What is bookmark-processor?

The Twitter Bookmark Processor is a sophisticated Claude skill designed to automate the end-to-end pipeline of fetching, analyzing, and organizing Twitter bookmarks. It leverages intelligent categorization and relevance scoring to transform social media saves into a structured local knowledge base, extracting actionable resources and generating formal skill proposals for professional development and research.

When should I use bookmark-processor?

bookmark-processor is useful in the following scenarios: • Automated Knowledge Discovery: Automatically fetch and categorize Twitter bookmarks to identify high-value AI patterns, developer tools, or business strategies without manual sorting. • Bookmark Management & Cleanup: Maintain a clutter-free Twitter account by archiving saved tweets into a local CSV database and automatically unbookmarking processed items. • Resource Extraction: Automatically scrape and summarize linked content such as blog posts, GitHub repositories, and documentation from bookmarked threads for deeper study. • Skill Implementation Pipeline: Convert technical insights from social media into structured Markdown proposals, including implementation notes and complexity estimates for future projects.

name	bookmark-processor
description	This skill should be used when the user asks to "process bookmarks", "run /process-bookmarks", "analyze my twitter bookmarks", "find skill candidates from bookmarks", or wants to fetch, categorize, and extract insights from Twitter bookmarks for skill discovery.
version	1.0.0

Twitter Bookmark Processor

Automate the bookmark-to-skill discovery pipeline. Fetch, analyze, extract, propose, track, clean.

The Workflow

Fetch Bookmarks → Categorize → Score → Extract Resources → Generate Proposals → Update CSV → Unbookmark

How to Use

Process All Recent Bookmarks

/process-bookmarks

Process with Options

/process-bookmarks --keep-bookmarks    # Don't unbookmark after processing
/process-bookmarks --min-relevance 4   # Only process relevance 4+
/process-bookmarks --category AI-LLM   # Only process specific category

What Gets Processed

Category	Examples
AI-LLM	Claude tips, agent patterns, MCP servers
DevOps	CI/CD, infrastructure, deployment
Marketing	Growth tactics, content strategy
Business	Pricing, sales, startup advice
Design	UI/UX, visual design
Personal	Health, productivity, life advice
Other	Everything else

Relevance Scoring (1-5)

Score	Meaning
5	Definitely a skill - actionable workflow or pattern
4	Likely a skill - useful technique or tool
3	Maybe a skill - interesting but needs exploration
2	Not a skill - just information
1	Not relevant - personal/off-topic

Skill Candidate Criteria

A bookmark becomes a skill candidate when:

Relevance score is 4-5
Contains actionable workflow
Has reusable pattern
Includes tools/techniques
Could benefit multiple projects

Output Files

CSV Tracking File

Location: ~/projects/adam/twitter-bookmarks.csv

Columns:

handle - Twitter username
tweet_text - Tweet content (truncated)
url - Tweet URL
timestamp - When bookmarked
category - Assigned category
relevance - Score 1-5
plugin - Yes/Candidate/blank

Skill Proposals Document

Location: ~/projects/adam/skill-proposals-YYYY-MM.md

Contains:

Summary table of candidates
Detailed skill specifications
Implementation notes
Source attribution

Processing Steps

1. Fetch Bookmarks

Uses Twitter API to get recent bookmarks with:

Tweet text (including long tweets)
Author info
URLs/links
Engagement metrics

2. Categorize

Analyzes tweet content to assign:

Category (AI-LLM, DevOps, etc.)
Relevance score (1-5)
Skill candidate flag

3. Extract Resources

For skill candidates, fetches linked resources:

Blog posts
GitHub repos
Documentation
Videos (extracts descriptions)

4. Generate Proposals

For each skill candidate:

Summarizes core concept
Identifies key features
Notes implementation approach
Estimates complexity

5. Update CSV

Adds new entries
Updates existing entries if re-bookmarked
Maintains chronological order

6. Unbookmark Processed

Removes processed bookmarks from Twitter to:

Keep bookmark list fresh
Prevent reprocessing
Signal "already captured"

Configuration

Set in CLAUDE.md or Memory

## Bookmark Processing Preferences

CSV Location: ~/projects/adam/twitter-bookmarks.csv
Proposals Location: ~/projects/adam/skill-proposals-{date}.md
Auto-unbookmark: true
Min Relevance for Skill: 4
Focus Categories: AI-LLM, DevOps

Example Session

User: /process-bookmarks

Claude:

Fetching bookmarks...
Found 25 new bookmarks since last processing.

Categorizing...
- AI-LLM: 15
- DevOps: 5
- Marketing: 3
- Personal: 2

Scoring relevance...
- Score 5: 8 bookmarks
- Score 4: 6 bookmarks
- Score 3: 7 bookmarks
- Score 2: 3 bookmarks
- Score 1: 1 bookmark

Skill candidates identified: 6

Extracting linked resources...
- Fetched 4 blog posts
- Fetched 2 GitHub repos

Generating skill proposals...
Created: skill-proposals-2026-01.md

Updating CSV...
Added 25 entries (total: 154)

Unbookmarking processed items...
Removed 25 bookmarks from Twitter.

Summary:
- New entries: 25
- Skill candidates: 6
- Resources extracted: 6
- Proposals generated: skill-proposals-2026-01.md

Integration with Other Skills

With skill-creator

When ready to implement a proposed skill:

/create-skill from proposal "Plain English E2E Testing"

With voice-learning

Extract voice patterns from thought leader bookmarks:

/learn-voice from bookmarks by @bcherny

Weekly Automation

Consider scheduling weekly processing:

# In cron or automation
claude -p "/process-bookmarks --quiet" >> ~/logs/bookmark-processing.log

Handling Rate Limits

If Twitter API rate limits hit:

Processing pauses automatically
Resume with /process-bookmarks --continue
Partial progress saved to CSV

Privacy Note

Bookmarks are private Twitter data
Processing happens locally
CSV is local file
Unbookmarking is optional

Credits

Meta-skill developed during Twitter bookmark analysis session on 2026-01-08.

bookmark-processor

When & Why to Use This Skill

Use Cases