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X Knowledge Base

自動收集 X 書籤並轉化為 Obsidian 知識庫,配備 AI 濃縮與交叉連結功能,支援自我進化趨勢分析

skill openclawclawhub Free
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自動收集 X 書籤並轉化為 Obsidian 知識庫,配備 AI 濃縮與交叉連結功能,支援自我進化趨勢分析

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Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
README.md, SKILL.md, _meta.json, config/interests.yaml, scripts/fetch_article.sh, scripts/fetch_bookmarks.sh

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.2

Documentation

ClawHub primary doc Primary doc: SKILL.md 19 sections Open source page

X Knowledge Base

自動將 X (Twitter) 書籤轉化為 Obsidian Markdown 格式,建立個人知識庫。支援自我進化,根據書籤傾向動態調整關鍵字和趨勢分析。

基礎功能

抓取書籤:從 X/Twitter 抓書籤內容 完整原文:使用 Jina AI 擷取完整文章內容 AI 濃縮:自動產生一句話摘要、三個重點、應用場景 交叉連結:根據標籤自動建立 wiki-link Obsidian 格式:YAML frontmatter + wiki-link

自我進化功能 ⭐

興趣趨勢追蹤:每次存書籤,自動記錄標籤並統計頻率 動態關鍵字調整:根據趨勢自動調整每日情報關鍵字 新興標籤偵測:發現突然增加的標籤,自動加入追蹤 自適應推薦:根據書籤傾向推薦相關內容

安裝

設定 Twitter API 或 bird CLI 設定 Brave Search API(用於趨勢分析) 設定 Jina AI(用於文章擷取) 設定 MiniMax API(用於 AI 濃縮) 設定 Obsidian vault 路徑

檢查書籤

"檢查我的書籤" - 抓取並儲存新書籤

自我進化

"今天的趨勢是什麼" - 興趣趨勢報告(含動態調整) "我的興趣變化了嗎" - 偵測興趣轉變

AI 濃縮

每次存書籤時,自動: 產生一句話摘要 列出三個重點 寫出應用場景

交叉連結

  • 根據標籤自動建立相關書籤連結:
  • ## 🔗 相關書籤
  • [[書籤標題|標籤]]

檔案結構

x-knowledge-base/ ├── SKILL.md ├── scripts/ │ ├── fetch_bookmarks.sh # 抓書籤 │ ├── fetch_article.sh # Jina AI 抓全文 │ └── save_obsidian.sh # 存 Obsidian ├── tools/ │ ├── bookmark_enhancer.py # AI 濃縮 + 交叉連結 │ └── trend_analyzer.py # 自我進化趨勢分析 └── config/ ├── interests.yaml # 興趣標籤配置 └── trends.json # 趨勢數據(自動產生)

1. 興趣趨勢追蹤

每次存書籤時: 擷取書籤的所有標籤 寫入 trends.json 統計每個標籤的出現頻率 計算趨勢分數

2. 動態關鍵字調整

根據趨勢數據,自動調整每日情報關鍵字: 頻率上升的標籤 → 提高優先級 新出現的標籤 → 加入觀察名單 下降的標籤 → 降低優先級

3. 新興標籤偵測

# 趨勢分數計算 new_score = (current_count - previous_count) / previous_count * 100 if new_score > 50%: # 超過 50% 成長 alert("新興趨勢!")

4. 自適應學習

根據書籤傾向: 自動調整感興趣的主題權重 推薦相似內容 預測可能想看的書籤

Jina AI 擷取

https://r.jina.ai/http://x.com/用戶名/status/ID

MiniMax API

endpoint: https://api.minimax.io/anthropic/v1/messages model: MiniMax-M2.5

趨勢數據格式 (trends.json)

{ "last_updated": "2026-02-18T14:00:00Z", "tags": { "ai": { "count": 15, "trend": "+20%" }, "video": { "count": 8, "trend": "+5%" }, "seo": { "count": 12, "trend": "-10%" } }, "new_emerging": ["vibe-coding", "claude-code"], "recommended_keywords": ["AI video generation", "Claude automation"] }

差異化

功能x-bookmarks我們的 skill內容深度摘要完整原文AI 濃縮無✅ 有儲存格式JSONObsidian Markdown趨勢分析無✅ 有交叉連結無✅ 有自我進化無✅ ⭐ 有

依賴

Python 3.10+ requests Jina AI(免費) MiniMax API Brave Search API(可選)

環境變數(必填/選填)

BIRD_AUTH_TOKEN(必填) BIRD_CT0(必填) MINIMAX_API_KEY(選填,不填則略過 AI 濃縮) MINIMAX_ENDPOINT(選填,預設 https://api.minimax.io/anthropic/v1/messages) MINIMAX_MODEL(選填,預設 MiniMax-M2.5)

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
2 Docs2 Scripts2 Config
  • SKILL.md Primary doc
  • README.md Docs
  • scripts/fetch_article.sh Scripts
  • scripts/fetch_bookmarks.sh Scripts
  • _meta.json Config
  • config/interests.yaml Config