# pyragify

> Converts code repositories and document directories into semantically-chunked text files optimized for NotebookLM ingestion, with support for config files and incremental processing.

- Skill: `auto-skiller/pyragify` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add auto-skiller/pyragify`
- Raw SKILL.md: https://api.skillmd.com/api/skills/auto-skiller/pyragify/raw
- Safety review: PASS (external: skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Data & Analytics, ETL & Pipelines, RAG & Embeddings
- Tags: Chunking, Css, Html, Markdown, Notebooklm, Python, Rag, Text Preprocessing
- Author: Auto-Skiller (https://skillmd.com/u/auto-skiller)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/auto-skiller/pyragify

---


# pyragify Skill

## Source
- **Repo**: https://github.com/ThomasBury/pyragify
- **PyPI**: `pip install pyragify`
- **Version**: v0.2.0 (April 2026)
- **License**: Unlicense (fully free)

## What It Does
Converts any code repository or document directory into clean, semantically-chunked `.txt` files optimized for NotebookLM ingestion. Run this BEFORE uploading to NotebookLM.

## Setup
```bash
pip install pyragify
```

## Usage
**CRITICAL:** Always use relative portable paths to execute the OS Engine so the workspace can be ported to new machines without breaking:

```bash
# With a config file (recommended)
.\.venv\Scripts\pyragify.exe --config-file config.yaml

# Or inline
.\.venv\Scripts\pyragify.exe \
  --repo-path /path/to/repository \
  --output-dir /path/to/output \
  --max-words 200000 \
  --skip-dirs "__pycache__" \
  --skip-dirs "node_modules" \
  --verbose
```

## Example `config.yaml`
```yaml
repo_path: c:\Users\BAB AL SAFA\Desktop\open-workspace
output_dir: c:\Users\BAB AL SAFA\Desktop\open-workspace\.core\toolbox_library\core.toolbox\notebooklm\skills\pyragify\output
max_words: 200000
max_file_size: 10485760  # 10 MB
skip_patterns:
  - "*.log"
  - "*.tmp"
skip_dirs:
  - "__pycache__"
  - "node_modules"
  - ".git"
verbose: false
```

## Output Structure
```
output/
  python/      ← Python functions, classes, comments
  markdown/    ← Markdown sections split by headers
  html/        ← HTML script and style chunks
  css/         ← CSS rule chunks
  other/       ← All other readable file types
  remaining/   ← Overflow chunks after word limit
  metadata.json
  hashes.json  ← MD5 hashes for incremental re-runs (only reprocesses changed files)
```

## Supported Inputs
- `.py` — chunked by function/class/comment
- `.md`, `.markdown` — split by header sections
- `.html` — script and style chunks
- `.css` — CSS rule chunks
- All other UTF-8 readable files — included as plain text

## Workflow with notebooklm-py
```bash
# Step 1: Preprocess the workspace
pyragify --config-file config.yaml

# Step 2: Upload output chunks to NotebookLM
notebooklm create "Workspace Analysis"
notebooklm use <notebook_id>
notebooklm source add "./output/markdown/chunk_0.txt"
notebooklm source add "./output/python/chunk_0.txt"

# Step 3: Query and generate
notebooklm ask "What are the architectural gaps in this workspace?"
```