Conciseness

The body is efficient and assumes competence — no primer on what OCR or C-CDA is, every section carries OpenMed-specific facts ("ocr() lives in the submodule (it is intentionally not re-exported)", "PDF and DOCX have no live handler yet"). It is not a 5 because two facts are stated twice: the submodule import and the PDF/DOCX UnsupportedDocumentError both appear in their main sections and again verbatim in "Edge cases & gotchas", which could be trimmed.

Actionability

All code is copy-paste ready with real imports ("from openmed.multimodal.ocr import ocr", "from openmed.multimodal import redact_document"), executable calls, and expected outputs ("doc.text", "doc.spans[:3]", "redacted.manifest"). Install commands are exact ("pip install \"openmed[multimodal]\"", "pip install \"openmed[ocr-paddle]\"") and the common cases — image OCR, one-step redact_document, CSV redaction, offset-to-bbox mapping — each get a runnable example. Not a 4: no example is pseudocode and none is missing key details.

Workflow Clarity

The sequence is explicit and well-ordered — "This is the **first** stage. After intake, hand off to deidentifying-clinical-text then extracting-clinical-entities", plus a numbered two-step quick start and a clear one-step vs two-step decision rule ("Use redact_document when you want OpenMed to own intake **and** redaction"). Checkpoints exist ("inspect OcrWord.confidence to flag low-quality pages", "MissingDependencyError with an install hint", run NER on the **redacted** text) but they live in a gotchas list rather than being woven into the pipeline steps, which fits anchor 4 ("most checkpoints present; minor validation gaps") better than 5.

Progressive Disclosure

SKILL.md is a true overview: the full data contract, engine list, and tabular pipeline are split into references/multimodal-ingest.md (verified to exist), which is one level deep and clearly signaled at three points ("See [references/multimodal-ingest.md] ... for the full contract, engines, and the tabular pipeline"). Sections are well-organized (when to use, install, quick start, per-format sections, hand-off, gotchas), matching the anchor-5 pattern of a concise overview with well-signaled single-depth references. Not a 4: there are no organization gaps or inline content that belongs in the reference file.

Specificity

The description lists multiple concrete actions — "Turn scanned faxes, images, and CSV/CDA exports into clean text ready for OpenMed de-identification and NER", structured intake, tabular redaction, layout and reading order — and names the exact entry points ("ocr() and redact_document()") and the "ExtractedDocument contract". Coverage is comprehensive across image, tabular, and C-CDA inputs, matching the anchor for multiple specific concrete actions. It is not a 4 because there are no minor gaps: every supported input family and the core API surface is explicitly named.

Completeness

It explicitly answers both questions: what ("Turn scanned faxes, images, and CSV/CDA exports into clean text ready for OpenMed de-identification and NER, fully on-device") and when ("Use when the user has clinical documents ... and needs OCR or structured intake before openmed.deidentify and openmed.analyze_text"). This mirrors the anchor-5 example's structure with concrete trigger phrases; the "what" is not vague and the "when" clause is explicit, so 4 ("when could be more explicit") does not fit.

Trigger Term Quality

Natural trigger terms are comprehensive with synonyms: "scanned faxes", "images", "image scans", "photographed/faxed notes", "tabular CSV/TSV exports", "C-CDA XML", "OCR", "Tesseract / PaddleOCR", "tabular redaction", and "layout and reading order", plus module-level triggers ("asks about openmed.multimodal", "openmed.deidentify"). Not a 4: even the engine names and both CSV/TSV and C-CDA format variants a user would naturally say are present.

Distinctiveness Conflict Risk

The niche is clear — on-device clinical document intake feeding openmed.deidentify/analyze_text — and it explicitly delimits against sibling skills ("Pairs before deidentifying-clinical-text and extracting-clinical-entities"), so triggers like "C-CDA", "PaddleOCR", and "tabular redaction" are unlikely to fire the wrong skill. Not a 4: there is no meaningful overlap risk with adjacent skills since the pipeline position and module names are stated.