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Found 86 Skills
Migrates JSON Schemas between draft versions for use with z-schema. Use when the user wants to upgrade schemas from draft-04 to draft-2020-12, convert between draft formats, update deprecated keywords, replace id with $id, convert definitions to $defs, migrate items to prefixItems, replace dependencies with dependentRequired or dependentSchemas, adopt unevaluatedProperties or unevaluatedItems, or adapt schemas to newer JSON Schema features.
Data processing expert including parsing, transformation, and validation
Provides practical Zod v4 validation utilities and schema patterns for TypeScript applications. Use when designing validation layers for API payloads, forms, configuration, and domain input parsing with strong type inference.
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.
OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-industry-commoncore-integration-procedure), authoring OmniScripts (use sf-industry-commoncore-omniscript), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
Use this skill when implementing structured data markup using JSON-LD and Schema.org vocabulary for rich search results. Triggers on adding schema markup for FAQ, HowTo, Product, Article, Breadcrumb, Organization, LocalBusiness, Event, Recipe, or any Schema.org type. Covers JSON-LD implementation, Google Rich Results eligibility, validation testing, and framework integration (Next.js, Nuxt, Astro).
Debug Scikit-learn issues systematically. Use when encountering model errors like NotFittedError, shape mismatches between train and test data, NaN/infinity value errors, pipeline configuration issues, convergence warnings from optimizers, cross-validation failures due to class imbalance, data leakage causing suspiciously high scores, or preprocessing errors with ColumnTransformer and feature alignment.
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
Use when building joi schemas, custom validators, extensions, or working with joi's validation pipeline. Covers all types, references, templates, errors, and the extension API.
Use when invalid data causes failures deep in execution, requiring validation at multiple system layers - validates at every layer data passes through to make bugs structurally impossible