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Found 68 Skills
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.
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.
Migrates databases between providers (Postgres, MySQL, Supabase, PlanetScale, MongoDB). Reads source schema, generates migration scripts, handles data type mapping, foreign keys, indexes, triggers, stored procedures. Validates migration with row counts and checksums. Generates migration-plan.md with step-by-step execution guide, rollback procedures, estimated downtime.
Reconciliation Report Analysis Expert - Parses ONLY local Settlement Detail report files (SETTLEMENT_DETAIL_*.csv / .xlsx) for settlement amount validation, fee analysis, and reconciliation knowledge Q&A. Does NOT support Transaction Detail or Settlement Summary reports. Triggers: settlement detail parsing, settlement amount validation, fee analysis, fee model, reconciliation knowledge, interchangeFee, schemeFee, fee rules, settlement, attribution.
Activated when the user wants to create a data model, validate data, serialize JSON, create Pydantic models, add validators, define settings, or create request/response schemas. Covers Pydantic v2 BaseModel, Field, validators, data validation, JSON schema generation, serialization, deserialization, and settings management.
Generates comprehensive synthetic fine-tuning datasets in ChatML format (JSONL) for use with Unsloth, Axolotl, and similar training frameworks. Gathers requirements, creates datasets with diverse examples, validates quality, and provides framework integration guidance.
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.
Multi-layer validation pattern - validates data at EVERY layer it passes through to make bugs structurally impossible, not just caught.