Conciseness

The body is dense with project-specific information (real signatures from 'openmed/processing/batch.py', gotchas like 'Append-only is the checkpoint') and contains no explanations of concepts Claude already knows. Not a 4: every section adds non-obvious information and there is no padding to trim.

Actionability

Quick start, per-operation constructor snippets, the streaming example, and a complete copy-paste 'resumable batch runner' are all fully executable and cover the common cases (NER, PII, de-identify; resume; sharding). The only nit — the undefined 'write_jsonl(item)' helper in the streaming snippet — is resolved by the full runner later, so it does not drop to 4.

Workflow Clarity

The 7-step Workflow is clearly sequenced and, appropriate for a batch operation, includes verification: resume skips ids already in output ('Re-run to resume: skip ids already present'), failures are recorded via 'continue_on_error=True', and step 7 reconciles via 'result.get_failed_results()'. Not a 5: there is no explicit post-run output-integrity or success-rate validation step, leaving a minor validation gap.

Progressive Disclosure

No bundle files exist, and the ~170-line body is well-sectioned with a 'Standards & references' section pointing to real, one-level-deep sources (jsonlines.org, HHS, 'openmed/processing/batch.py'). Not a 5: the inlined API-signature listing and the full runner code could live in reference files to keep SKILL.md a leaner overview.

Specificity

The description lists multiple concrete actions — 'Run large-scale batch NER, PII extraction, or de-identification... with sharding, checkpointing, resumability, and append-only JSONL output' — with comprehensive coverage of the skill's capabilities. A score of 4 would require minor gaps in coverage, but the action list here spans operations, output format, and reliability mechanisms.

Completeness

It clearly answers 'what' ('Run large-scale batch NER, PII extraction, or de-identification... with OpenMed' plus the 'Covers...' sentence naming the API surface) and 'when' via an explicit 'Use when the user needs to...' clause with concrete trigger phrases. Not a 4: both what and when are explicit and specific, matching the anchor-5 example structure.

Trigger Term Quality

'process a corpus or folder of notes, de-identify a dataset, run NER over thousands of documents, build a resumable batch pipeline, or stream results to JSONL' gives comprehensive natural-phrase coverage with synonyms (corpus/dataset/folder/thousands of documents). Not a 4: even common variations like 'de-identify a dataset' and 'resumable pipeline' are present, matching how users would phrase batch-scale needs.

Distinctiveness Conflict Risk

The batch/corpus framing ('large-scale', 'corpus or folder of notes', 'thousands of documents', 'resumable batch pipeline') carves a clear niche distinct from single-note or serving skills. Not a 5: 'NER, PII extraction, or de-identification' mildly overlaps the single-document sibling skills until the scale triggers disambiguate, leaving minor overlap risk with closely related skills.