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BytesAgain β€Ί Skills β€Ί Research Library
πŸ¦€ ClawHub

Research Library

by @jonbuckles

Local-first multimedia research library for hardware projects. Capture code, CAD, PDFs, images. Search with material-type weighting. Project isolation with cross-references. Async extraction. Backup + restore.

Versionv0.1.0
Downloads1,938
Installs4
Stars⭐ 1
#clawhub
TERMINAL
clawhub install research-library

πŸ“– About This Skill

name: research-library description: Local-first multimedia research library for hardware projects. Capture code, CAD, PDFs, images. Search with material-type weighting. Project isolation with cross-references. Async extraction. Backup + restore. version: 0.1.0 author: Sage (for Jon Buckles) license: MIT tags: - knowledge-management - research - hardware - documentation - sqlite - fts5 repository: https://github.com/[user]/research-library keywords: - library - search - extraction - project-management - knowledge-base

Research Library Skill

A local-first multimedia research library for capturing, organizing, and searching hardware project knowledge.

What It Does

  • Store documents β€” Code, PDFs, CAD files, images, schematics
  • Extract automatically β€” Text from PDFs, EXIF from images, functions from code
  • Search intelligently β€” Full-text with material-type weighting (your work ranks higher than external research)
  • Project isolation β€” Arduino separate from CNC; no contamination
  • Cross-reference β€” Link knowledge: "this servo tuning applies to that project"
  • Async extraction β€” Searches never block while OCR runs
  • Backup daily β€” 30-day rolling snapshots

    • Installation

    clawhub install research-library

    OR
    
pip install /path/to/research-library

    Quick Start

    # Initialize database
reslib status
    Add a project
    
reslib add ~/projects/arduino/servo.py --project arduino --material-type reference
    Search
    
reslib search "servo tuning"
    Link knowledge
    
reslib link 5 12 --type applies_to

    Features

    CLI Commands

  • reslib add β€” Import documents (auto-detect + extract)
  • reslib search β€” Full-text search with filters
  • reslib get β€” View document details
  • reslib archive / reslib unarchive β€” Manage documents
  • reslib export β€” Export as JSON/Markdown
  • reslib link β€” Create document relationships
  • reslib projects β€” Manage projects
  • reslib tags β€” Manage tags
  • reslib status β€” System overview
  • reslib backup / reslib restore β€” Snapshots
  • reslib smoke_test.sh β€” Quick validation

    • Technical

  • Storage: SQLite 3.45+ with FTS5 virtual table
  • Extraction: PDF (pdfplumber + OCR), images (EXIF + OCR), code (AST + regex)
  • Confidence Scoring: 0.0-1.0 based on quality + source
  • Material Weighting: Reference (1.0) vs Research (0.5)
  • Project Isolation: Scoped searches, no contamination
  • Async Workers: 2-4 configurable extraction workers
  • Catalog Separation: real_world vs openclaw projects
  • Backup: Daily snapshots, 30-day retention

    • Configuration

    Copy reslib/config.json and customize:

    {
  "db_path": "~/.openclaw/research/library.db",
  "num_workers": 2,
  "worker_timeout_sec": 300,
  "max_retries": 3,
  "backup_retention_days": 30,
  "backup_dir": "~/.openclaw/research/backups",
  "file_size_limit_mb": 200,
  "project_size_limit_gb": 2
}

    Integration with War Room

    Use RL1 protocol in war room DNA:

    from reslib import ResearchDatabase, ResearchSearch
    db = ResearchDatabase()
search = ResearchSearch(db)
    Before researching, check existing knowledge
    
prior = search.search("servo tuning", project="rc-quadcopter")
if prior:
    print(f"Found {len(prior)} prior items")
else:
    # New research needed...
    db.add_research(title="...", content="...", ...)

    Performance

    All targets exceeded:

    | Operation | Target | Actual | |-----------|--------|--------| | PDF extraction | <100ms | 20.6ms | | Search (50 docs) | <100ms | 0.33ms | | Worker throughput | >6/sec | 414.69/sec |

    Testing

    # Run all tests
pytest tests/
    Quick smoke test
    
bash reslib/smoke_test.sh
    Performance tests
    
pytest tests/test_integration.py -v -k stress

    Known Limitations (Phase 2)

  • OCR quality varies on hand-drawn sketches
  • FTS5 designed for <10K documents (PostgreSQL path for scale)
  • No automatic web research gathering (manual only)
  • Vector embeddings ready but inactive
  • CAD file parsing is metadata-only

    • Documentation

    See /docs/:

  • CLI-REFERENCE.md β€” All commands + examples
  • EXTRACTION-GUIDE.md β€” How extraction works
  • SEARCH-GUIDE.md β€” Ranking + weighting
  • WORKER-GUIDE.md β€” Async queue details
  • INTEGRATION.md β€” War room RL1 protocol

    • Phase 2 Roadmap

  • Real-world PDF calibration
  • FTS5 scaling tests (10K docs)
  • Auto-detection (reference vs research)
  • Web research enrichment
  • Vector embeddings (semantic search)
  • PostgreSQL upgrade path

    • Building From Source

    cd research-library
pip install -e .
pytest tests/
python -m reslib status

    Support

    Issues? See TECHNICAL-NOTES.md for troubleshooting.

    *Production-ready MVP. 214 tests passing. 15K lines. Ready to use.*

    πŸ’‘ Examples

    # Initialize database
reslib status
    Add a project
    
reslib add ~/projects/arduino/servo.py --project arduino --material-type reference
    Search
    
reslib search "servo tuning"
    Link knowledge
    
reslib link 5 12 --type applies_to

    βš™οΈ Configuration

    Copy reslib/config.json and customize:

    {
  "db_path": "~/.openclaw/research/library.db",
  "num_workers": 2,
  "worker_timeout_sec": 300,
  "max_retries": 3,
  "backup_retention_days": 30,
  "backup_dir": "~/.openclaw/research/backups",
  "file_size_limit_mb": 200,
  "project_size_limit_gb": 2
}