document-indexing
Extract structured metadata from documents using AI. Classify content types, extract topics and tools. Supports async batch processing.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install boringdata-kurt-demo-document-indexing-skill
Repository
Skill path: _archived/document-indexing-skill
Extract structured metadata from documents using AI. Classify content types, extract topics and tools. Supports async batch processing.
Open repositoryBest for
Primary workflow: Write Technical Docs.
Technical facets: Full Stack, Data / AI, Tech Writer.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: boringdata.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install document-indexing into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/boringdata/kurt-demo before adding document-indexing to shared team environments
- Use document-indexing for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: document-indexing
description: Extract structured metadata from documents using AI. Classify content types, extract topics and tools. Supports async batch processing.
---
# Document Indexing
## Overview
Extract structured metadata from fetched documents using LLM:
- **Content type**: blog, tutorial, guide, reference, etc.
- **Topics & Tools**: Main subjects and technologies
- **Structure**: Code examples, procedures, narrative
Creates `DocumentMetadata` records for search and clustering.
## Quick Start
```bash
# Index single document
kurt index 5494cc13
# Batch index (async, 5-10x faster)
kurt index --url-prefix https://example.com/
# Re-index with custom concurrency
kurt index --url-prefix https://example.com/ --force --max-concurrent 10
```
**Prerequisites:** Documents must be FETCHED (`kurct content fetch`)
## Commands
```bash
# Single
kurt index <doc-id>
kurt index <doc-id> --force
# Batch (async parallel)
kurt index --url-prefix <url>
kurt index --url-contains <string>
kurt index --max-concurrent 10 # Default: 5
# Filters
kurt index --status FETCHED --url-prefix <url>
```
## Content Types
`BLOG` | `TUTORIAL` | `GUIDE` | `REFERENCE` | `WHITEPAPER` | `CASE_STUDY` | `FAQ` | `CHANGELOG` | `MARKETING` | `OTHER`
## Extracted Metadata
```json
{
"content_type": "TUTORIAL",
"extracted_title": "Machine Learning Guide",
"primary_topics": ["Machine Learning", "Python"],
"tools_technologies": ["TensorFlow", "Pandas"],
"has_code_examples": true,
"has_step_by_step_procedures": true,
"has_narrative_structure": false
}
```
## Performance
- **Sequential**: ~3-5s per document
- **Parallel (5 concurrent)**: ~1s per document avg
- **Example**: 92 docs in 30s (parallel) vs 5 mins (sequential)
## Python API
```python
from kurt.indexing import extract_document_metadata, batch_extract_document_metadata
import asyncio
# Single
result = extract_document_metadata("abc-123")
# Batch
results = asyncio.run(batch_extract_document_metadata(
["abc-123", "def-456"],
max_concurrent=5
))
```
## Troubleshooting
| Issue | Solution |
|-------|----------|
| "Document not FETCHED" | Run `kurct content fetch <id>` first |
| "Content file not found" | Re-fetch document |
| Slow batch | Increase `--max-concurrent` |
| Rate limits | Reduce `--max-concurrent` |
## Next Steps
- **ingest-content-skill** - Fetch documents first
- **document-management-skill** - Query and manage documents