🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί Note Processor
πŸ¦€ ClawHub

Note Processor

by @johstracke

Summarize, extract keywords, search, and list research notes from research-assistant's database to review progress and find insights efficiently.

Versionv1.0.0
Downloads1,691
Installs3
Stars⭐ 2
#research#legal#communication
TERMINAL
clawhub install note-processor

πŸ“– About This Skill

name: note-processor description: Summarize and analyze research notes created by research-assistant. Features: generate summaries, extract keywords, search within topics, list all topics. Works with research_db.json format. Perfect for finding patterns, reviewing research progress, and extracting insights from accumulated notes without re-reading everything.

Note Processor

Analyze and summarize research notes to extract insights quickly.

Quick Start

note_processor.py summarize 
note_processor.py keywords 
note_processor.py extract  
note_processor.py list

Examples: 

# Get a summary of a research topic
note_processor.py summarize income-experiments
Extract top keywords from notes

note_processor.py keywords security-incident
Search for specific information

note_processor.py extract income-experiments skill
List all research topics with stats

note_processor.py list

Features

  • Summaries - Overview of topic with statistics, tags, key points
  • Keywords - Extract most common words (filters stop words)
  • Search - Find notes containing specific keywords
  • List - See all research topics with basic stats
  • Integration - Works with research-assistant's database format

    • When to Use

    After Research Sessions

    # Summarize what you learned
note_processor.py summarize new-research-topic
    Extract key themes
    
note_processor.py keywords new-research-topic

    Before Writing Reports

    # Find specific information
note_processor.py extract income-experiments monetization
    Get overview for introductions
    
note_processor.py summarize income-experiments

    Reviewing Progress

    # See all topics and their sizes
note_processor.py list
    Check what you've been working on
    
note_processor.py keywords income-experiments

    Command Details

    summarize

    Shows:
  • Note count and word count
  • Creation and last update dates
  • Top 5 tags
  • Key points (sentences with important words)
  • 3 most recent notes

    • Output example: 

    πŸ“Š Summary: income-experiments
------------------------------------------------------------
Notes: 4
Words: 63
Created: 2026-02-07
Last update: 2026-02-07
    🏷️  Top Tags:
   content: 2
   automation: 2
   experiment: 2
    πŸ’‘ Key Points:
   1. First experiment: create and publish skills...
   2. Second experiment: content automation pipeline...

    keywords

    Shows:
  • Total unique keywords
  • Top 20 keywords with frequency
  • Filters common stop words (that, this, with, from, etc.)

    • Output example: 

    πŸ”€ Keywords: income-experiments
------------------------------------------------------------
Total unique keywords: 38
    Top 20 Keywords:
  1. experiment           ( 4x)
  2. skill                ( 3x)
  3. clawhub              ( 2x)
  4. content              ( 2x)

    extract

    Shows:
  • All notes containing the keyword
  • Keyword highlighted in uppercase
  • Timestamps and tags
  • Preview of matched content

    • Output example: 

    πŸ” Search Results: 'skill' in income-experiments
------------------------------------------------------------
Found 4 match(es)
    1. [2026-02-07 19:09:51]
   Tags: ideas, autonomous
   First experiment: create and publish SKILLs to ClawHub...

    list

    Shows:
  • All research topics
  • Note count and word count
  • Last update date
  • Preview of most recent note

    • Output example: 

    πŸ“š Research Topics (5)
------------------------------------------------------------
    income-experiments
   Notes: 4 | Words: 63 | Updated: 2026-02-07
   Latest: Experiment 2 STARTING: Content automation...
    security-incident
   Notes: 1 | Words: 45 | Updated: 2026-02-07
   Latest: Day 1: Security vulnerability found...

    Integration with research-assistant

    note-processor works with the same database as research-assistant (research_db.json).

    Typical Workflow

    # 1. Add research notes
research_organizer.py add "new-topic" "Research finding here" "tag1" "tag2"
    2. Add more notes over time
    
research_organizer.py add "new-topic" "Another finding" "tag3"
    3. Summarize when done
    
note_processor.py summarize new-topic
    4. Find specific information
    
note_processor.py extract new-topic keyword
    5. See all topics
    
note_processor.py list

    Using Both Together

    # Research phase
research_organizer.py add "experiment" "Test result 1" "testing"
research_organizer.py add "experiment" "Test result 2" "testing"
research_organizer.py add "experiment" "Conclusion: worked!" "results"
    Analysis phase
    
note_processor.py summarize experiment
note_processor.py keywords experiment
    Writing phase
    
note_processor.py extract experiment conclusion

    Now write report based on extracted notes

    Key Point Detection

    The summarize command detects key points by finding sentences with important words:

  • important, key, critical, essential
  • must, should, note, remember
  • warning, priority, critical

    • This helps surface actionable insights from your research.

