection-scraping work the API path consistently does the whole job while the browser path is never needed.

Scope (read this first)

This skill's contract is faithful per-source Markdown + a record of what was extracted. It does not decide how the resulting files are named, indexed, deduplicated against existing notes, or organized into a knowledge base — that belongs to the host PKM / the user's own conventions. Stopping at faithful extraction keeps this skill orthogonal and reusable. When the user wants the output filed into a vault, extract first, then hand the clean Markdown to their organizing workflow.

Choose the path

Is the source a Feishu/Lark URL (wiki / docx / sheets / minutes / base)?
├── YES → is lark-cli installed and authenticated to that tenant?
│        ├── YES → PATH A: lark-cli API extraction  (primary — start here)
│        │         └── hit code 131006 / 99991679 (permission denied)?
│        │              └── PATH B: owner-exported .docx → faithful Markdown
│        └── NO  → install/auth lark-cli first (it is worth it); only if
│                  truly impossible → PATH D: browser DOM fallback
├── the URL is a Minutes / 妙记 link, or a doc references one → PATH C: Minutes transcript
└── you were handed an exported .docx (not a URL) → PATH B

A collection/hub is just a docx whose body references other docs — Path A handles it by recursively following the reference graph, not by visiting pages in a browser.

Path A — lark-cli API extraction (primary)

Full command catalog, recursion engine, cross-tenant and personal-space nuances: references/lark-cli-api-extraction.md. The essentials for the common case:

1. Disable the proxy for Feishu domestic domains. Feishu's *.feishu.cn endpoints are direct-connect in mainland China; routing them through a local proxy leaks credentials through the proxy and gets DNS-hijacked. lark-cli itself warns about this. Always:

export LARK_CLI_NO_PROXY=1

This does not conflict with any "Claude/Anthropic domains must use the proxy" rule — Feishu is a different host and is direct.

2. Classify the URL, then resolve to a fetchable doc token.

  • …/wiki/<node_token> — a wiki node token is not a doc token. Resolve it first:
    lark-cli wiki spaces get_node --params '{"token":"<node_token>"}'
    # → .data.node.obj_token  and  .data.node.obj_type  (e.g. "docx")
    
  • …/docx/<doc_token> — already a doc token, fetch directly.
  • …/sheets/<token> — spreadsheet, use the sheets commands (see reference).
  • …/minutes/<token> — Minutes, go to Path C.

3. Fetch the body programmatically — never via the model. The body field moved across lark-cli versions, so probe both rather than hard-coding one (this keeps working whichever version is installed):

lark-cli docs +fetch --doc <obj_token> --format json > /tmp/fetch.json 2> /tmp/fetch.err
# ≤1.0.32: clean Markdown in .data.markdown.
# 1.0.55: body moved to .data.document.content as HTML (.data.markdown is null).
if jq -e -r '.data.markdown // empty' /tmp/fetch.json > source.md && [ -s source.md ]; then
  : # got clean Markdown directly
else
  jq -r '.data.document.content' /tmp/fetch.json | pandoc -f html -t gfm > source.md
fi

--format markdown is not a valid value (lark-cli warns and falls back to json). Keep stdout and stderr separate — a harmless [deprecated] line goes to stderr, and piping 2>/dev/null and jq together produced a false Exit code 5 in practice. The body must reach disk via jq/pandoc, never retyped or summarized by the model — paraphrasing silently corrupts source text, the single most important fidelity rule. (pandoc only re-renders HTML structure to Markdown; it does not rewrite prose, so fidelity holds.)

4. If it's a collection/hub, follow the reference graph (BFS). The hub body contains <mention-doc>, <sheet>, <image> tags and cross-tenant / Minutes / Tencent-Meeting URLs. Extract every reference, dispatch by type, fetch, and repeat on each newly fetched doc until no new references remain (leaf nodes). Use the bundled extractor so nothing is silently missed (a missed reference = a missing document, the #1 hub-scraping failure):

python3 scripts/feishu_extract_refs.py source.md   # → JSON list of {type, token, title}

Recursion loop, dispatch table, and the cross-tenant/my.feishu.cn personal-space rules are in the reference.

5. Final residual-tag check (acceptance gate for collections). Every rich-media reference must have been resolved and rendered:

grep -rlE '<(lark-table|lark-tr|sheet token=|mention-doc|view type=)' . && echo "UNRESOLVED — keep recursing" || echo "clean"

Must be empty before you stop.

Path B — permission denied → owner-exported .docx

lark-cli wiki spaces get_node returning code 131006 … node permission denied, user needs read permission (or fetch returning it) is a hard Feishu-side boundary. lark-cli, anonymous curl, and the browser all fail it — this has been verified exhaustively; do not spend cycles trying to bypass it. The only correct move: ask the permission holder to export the doc as .docx and send it back out-of-band, then convert with fidelity (font-size→heading and w:shd→highlight restoration, then visual verification). Full procedure: references/docx-export-to-markdown.md.

Path C — Feishu Minutes (妙记) transcript

lark-cli minutes only returns metadata and can download audio/video — it cannot export the text transcript. The transcript comes from a native endpoint called through lark-cli api, and needs an extra scope granted via a device-flow login. Native AI transcription is far better than downloading the media and re-running ASR — never do the latter. Endpoint, scope name, the device-flow timeout trap, and per-minute (not per-tenant) permission behavior: references/feishu-minutes-transcript.md.

