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bridging-presidio-and-spacy

Combine OpenMed clinical NLP with Microsoft Presidio, spaCy, or LangChain through OpenMed's built-in interop adapter registry (openmed.interop). Covers the lazy adapter registry (available_adapters, get_adapter, adapter_spec), the presidio/spacy/langchain pip extras, and the verified callables — Presidio to_canonical/from_canonical/merge_with_openmed, the spaCy openmed_deid pipeline factory, and the LangChain create_redaction_runnable. Use when the user wants to add Presidio recognizers, embed OpenMed PII detection in a spaCy pipeline, or use OpenMed de-identification as a LangChain runnable. Pairs adjacent to the OpenMed PII skills.

70

Quality

85%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A dense, actionable reference skill with executable examples for every bridge and useful OpenMed-specific gotchas. It is efficient and well-structured, with only minor conciseness and workflow-loop gaps.

DimensionReasoningScore

Conciseness

Mostly lean with dense, OpenMed-specific code and minimal generic padding, but a few prose passages (e.g. the 'Why merge instead of union' rationale and intro framing) could be trimmed slightly without losing clarity.

4 / 5

Actionability

Copy-paste-ready executable code blocks for the registry, Presidio, spaCy, and LangChain bridges with concrete imports, signatures, and config dicts, clearly marking where the user supplies their own analyzer/anonymizer objects.

5 / 5

Workflow Clarity

Content is clearly sequenced (when-to-use → registry → each bridge → hand-off → gotchas) and includes a validation checkpoint ('Gate de-id quality with openmed.eval leakage gates'), but there is no explicit validate→fix→retry feedback loop.

4 / 5

Progressive Disclosure

Well-organized into clearly headed sections with a dedicated 'Standards & references' list of external URLs and no nested file references, but as a single ~180-line file with no bundle split it is good rather than exemplary.

4 / 5

Total

17

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20

Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A highly specific, well-structured description that clearly states both capability and trigger conditions with concrete callable and ecosystem names. Trigger-term breadth and adjacent-skill overlap keep it just short of perfect.

DimensionReasoningScore

Specificity

Lists multiple concrete actions across all three bridges — 'to_canonical/from_canonical/merge_with_openmed', the spaCy 'openmed_deid pipeline factory', and the LangChain 'create_redaction_runnable' — plus the registry API and pip extras, giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both what (combine via the interop adapter registry, covering registry/extras/callables) and when ('Use when the user wants to add Presidio recognizers, embed... or use... as a LangChain runnable') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural trigger phrases like 'add Presidio recognizers', 'embed OpenMed PII detection in a spaCy pipeline', and 'use OpenMed de-identification as a LangChain runnable', but lacks synonym/file-extension breadth to reach fully comprehensive coverage.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (interop bridging to three named ecosystems) with distinct triggers, but the closing 'Pairs adjacent to the OpenMed PII skills' signals minor overlap risk with closely related sibling skills.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
maziyarpanahi/openmed
Reviewed

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