document-rag-pipeline-complete-pipeline-script
Sub-skill of document-rag-pipeline: Complete Pipeline Script.
Best use case
document-rag-pipeline-complete-pipeline-script is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of document-rag-pipeline: Complete Pipeline Script.
Teams using document-rag-pipeline-complete-pipeline-script should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/complete-pipeline-script/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How document-rag-pipeline-complete-pipeline-script Compares
| Feature / Agent | document-rag-pipeline-complete-pipeline-script | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of document-rag-pipeline: Complete Pipeline Script.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Complete Pipeline Script
## Complete Pipeline Script
```python
#!/usr/bin/env python3
"""
Document RAG Pipeline - Build searchable knowledge base from PDF folder.
Usage:
python build_knowledge_base.py /path/to/documents --db inventory.db
python build_knowledge_base.py /path/to/documents --search "query text"
"""
import argparse
import os
from pathlib import Path
from tqdm import tqdm
def build_inventory(folder_path, db_path):
"""Build document inventory from folder."""
conn = create_database(db_path)
cursor = conn.cursor()
pdf_files = list(Path(folder_path).rglob("*.pdf"))
print(f"Found {len(pdf_files)} PDF files")
for pdf_path in tqdm(pdf_files, desc="Building inventory"):
# Check if already processed
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