Automatically fetch, analyze, and convert your X/Twitter bookmarks into an actionable digest of tools, repos, and project ideas.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install x-bookmarks-digest
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install x-bookmarks-digest using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is X Bookmarks Digest?
X Bookmarks Digest is a sophisticated integration for Openclaw Skills designed to solve the 'bookmark graveyard' problem. By leveraging xurl for data retrieval and Python-based analysis scripts, it systematically reviews your saved posts on X/Twitter to identify high-value content such as GitHub repositories, new software tools, and emerging project ideas.
The skill goes beyond simple curation by categorizing bookmarks based on relevance and technical utility. It provides a structured output that suggests immediate next steps, such as installing a discovered tool via homebrew or scaffolding a new skill within your local development environment, ensuring that your bookmarks lead to tangible productivity.
X Bookmarks Digest Use Cases
- Periodic cleaning and review of accumulated X/Twitter bookmarks to find forgotten tools.
- Automated discovery of new GitHub repositories and open-source projects for developer research.
- Generating a weekly digest of technical threads and tutorials saved for later study.
- Identifying content suitable for conversion into new Openclaw Skills or local knowledge base entries.
How X Bookmarks Digest Works
- The skill checks a local state file to enforce rate limits and ensure optimal API usage.
- It utilizes xurl to securely fetch the latest batch of bookmarks from the user's X/Twitter account.
- A Python analysis engine processes the bookmark text to extract URLs, identify GitHub repos, and assign relevance scores.
- The system generates a structured Markdown digest categorized by value (High, Medium, Low).
- Proposes specific actions for high-value items, such as cloning repositories or saving articles to a PKM tool.
- Updates the local state watermark to ensure subsequent runs only process new content.
X Bookmarks Digest Setup
To get started with this entry in the Openclaw Skills library, ensure you have the required dependencies and authenticated access:
# Install the required CLI tool for X/Twitter access
brew install xurl
# Verify your authentication status
xurl whoami
# If unauthorized, add your application credentials
xurl auth apps add <app-name> --client-id <id> --client-secret <secret>
xurl auth default <app-name>
Once configured, you can trigger the workflow by simply saying "digest x bookmarks" to your AI agent.
X Bookmarks Digest Data Schema & Taxonomy
The skill manages its operation through a centralized state file and produces structured JSON analysis.
State Management
Located at {baseDir}/state.json, this file tracks:
| Key | Description |
|---|---|
| last_bookmark_id | The ID of the most recent bookmark processed for incremental updates. |
| last_run_ts | Timestamp of the last execution to manage rate limits. |
| processed_count | A running total of all bookmarks analyzed by the skill. |
Analysis Output
The analyse_bookmarks.py script generates objects containing the original text, author, extracted URLs, identified GitHub repositories, and a relevance score (1-5).
X Bookmarks Digest Advanced Features
- Intelligent rate-limit enforcement to protect X API tier limits with override capabilities.
- Automated GitHub repository detection and suggested integration with git or brew.
- Incremental fetching logic that only processes bookmarks saved since the last successful run.
- Native compatibility with Openclaw Skills scaffolding to turn bookmarked ideas into functional code immediately.
SKILL.md
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