--- 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 <topic>
note_processor.py keywords <topic>
note_processor.py extract <topic> <keyword>
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:

📊 Summary: income-experiments
------------------------------------------------------------
Notes: 4
Words: 63
Created: 2026-02-07
Last update: 2026-02-07

🏷️  Top Tags:
   content: 2
   automation: 2
   experiment: 2

💡 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:

🔤 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:

🔍 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 **SKILL**s to ClawHub...

list

Shows:

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

Output example:

📚 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

...