Link Library

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.

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/:

text

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

bash

# 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

bash

# 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)

yaml

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)

markdown

# {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

<slugified-title>-<YYYY-MM-DD>.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

python

# 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