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The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
133 lines
4.4 KiB
Markdown
133 lines
4.4 KiB
Markdown
---
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name: ocr-and-documents
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description: Extract text from PDFs and scanned documents. Use web_extract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.
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version: 2.3.0
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author: Hermes Agent
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license: MIT
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metadata:
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hermes:
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tags: [PDF, Documents, Research, Arxiv, Text-Extraction, OCR]
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related_skills: [powerpoint]
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---
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# PDF & Document Extraction
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For DOCX: use `python-docx` (parses actual document structure, far better than OCR).
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For PPTX: see the `powerpoint` skill (uses `python-pptx` with full slide/notes support).
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This skill covers **PDFs and scanned documents**.
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## Step 1: Remote URL Available?
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If the document has a URL, **always try `web_extract` first**:
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```
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web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
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web_extract(urls=["https://example.com/report.pdf"])
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```
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This handles PDF-to-markdown conversion via Firecrawl with no local dependencies.
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Only use local extraction when: the file is local, web_extract fails, or you need batch processing.
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## Step 2: Choose Local Extractor
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| Feature | pymupdf (~25MB) | marker-pdf (~3-5GB) |
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|---------|-----------------|---------------------|
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| **Text-based PDF** | ✅ | ✅ |
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| **Scanned PDF (OCR)** | ❌ | ✅ (90+ languages) |
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| **Tables** | ✅ (basic) | ✅ (high accuracy) |
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| **Equations / LaTeX** | ❌ | ✅ |
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| **Code blocks** | ❌ | ✅ |
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| **Forms** | ❌ | ✅ |
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| **Headers/footers removal** | ❌ | ✅ |
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| **Reading order detection** | ❌ | ✅ |
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| **Images extraction** | ✅ (embedded) | ✅ (with context) |
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| **Images → text (OCR)** | ❌ | ✅ |
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| **EPUB** | ✅ | ✅ |
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| **Markdown output** | ✅ (via pymupdf4llm) | ✅ (native, higher quality) |
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| **Install size** | ~25MB | ~3-5GB (PyTorch + models) |
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| **Speed** | Instant | ~1-14s/page (CPU), ~0.2s/page (GPU) |
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**Decision**: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.
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If the user needs marker capabilities but the system lacks ~5GB free disk:
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> "This document needs OCR/advanced extraction (marker-pdf), which requires ~5GB for PyTorch and models. Your system has [X]GB free. Options: free up space, provide a URL so I can use web_extract, or I can try pymupdf which works for text-based PDFs but not scanned documents or equations."
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---
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## pymupdf (lightweight)
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```bash
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pip install pymupdf pymupdf4llm
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```
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**Via helper script**:
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```bash
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python scripts/extract_pymupdf.py document.pdf # Plain text
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python scripts/extract_pymupdf.py document.pdf --markdown # Markdown
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python scripts/extract_pymupdf.py document.pdf --tables # Tables
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python scripts/extract_pymupdf.py document.pdf --images out/ # Extract images
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python scripts/extract_pymupdf.py document.pdf --metadata # Title, author, pages
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python scripts/extract_pymupdf.py document.pdf --pages 0-4 # Specific pages
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```
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**Inline**:
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```bash
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python3 -c "
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import pymupdf
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doc = pymupdf.open('document.pdf')
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for page in doc:
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print(page.get_text())
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"
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```
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---
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## marker-pdf (high-quality OCR)
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```bash
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# Check disk space first
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python scripts/extract_marker.py --check
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pip install marker-pdf
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```
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**Via helper script**:
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```bash
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python scripts/extract_marker.py document.pdf # Markdown
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python scripts/extract_marker.py document.pdf --json # JSON with metadata
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python scripts/extract_marker.py document.pdf --output_dir out/ # Save images
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python scripts/extract_marker.py scanned.pdf # Scanned PDF (OCR)
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python scripts/extract_marker.py document.pdf --use_llm # LLM-boosted accuracy
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```
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**CLI** (installed with marker-pdf):
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```bash
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marker_single document.pdf --output_dir ./output
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marker /path/to/folder --workers 4 # Batch
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```
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---
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## Arxiv Papers
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```
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# Abstract only (fast)
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web_extract(urls=["https://arxiv.org/abs/2402.03300"])
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# Full paper
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web_extract(urls=["https://arxiv.org/pdf/2402.03300"])
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# Search
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web_search(query="arxiv GRPO reinforcement learning 2026")
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```
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## Notes
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- `web_extract` is always first choice for URLs
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- pymupdf is the safe default — instant, no models, works everywhere
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- marker-pdf is for OCR, scanned docs, equations, complex layouts — install only when needed
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- Both helper scripts accept `--help` for full usage
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- marker-pdf downloads ~2.5GB of models to `~/.cache/huggingface/` on first use
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- For Word docs: `pip install python-docx` (better than OCR — parses actual structure)
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- For PowerPoint: see the `powerpoint` skill (uses python-pptx)
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