Conciseness

The body is efficient — no explaining of what PDFs/Markdown/OCR are and no library tutorials — but the provenance note describing the 18-PDF historical-entomology corpus (1833–2015, 5 languages) is a mild over-share of background that could be trimmed, fitting the 'efficient; minor instances of over-explanation' anchor.

Actionability

Fully executable, copy-paste-ready commands throughout — 'conda env create -f environment.yml', 'python scripts/ocr_document.py --input docs/ --dry-run', the full run invocation with --backend/--host/--dpi — plus a complete actionable backend matrix and a concrete file-extension list, covering the common cases.

Workflow Clarity

A clear 5-step workflow (confirm backend, gather inputs, classify via --dry-run, run OCR, review) with explicit preflight validation ('fails with a clear message if the backend isn't configured') and per-page-failure handling, plus cost/safety guidance for cloud batch runs; falls just short of 5 because there is no explicit validate->fix->retry loop beyond 'iterate if needed'.

Progressive Disclosure

The body is a clear overview with well-signaled one-level-deep references — references/server_setup.md, references/backends.md ('See ... for the full matrix ...'), references/output_format.md ('Full schema and frontmatter fields') — all of which exist as real, non-nested files, with detail appropriately split out and scripts' internals kept out of the overview.

Specificity

Concrete actions are named — 'Convert scanned PDFs and document images into clean Markdown', 'preserving multi-column reading order, diacritics, special characters, and figures', 'Supports local vLLM/Ollama servers and cloud vision APIs' — with comprehensive coverage, though the core action is a single convert-to-Markdown operation with preservation details rather than a list of distinct operations.

Completeness

Both 'what' ('Convert scanned PDFs and document images into clean Markdown using docling for layout ... plus a vision-language OCR model') and 'when' ('Use when a user needs high-quality OCR of scanned documents, historical literature, or photographed pages') are explicitly stated with concrete trigger phrases.

Trigger Term Quality

Strong natural keyword coverage — 'scanned PDFs', 'document images', 'OCR', 'scanned documents', 'historical literature', 'photographed pages' — with only a few synonyms/extensions (e.g. .pdf, image-to-text) missing, fitting the 'good coverage, a few natural terms missing' anchor.

Distinctiveness Conflict Risk

Clear niche (OCR of scanned documents via docling + VLM) with the 'Assumes an OCR backend already exists' constraint and explicit disambiguation from born-digital extraction giving it mostly distinct triggers, with only minor overlap risk against a generic extract-from-pdfs skill.