Link Library

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name: link-library version: "1.0.0" description: > Personal knowledge base that captures web content (articles, tweets/threads, videos, podcasts, images, PDFs) and makes it retrievable for future conversations and writing. Use when: (1) User shares a URL with ANY interest signal — asking to summarize, commenting positively, saying "有意思/不错/interesting/值得看/学到了", or requesting it be saved, (2) User asks to find previously saved content ("我之前存的那篇...", "find that article about..."), (3) User needs reference material for writing or discussion, (4) User shares Twitter/X, WeChat, YouTube, Bilibili, or any web link and engages with it. Interest signals that trigger save: "帮我总结一下", "这篇不错", "有意思", "记一下", "留着以后用", "这个观点很好", "学到了", "值得保存", "放进知识库", sharing link + any commentary or opinion, asking follow-up questions about link content. Do NOT require literal "save"/"bookmark" keywords.

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Link Library — Personal Content Knowledge Base

Save web content with full original text, generate summaries and tags, retrieve semantically.

Core Rules

1. Always save original full text — summaries are for retrieval, originals are for re-reading 2. Detect interest, don't demand commands — if user engages with a link, offer to save 3. Twitter/X is first-class — tweets, threads, and articles are fully supported

Interest Detection

When user shares a link, evaluate interest signals:

Auto-save (no confirmation needed):

User explicitly says save/bookmark/记一下/放进知识库

User asks "帮我总结一下" (summarize implies save-worthy)

Offer to save (ask once):

User shares link + positive commentary ("这篇不错", "有意思", "学到了")

User asks follow-up questions about link content

User discusses link content substantively

Don't save:

User shares link just for quick reference in conversation

User says "不用保存" or similar

Data Location

All entries in ~/.openclaw/workspace-main/library/:

library/
├── articles/     # Web articles, blog posts, WeChat, Zhihu
├── tweets/       # Twitter/X posts and threads
├── videos/       # YouTube, Bilibili
├── podcasts/     # Podcast episodes
├── papers/       # Academic papers, PDFs
├── images/       # Infographics, visual content
└── misc/         # Everything else

Content Types & Fetch Methods

| Type | URL Patterns | Fetch Method | Template | |------|-------------|--------------|----------| | article | Generic web, blog, /post/ | web_fetch or curl -s "https://r.jina.ai/URL" | article.md | | wechat | mp.weixin.qq.com | cd ~/.agent-reach/tools/wechat-article-for-ai && python3 main.py "URL" | article.md | | tweet | x.com, twitter.com /status/ | xreach tweet URL --json | tweet.md | | thread | x.com, twitter.com (thread) | xreach thread URL --json | tweet.md | | video | youtube.com, youtu.be | yt-dlp --dump-json "URL" + subtitle extraction | video.md | | bilibili | bilibili.com | yt-dlp --dump-json "URL" + subtitle extraction | video.md | | paper | arxiv.org, .pdf links | web_fetch or browser | paper.md | | podcast | Podcast platforms | web_fetch metadata | podcast.md | | image | Image URLs | Download + describe | image.md |

Twitter/X Fetch Details

# Single tweet
xreach tweet URL_OR_ID --json
Full thread
xreach thread URL_OR_ID --json
User timeline (for context)
xreach tweets @username -n 20 --json

Extract from JSON: full_text, user.screen_name, created_at, entities, media URLs. For threads: concatenate all tweets in order as full content.

Video Subtitle Extraction

# Download subtitles
yt-dlp --write-sub --write-auto-sub --sub-lang "zh-Hans,zh,en" \
  --convert-subs vtt --skip-download -o "/tmp/%(id)s" "URL"
Then read the .vtt file as transcript

Entry Structure

Every entry has two parts:

1. YAML Frontmatter (structured metadata)

title: "..."
source: "..."           # Platform/domain
url: "..."              # Original URL
author: "..."           # Author or @handle
date_published: "..."   # When content was created
date_saved: "..."       # When we saved it
last_updated: "..."     # Last modification
type: article|tweet|video|podcast|paper|image
tags: [tag1, tag2, ...]
status: unread|read|reviewed
priority: low|normal|high
related: []             # Paths to related entries

2. Markdown Body (content)

# {title}
Summary
2-3 sentence summary.
Key Points
Point 1
Point 2
Original Content
THE FULL ORIGINAL TEXT — not truncated, not summarized.
This is the authoritative source for re-reading and quoting.
Quotes
> Notable quotes worth highlighting
Notes
Personal observations, connections, action items.
Related
[[library/tweets/related-tweet]]
[[library/articles/related-article]]

⚠️ MANDATORY: Always save original full text in "Original Content" section. Summaries and key points are for quick retrieval. The original text is for accurate re-reading and quoting. Never skip saving the full content.

Filename Convention

-.md

Examples:

library/articles/yc-why-not-work-and-startup-2026-03-12.md

library/tweets/garry-tan-on-yc-advice-2026-03-13.md

library/videos/how-to-build-agents-2026-03-13.md

Save Workflow

1. Detect URL — Parse link from user message 2. Identify type — Match URL pattern to content type 3. Check dedup — memory_search("URL or title") to avoid duplicates 4. Fetch content — Use appropriate method from table above 5. Generate metadata — Title, summary, key points, tags (3-7) 6. Write entry — Use template, fill frontmatter + full original text 7. Confirm — Tell user: title, tags, and where it's saved

Search & Retrieval

# Semantic search
memory_search("创业方法论")
memory_search("Garry Tan 的推文")
memory_search("AI agent 视频教程")
Read specific entry
memory_get("library/tweets/garry-tan-on-yc-2026-03-13.md")

When returning search results, show:

Title + source + date

Summary (2 lines max)

Tags

Offer to show full original text

Writing Reference Mode

When user asks to write something using saved content:

1. Search library for relevant entries 2. Read full original text of top matches 3. Synthesize insights, cite sources inline 4. Format citations as [[library/type/entry-name]]

Templates

Located in templates/:

article.md — Web articles, blog posts, newsletters

tweet.md — Twitter/X posts and threads

video.md — Videos with transcript

podcast.md — Podcast episodes

paper.md — Academic papers

image.md — Visual content

Best Practices

Save originals religiously — summaries lose nuance

Tag consistently — reuse existing tags, keep vocabulary tight

Link related entries — build a knowledge graph over time

Don't over-ask — if interest is clear, just save and confirm

Save originals religiously — summaries lose nuance

Tag consistently — reuse existing tags, keep vocabulary tight

Link related entries — build a knowledge graph over time

Don't over-ask — if interest is clear, just save and confirm