| name | chubbyskills |
| description | Integrate chubbyguan/chubbyskills as LLM Wiki's local multi-channel ingest pack, turning Chinese feeds, videos, podcasts, articles, and social posts into searchable Obsidian/Markdown knowledge. |
ChubbySkills local ingest pack
Use this skill when the user wants LLM Wiki to behave like a local NotebookLM / NetbookLM over their own feeds and saved content.
LLM Wiki's role:
- Own the vault path, search, citations, graph, memory, review, and promotion workflow.
- Query saved Markdown through
vault.search, query.unified, vault.read, memory.*, and AI-Output review tools.
- Keep governance boundaries: raw source, compiled wiki, agent output, reviewed decisions.
ChubbySkills' role:
- Own platform-specific capture and transcription.
- Convert links from Douyin, Bilibili, Xiaohongshu, WeChat, X/Twitter, podcasts, YouTube, Weibo, Zhihu, TikTok, and related feeds into Markdown assets.
- Use its own optional dependencies for video/audio transcription, subtitles, images, and enrichment.
Source project: https://github.com/chubbyguan/chubbyskills.
Integration model
Do not copy large upstream dependencies into the LLM Wiki MCP server.
Instead:
- Install LLM Wiki normally.
- Install upstream ChubbySkills separately when the user wants multi-channel capture.
- Point both systems at the same local vault:
export VAULT_MIND_VAULT_PATH=/path/to/your/vault
export VAULT_DIR="$VAULT_MIND_VAULT_PATH"
- Let ChubbySkills write captured Markdown into the vault.
- Let LLM Wiki search, cite, enrich, review, and promote the result.
Recommended vault layout
raw/ # LLM Wiki raw compiler sources
素材库/ # ChubbySkills raw captured material
wiki/ # compiled / structured knowledge
00-Inbox/AI-Output/<actor>/ # agent-authored review candidates
00-Inbox/Agent-Memory/<actor>/
20-Decisions/
30-Architecture/
40-Runbooks/
If the user's vault already has a different Chinese folder structure, preserve it. Do not force migration.
Skill routing
| Need | Upstream ChubbySkills skill | LLM Wiki follow-up |
|---|
| Douyin video | douyin-transcribe | query.unified, vault.writeAIOutput |
| Bilibili video | bilibili-transcribe | query.unified, concept compilation |
| YouTube video | youtube-transcribe | bilingual summary, tags, AI-Output |
| Podcast | podcast-transcribe | learning notes, memory handoff |
| WeChat article | wechat-article-ingest | source-backed wiki page |
| Xiaohongshu | xiaohongshu-ingest | image/video routing, hook analysis |
| X/Twitter single tweet | x-ingest | raw Markdown ingest |
| X/Twitter high-signal browser capture | x-to-obsidian | Web Clipper capture, LLM Wiki search |
| Any captured note | content-enrich | summary, tags, value judgment |
| Whole vault management | knowledge-base-management | LLM Wiki doctor, health, MCP query |
Helper script
This skill bundles a tiny helper that prints safe install and environment commands:
python3 scripts/chubbyskills_bridge.py --help
python3 scripts/chubbyskills_bridge.py list
python3 scripts/chubbyskills_bridge.py plan --vault /path/to/vault --skills bilibili-transcribe podcast-transcribe
The helper does not install or clone anything by default. Treat it as a reproducible checklist.
Operating procedure
- Identify the source URL or content channel.
- Pick the upstream skill using the routing table.
- If upstream ChubbySkills is not installed, run the helper
plan command and show the user the commands.
- Run upstream capture only when the local environment is ready.
- Confirm the Markdown note exists under the vault.
- Use LLM Wiki MCP tools to search and summarize the saved note.
- File agent-authored synthesis under
00-Inbox/AI-Output/<actor>/.
- Promote durable conclusions only after review.
Good prompts
/chubbyskills install the Bilibili and podcast ingest path for my LLM Wiki vault
/chubbyskills turn this Bilibili video into a searchable vault note, then summarize it
/chubbyskills capture these WeChat articles into 素材库 and build a wiki index
/chubbyskills make this vault work like a local NotebookLM over my saved feeds
Safety rules
- Respect source platform terms and rate limits.
- Do not bypass login, paywalls, private content, or deleted content.
- Do not hardcode cookies, API keys, or tokens into vault notes.
- Keep heavy transcription dependencies outside LLM Wiki's MCP bundle.
- Prefer source-preserving Markdown over lossy summaries.
- Never claim "local NotebookLM" completeness unless retrieval was tested against the saved notes.
