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Found 91 Skills
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
Comprehensive HPK (proprietary healthcare message format) parser and explainer. Supports 100+ message types across patient administration (ID, MV, CV), supply chain (PR, FO, MA, CO, LI, RO, FA), inventory (SO, IM), organizational structure (ST, UT), and financial operations (RD, DD). Uses @erp-pas/hpk-dictionary as source of truth. Validates structure, extracts fields, explains business context, maps to HL7 v2.5/IHE PAM, and troubleshoots integration issues.
The drum sounds. Bear and Bloodhound gather for safe data movement. Use when migrating data that requires both careful movement and codebase understanding.
Minimal smoke test for DlfNext skill. Validate metadata discovery and one read-only API call.
Authors JSON Schema definitions for use with z-schema validation. Use when the user needs to write a JSON Schema, define a schema for an API payload, create schemas for form validation, structure schemas with $ref and $defs, choose between oneOf/anyOf/if-then-else, design object schemas with required and additionalProperties, validate arrays with items or prefixItems, add format constraints, organize schemas for reuse, or write draft-2020-12 schemas.
Use when validating data with Standard Schema-compatible schemas or handling ValidationError results.
Parse and explain HL7 v2.5 IHE PAM (Patient Administration Management) messages. Identifies message type, extracts segments (MSH, EVN, PID, PV1, PV2), validates structure, and provides detailed explanations of ADT messages for patient administration workflows.
Standards and best practices for writing LookML tests to ensure data integrity, accuracy, and logic validation.
Sync and validate App Store metadata and localizations with asc, including Fastlane format migration. Use when updating metadata or translations.
pytest, data validation, Great Expectations, and quality assurance for data systems
Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.
Digital archiving workflows with AI enrichment, entity extraction, and knowledge graph construction. Use when building content archives, implementing AI-powered categorization, extracting entities and relationships, or integrating multiple data sources. Covers patterns from the Jay Rosen Digital Archive project.