What this skill does

--- 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 ```bash note_processor.py summarize <topic> note_processor.py keywords <topic> note_processor.py extract <topic> <keyword> note_processor.py list ``` **Examples:** ```bash # 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 ```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 ``` ## Command Details ### summarize <topic> 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 <topic> 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 <topic> <keyword> 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 → analyze → write report ### With task-runner ```bash # 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 ```bash # 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: - ✅ Python 3 (included) - ✅ No API keys - ✅ No external dependencies - ✅ No paid services - ✅ Works with research-assistant (free) Perfect for autonomous research workflows with no additional costs.