Path D — browser DOM fallback (last resort)

Only when lark-cli genuinely cannot reach the content (no install possible, and the doc is not permission-walled). This is the old virtual-scroll / TOC-driven DOM capture workflow. It is slower, depends on a connected browser surface (the in-browser extension frequently fails to connect), and an anonymous debugging Chrome can only tell you whether a page is publicly reachable — it cannot read login-walled content. Workflow: references/browser-dom-fallback.md. Battle-tested DOM rules (virtual scroll, data-block-id ordering, table/bullet extraction, image streams): references/browser-failure-rules.md.

Hard rules

These are the rules whose violation silently ruins the output. Each has a reason — follow the reason, not just the letter.

  • Never let the document body pass through the model. Extract with jq/cat/scripts straight to disk. The model paraphrasing source text is undetectable later and destroys fidelity. This is why Path A beats the browser path structurally.
  • export LARK_CLI_NO_PROXY=1 for *.feishu.cn. Otherwise credentials transit a local proxy and DNS is hijacked.
  • Transcripts come from the platform's native transcription, never re-ASR. Downloading media and transcribing again loses speaker labels, timestamps, and accuracy.
  • A generated docx Markdown is not done until it has been visually verified against the source (render to image, read it). Feishu-exported docx uses font-size+bold for headings rather than Word heading styles, so a "no errors, word count matches" check passes while the entire heading hierarchy is silently flat. Text-level checks cannot catch this.
  • Do not 死磕 (grind) on docx embedded-image download. lark-cli (through 1.0.32) cannot download <image> tokens from a docx — exhaustively verified. Register the image tokens and note "needs document owner to right-click → save"; the text is the value, images are a tracked gap.
  • HTTP 200 from anonymous curl ≠ accessible. A Feishu login wall returns 200 with a body containing accounts.feishu.cn / login / passport / an empty <title>. Check the body, never infer "public" from the status code.
  • A file "not found" by a search agent is not authoritative. Verify against authoritative sources before concluding (this is general Inference Discipline; relevant when locating where ingested content already lives).
  • U+FFFD final check on every produced file: LC_ALL=C grep -rl $'\xef\xbf\xbd' . must be empty. A replacement character means an encoding step corrupted the text.

Acceptance contract

Stop only when all that apply are true:

  • Every fetched body reached disk via jq/script, not retyped by the model.
  • Collections: the residual rich-media-tag grep (Path A step 5) is empty — every mention-doc/sheet/cross-tenant reference was followed to a leaf.
  • LC_ALL=C grep -rl $'\xef\xbf\xbd' . is empty.
  • docx path: rendered to an image and visually compared to the source; heading hierarchy and highlights match (see docx reference's checklist).
  • Browser fallback only: TOC coverage + scale check (see browser-failure-rules.md).
  • Each output file's frontmatter records source (the original URL/token) and, if any post-processing was applied, a post_process provenance line.
  • Permission gaps (131006 docs not exported yet, undownloadable images) are explicitly listed for the user — a transparent gap beats a silent omission.

Do NOT attempt

Verified dead-ends — retrying them only wastes the session. Full table with failure modes and root causes: references/permission-and-failure-boundaries.md. The top ones:

  • Bypassing 131006 permission-denied by any means (lark-cli / curl / anonymous browser) — it is a server-side boundary.
  • Downloading docx embedded images via docs +media-download, api …/drive/v1/medias/<t>/download (with or without extra), or schema drive.medias.download — none work; lark-cli even mis-reports the real HTTP 400 as "empty JSON".
  • WebFetch against open.feishu.cn/document/server-docs/... for API specs — backend is flaky; use open.feishu.cn/llms-docs/zh-CN/llms-<module>.txt instead (LLM-friendly, stable).
  • AppleScript/JXA executeJavaScript, Chrome CDP on port 9222 — disabled/empty in this environment (browser path only).
  • Using minimax-docx to convert docx→md — it is a docx authoring tool; use the doc-to-markdown skill instead.

Bundled resources

  • scripts/feishu_extract_refs.py — deterministic reference-token extractor; the recursion engine's core. Run it on every fetched body to enumerate <mention-doc>/<sheet>/<image>/cross-tenant/Minutes/Tencent-Meeting references as JSON.
  • scripts/restore_docx_headings.py — for Path B: reads true font sizes via python-docx, maps them to heading levels, restores w:shd highlights to Obsidian ==…==, without retyping body text.
  • scripts/feishu_dom_capture.js — Path D: injectable end-to-end browser DOM capture.
  • scripts/download_feishu_images.py — Path D: SSR image extraction when browser automation is unavailable.
  • scripts/build_feishu_markdown.py — Path D: render a capture manifest into Markdown.
  • scripts/check_heading_coverage.py — coverage verification (both paths).
  • references/lark-cli-api-extraction.md — Path A full reference (commands, recursion, sheets, cross-tenant).
  • references/feishu-minutes-transcript.md — Path C native transcript API + scope auth.
  • references/permission-and-failure-boundaries.md — error codes + the full Do-NOT-attempt table.
  • references/docx-export-to-markdown.md — Path B faithful conversion procedure.
  • references/browser-dom-fallback.md + references/browser-failure-rules.md — Path D.
  • references/capture-manifest.md — manifest shape for build_feishu_markdown.py.

Next step

After extraction completes, the clean Markdown typically feeds the user's own knowledge-base ingestion (filing, indexing, dedup) — which is deliberately out of this skill's scope. If the source went through Path B (a docx), the doc-to-markdown skill is already part of that flow. Offer the handoff; do not auto-organize:

Extraction complete: [N] sources → faithful Markdown ([M] permission/image gaps listed).

Options:
A) Hand off to your PKM/organizing workflow — file & index these (Recommended if part of a vault)
B) Run /daymade-docs:docs-cleaner — consolidate redundant content across the extracted files
C) Stop here — the faithful Markdown is the deliverable

Frequently asked questions about Feishu Doc Scraper

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