Back to Skills

index-manager

RobThePCGuy
Updated Today
32 views
2
2
View on GitHub
Metaaidesign

About

The index-manager skill handles the complete lifecycle of MPEP search indexes, including downloading PDFs, extracting content, generating embeddings, and building FAISS/BM25 indexes. It provides automated tools for maintenance, optimization, and troubleshooting when rebuilding indexes or addressing corruption issues. Developers should use it for initial index creation, adding new content, or when diagnostic checks indicate index problems.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/RobThePCGuy/Claude-Patent-Creator
Git CloneAlternative
git clone https://github.com/RobThePCGuy/Claude-Patent-Creator.git ~/.claude/skills/index-manager

Copy and paste this command in Claude Code to install this skill

Documentation

Index Manager Skill

Expert system for managing MPEP search index lifecycle: PDF downloads, index building, maintenance, updates, optimization.

FOR CLAUDE: All dependencies installed, system operational.

  • Go directly to appropriate phase
  • Scripts/tools in mcp_server/
  • Use patent-creator CLI when available
  • Only run diagnostics if operations fail

When to Use

Building/rebuilding MPEP index, corruption/missing files, optimization, adding content, troubleshooting.

Index Lifecycle

PDFs Not Present -> Download (2-5 min, 500MB)
  -> Extract & Parse (500MB data)
  -> Generate Embeddings (5-10 min GPU, 35-65 min CPU)
  -> Build FAISS + BM25 Indexes
  -> Index Ready (mcp_server/index/)
  -> Maintenance (Verify -> Optimize -> Update)

Phase 1: PDF Management

Check Status:

ls pdfs/  # Should show mpep-*.pdf, consolidated_laws.pdf, consolidated_rules.pdf

Download PDFs:

patent-creator download-mpep
# Or: python install.py (Select "Download MPEP PDFs")

Verify Integrity:

python -c "
import fitz
from pathlib import Path
for pdf in Path('pdfs').glob('*.pdf'):
    try:
        doc = fitz.open(pdf)
        print(f'[OK] {pdf.name}: {len(doc)} pages')
        doc.close()
    except Exception as e:
        print(f'[X] {pdf.name}: ERROR - {e}')
"

Phase 2: Index Building

patent-creator rebuild-index
# Or: python mcp_server/server.py --rebuild-index

Timeline:

  • Load PDFs: 30s
  • Extract text: 1-2 min
  • Chunk text (500 tokens): 30s
  • Generate embeddings: 5-10 min (GPU) or 35-65 min (CPU)
  • Build FAISS/BM25: 1 min
  • Save to disk: 10s

Total: 5-15 min (GPU) or 35-65 min (CPU)

Custom Build:

from mcp_server.mpep_search import MPEPIndex
index = MPEPIndex(use_hyde=False)
index.build_index(
    chunk_size=500,
    overlap=50,
    batch_size=32  # Reduce to 16/8 if OOM
)

Phase 3: Verification

# Check files
ls -lh mcp_server/index/
# Expected: mpep_index.faiss (~150MB), mpep_metadata.json (~80MB), mpep_bm25.pkl (~60MB)

# Verify health
patent-creator health
# Should show: [OK] MPEP Index: Ready (12,543 chunks)

# Manual test
python -c "
from mcp_server.mpep_search import MPEPIndex
index = MPEPIndex()
print(f'Chunks: {len(index.chunks)}')
results = index.search('claim definiteness', top_k=3)
print(f'Search results: {len(results)}')
"

Phase 4: Maintenance

When to Rebuild:

  • MPEP updates (quarterly check uspto.gov)
  • Index corruption
  • After adding new PDFs
  • Performance degradation
  • Machine migration

Rebuild Process:

# Backup (optional)
cp -r mcp_server/index mcp_server/index_backup_$(date +%Y%m%d)

# Rebuild
patent-creator rebuild-index

# Verify
patent-creator health

# Remove backup if successful
rm -rf mcp_server/index_backup_*

Phase 5: Content Updates

# Download new PDF
wget https://www.uspto.gov/web/offices/pac/mpep/mpep-2900.pdf -O pdfs/mpep-2900.pdf

# Rebuild (includes new section)
patent-creator rebuild-index

Note: Incremental updates not supported. Full rebuild required.

Troubleshooting

  • OOM errors during build
  • Build taking too long
  • Corrupted index files
  • Search returning no results

Performance Tuning

  • Embedding generation speed (GPU vs CPU)
  • Search latency optimization
  • Index size reduction
  • Batch size tuning

Quick Reference

CommandPurpose
patent-creator download-mpepDownload MPEP PDFs
patent-creator rebuild-indexBuild/rebuild search index
patent-creator healthCheck index health
ls -lh mcp_server/index/View index files

Best Practices:

  1. Backup before rebuild
  2. Verify PDFs before building
  3. Use GPU for 10x faster builds
  4. Test after rebuild
  5. Keep PDFs until verified
  6. Weekly health checks

GitHub Repository

RobThePCGuy/Claude-Patent-Creator
Path: skills/index-manager
bigqueryclaude-codeclaude-code-pluginfaissmcp-servermpep

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill