vamseeachanta
vamseeachanta/workspace-hub
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npx skills add https://github.com/vamseeachanta/workspace-hub --skill pdf-text-extractor-readability-classification命令会保持在同一行。复制前请横向滚动并检查完整内容。
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Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
基于 SOC 职业分类
正在显示 SKILL.md
| name | pdf-text-extractor-readability-classification |
| description | Sub-skill of pdf-text-extractor: Readability Classification. |
| version | 1.2.0 |
| category | data |
| type | reference |
| scripts_exempt | true |
Before extracting text from a large PDF collection, classify each PDF's readability
using enrich-readability.py. This determines which extraction strategy to use:
| Classification | Meaning | Extraction strategy |
|---|---|---|
machine | Text layer present, directly extractable | pdfplumber / PyMuPDF |
ocr-needed | Scanned image, no text layer | tesseract / doctr / azure-doc-intelligence |
mixed | Some pages machine-readable, some scanned | Hybrid — extract text pages, OCR image pages |
error | Corrupted or unreadable | Skip; log for manual review |
Key finding: 27-30% of project PDFs are scanned with no text layer. Attempting direct text extraction on these returns empty strings — always classify first.
| Classification | Count | Percentage |
|---|---|---|
| native | 623,455 | 60.3% |
| machine | 278,899 | 27.0% |
| ocr-needed | 92,042 | 8.9% |
| missing | 27,476 | 2.7% |
| error | 6,221 | 0.6% |
| mixed | 5,246 | 0.5% |
| Total classified | 1,033,933 | 96.7% |
Error reduction: 296,626 → 6,221 (97.9% recovery). Remaining errors are genuine edge cases (corrupt PDFs, missing files, extremely complex documents).
Use pdftotext (poppler) for batch classification — not pdfplumber:
# Classify all PDFs with parallel workers (resume-safe)
uv run --no-project python scripts/data/document-index/enrich-readability.py \
--workers 10 --resume
Use --workers 10 for bulk enrichment to parallelize across CPU cores. The --resume
flag skips already-classified entries, making it safe to restart after interruption.
WARNING (WRK-1277): The original
enrich-readability.pyused pdfplumber inProcessPoolExecutor— this hung in D-state on NTFS/NFS mounts. The proven pattern is pdftotext viasubprocess.run(timeout=30)with 8 workers (seepdf/pdftotext-popplersub-skill for code). Throughput: ~49 files/sec vs ~1.3 with pdfplumber.