    Keyword Extraction

    The keywords command:

  • Filters words shorter than 4 characters
  • Removes common stop words
  • Counts frequency across all notes
  • Shows top 20 keywords

    • Stop words filtered: that, this, with, from, have, been, will, what, when, where, which, their, there, would, could, should, about, these, those, other, into, through

    Use Cases

    Before Writing a Report

    # Get overview
note_processor.py summarize research-topic
    Find specific data points
    
note_processor.py extract research-topic metrics
    Extract themes
    
note_processor.py keywords research-topic

    Reviewing Research Progress

    # See what you've been working on
note_processor.py list
    Check a specific topic's progress
    
note_processor.py summarize current-project
    Find patterns
    
note_processor.py keywords current-project

    Finding Specific Information

    # Search across a topic
note_processor.py extract income-experiments monetization
    Find references to specific tools
    
note_processor.py extract security-incident path-validation
    Locate conclusions
    
note_processor.py extract experiment conclusion

    Best Practices

    1. Use summaries - Get overview before diving into details 2. Search first - Use extract before reading all notes 3. Check keywords - Find themes you might have missed 4. List regularly - Review all topics to see gaps 5. Tag consistently - Makes keywords more meaningful

    Data Location

    Database: ~/.openclaw/workspace/research_db.json Format: Compatible with research-assistant skill

    Limitations

  • Simple keyword extraction - Frequency-based, not semantic
  • No NLP - Basic text processing (no ML/AI)
  • Stop word list - English-focused, customize for other languages
  • Key point detection - Pattern-based, not understanding-based

    • Tips

    For Better Keywords

  • Use consistent terminology in your notes
  • Avoid abbreviations or synonyms for the same concept
  • Tag notes with important terms
  • Review keywords to see if important terms appear

    • For Better Summaries

  • Write complete sentences in notes
  • Include important words (key, critical, must, etc.)
  • Tag notes with themes
  • Regularly summarize to track progress

    • For Better Search

  • Use specific keywords in extract
  • Search for related terms (synonyms)
  • Check tags in results
  • Use summaries to find the right topic

    • Troubleshooting

    "Topic not found"

    Topic 'x' not found.
    Solution: Check topic name spelling. Use note_processor.py list to see all topics.

    "No matches found"

    No matches for 'keyword' in topic 'x'
    Solution: Try different keywords, check spelling, use note_processor.py keywords to find related terms.

    Poor keyword results

    Top Keywords are mostly common words
    Solution:
  • Use more specific terms in your notes
  • Tag notes with important terms
  • The stop word filter can be customized in the code

    • Examples by Use Case

    Project Review

    # What have I been working on?
note_processor.py list
    Tell me about this project
    
note_processor.py summarize project-x
    What are the main themes?
    
note_processor.py keywords project-x

    Writing Documentation

    # Find specific details
note_processor.py extract security-incident vulnerability
    Get overview for introduction
    
note_processor.py summarize security-incident
    What's important?
    
note_processor.py keywords security-incident

    Preparing a Report

    # Find all relevant information
note_processor.py extract income-experiments monetization
    Get summary
    
note_processor.py summarize income-experiments
    Extract key points
    
note_processor.py summarize income-experiments

    Key points are in the output

    Integration with Other Skills

    With research-assistant

  • research-assistant: add notes
  • note-processor: analyze notes
  • Use together: add β†’ analyze β†’ write report

    • With task-runner

    # Add task to summarize research
task_runner.py add "Summarize experiment results" "documentation"
    When complete
    
note_processor.py summarize experiment
    Mark done
    
task_runner.py complete 1

    With file skills

    # Extract research notes
note_processor.py extract research-topic important
    Export for sharing
    
research_organizer.py export research-topic ~/shared/summary.md
    Or export summary output to file
    
note_processor.py summarize research-topic > ~/shared/summary.txt

    Zero-Cost Advantage

    This skill requires:

  • βœ… Python 3 (included)
  • βœ… No API keys
  • βœ… No external dependencies
  • βœ… No paid services
  • βœ… Works with research-assistant (free)

    • Perfect for autonomous research workflows with no additional costs.

    ⚑ When to Use

    TriggerAction
    ```bash
    # Summarize what you learned
    note_processor.py summarize new-research-topic
    # Extract key themes
    note_processor.py keywords new-research-topic
    ```
    ### Before Writing Reports
    ```bash
    # Find specific information
    note_processor.py extract income-experiments monetization
    # Get overview for introductions
    note_processor.py summarize income-experiments
    ```
    ### Reviewing Progress
    ```bash
    # See all topics and their sizes
    note_processor.py list
    # Check what you've been working on
    note_processor.py keywords income-experiments
    ```

    πŸ’‘ Examples

    note_processor.py summarize 
note_processor.py keywords 
note_processor.py extract  
note_processor.py list

    Examples: 

    # Get a summary of a research topic
note_processor.py summarize income-experiments
    Extract top keywords from notes
    
note_processor.py keywords security-incident
    Search for specific information
    
note_processor.py extract income-experiments skill
    List all research topics with stats
    
note_processor.py list

    πŸ“‹ Tips & Best Practices

    For Better Keywords

  • Use consistent terminology in your notes
  • Avoid abbreviations or synonyms for the same concept
  • Tag notes with important terms
  • Review keywords to see if important terms appear

    • For Better Summaries

  • Write complete sentences in notes
  • Include important words (key, critical, must, etc.)
  • Tag notes with themes
  • Regularly summarize to track progress

    • For Better Search

  • Use specific keywords in extract
  • Search for related terms (synonyms)
  • Check tags in results
  • Use summaries to find the right topic