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Found 91 Skills
Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.
Use when invalid data causes failures deep in execution - validates at every layer data passes through to make bugs structurally impossible rather than temporarily fixed
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Execute read-only SQL queries against Databricks. Use when you need to run a specific SQL query, aggregate data, join tables, or answer analytical questions about Databricks data.
Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says "审查论文数据", "check paper claims", "verify numbers", "论文数字核对", or before submission to ensure paper-to-evidence fidelity.
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
Complete, populate and fill out 3-statement financial model templates (Income Statement, Balance Sheet, Cash Flow Statement) . Use when asked to fill out model templates, complete existing model frameworks, populate financial models with data, complete a partially filled IS/BS/CF framework, or link integrated financial statements within an existing template structure. Triggers include requests to fill in, complete, or populate a 3-statement model template
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
Zod v4 schema validation for TypeScript. Covers primitives, string formats, objects, arrays, unions, coercion, transforms, refinements, parsing, type inference, error customization, JSON Schema, file validation, and metadata. Use when writing schemas, validating input, parsing data, inferring types, or converting schemas with Zod.
This skill should be used when the user asks to "validate data with pydantic", "create a pydantic model", "use pydantic best practices", "write pydantic validators", or needs guidance on pydantic v2 patterns, serialization, configuration, or performance optimization.
Parseur et explicateur complet du format HPK (format de message propriétaire santé). Supporte plus de 100 types de messages couvrant l'administration des patients (ID, MV, CV), la chaîne logistique (PR, FO, MA, CO, LI, RO, FA), les stocks (SO, IM), la structure organisationnelle (ST, UT) et les opérations financières (RD, DD). Utilise @erp-pas/hpk-dictionary comme source de vérité. Valide la structure, extrait les champs, explique le contexte métier, mappe vers HL7 v2.5/IHE PAM et aide au dépannage des problèmes d'intégration.
Review football data code and visualisations for correctness. Use after building a chart, data pipeline, or analysis. Dispatches specialised reviewers for data correctness, chart conventions, visual inspection, and interactive edge cases.