Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Note Processor
Summarize, extract keywords, search, and list research notes from research-assistant's database to review progress and find insights efficiently.
skill openclawclawhub Free
Summarize, extract keywords, search, and list research notes from research-assistant's database to review progress and find insights efficiently.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/note_processor.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- johstracke
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
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
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: ๐ Summary: income-experiments ------------------------------------------------------------ Notes: 4 Words: 63 Created: 2026-02-07 Last update: 2026-02-07 ๐ท๏ธ Top Tags: content: 2 automation: 2 experiment: 2 ๐ก 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: ๐ค 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: ๐ 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: ๐ 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
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
Use summaries - Get overview before diving into details Search first - Use extract before reading all notes Check keywords - Find themes you might have missed List regularly - Review all topics to see gaps 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
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
"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
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
With research-assistant
research-assistant: add notes note-processor: analyze notes Use together: add โ analyze โ 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: โ Python 3 (included) โ No API keys โ No external dependencies โ No paid services โ Works with research-assistant (free) Perfect for autonomous research workflows with no additional costs.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/note_processor.py