Find the perfect capability for your agent.
Turn the model into a SQLAlchemy-focused Python coding agent for designing models, writing queries, debugging database issues, and integrating SQLAlchemy with Alembic and FastAPI in new or existing Python projects.
Execute SQL queries against Snowflake data warehouse using Python connector. Supports password, key-pair, and SSO/OAuth authentication. Use for ad-hoc queries, data extraction, and schema exploration. Output in JSON, table, or CSV format.
FastAPI + Pydantic v2 backend workflow: implement/modify API endpoints with thin handlers + service layer, add pytest tests, manage idempotent Alembic migrations, and implement streaming ETL/parsers with invalid-row CSV logging. Use this skill when working on Python backend APIs, schema changes, or ingestion/parsing pipelines.
Python dataclass best practices: slots, frozen, validation. Trigger when optimizing dataclasses or creating config classes.
Use when editing .sql files, reviewing SQL in Python/JavaScript/TypeScript code, writing database queries, or when user mentions PostgreSQL, SQL validation, query optimization, or database schema.
ALWAYS USE when working with dbt models, SQL transformations, tests, snapshots, or macros. Use IMMEDIATELY when editing dbt_project.yml, profiles.yml, or creating SQL models. MUST be loaded before any transform-layer work. Enforces dbt owns SQL principle - never parse, validate, or transform SQL in Python.
A skill that configures the agent to design, refactor, validate, and debug Pydantic v2 and v1 models, validators, and settings. Use this skill for structured data modeling, API schema generation, validation logic, or Pydantic version migration tasks.
Expert guidance for SQLModel - the Python library combining SQLAlchemy and Pydantic for database models. Use when (1) creating database models that work as both SQLAlchemy ORM and Pydantic schemas, (2) building FastAPI apps with database integration, (3) defining model relationships (one-to-many, many-to-many), (4) performing CRUD operations with type safety, (5) setting up async database sessions, (6) integrating with Alembic migrations, (7) handling model inheritance and mixins, or (8) converting between database models and API schemas.
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, or implementing data governance.
Diff-aware guardrail checker for Fear-of-Falling (FOF) changes; fails closed on raw data edits, Kxx intro/req_cols mismatches, and output discipline risks.
Use when adding lightweight data versioning and dataset reproducibility practices.
Create and manage database migrations with reversible up/down methods, zero-downtime deployment strategies, and proper schema versioning. Use this skill when creating database migration files, altering table schemas, adding or modifying database indexes, or implementing data migrations. Use when working with migration tools like TypeORM migrations, Sequelize migrations, Alembic (Python), Rails migrations, Flyway, or Liquibase. Use when writing migration files (e.g., YYYYMMDDHHMMSS_migration_name.ts, 001_create_table.sql, versions/*.py) or when modifying database schema in a version-controlled manner. Use when handling backwards compatibility for high-availability deployments or when separating schema changes from data migrations.
SQLModel ORM skill for Python database operations. Use when defining database models, creating tables, managing sessions, writing CRUD operations, or integrating with FastAPI. Triggers include "create model", "database table", "SQLModel", "ORM", "session management", or "CRUD operations".
Guidance for working with Awkward Array 2.0 jagged arrays and records in Python. Use when building or debugging `awkward` workflows, including record construction with `ak.zip`, adding fields with `ak.with_field`, filtering/aggregation, combinatorics (`ak.cartesian`/`ak.combinations`), `argmin`/`argmax` slicing, flattening, sorting, and NumPy interop or common Awkward pitfalls.
Field naming conventions for the Job Aggregator project. Use this skill when encountering type errors related to field names (camelCase vs snake_case), database constraint violations, or data mapping issues between Python/TypeScript/PostgreSQL.
Python specialist for Faiston One Platform. Use when writing FastAPI endpoints, Google ADK agents, async patterns, Pydantic models, and Lambda handlers. PROACTIVE for code review and refactoring.
Comprehensive SQLModel skill for Python database operations with SQL databases. Use when working with SQLModel for (1) Designing database models and table schemas, (2) Creating relationships between tables (foreign keys, one-to-many, many-to-many), (3) Integrating SQLModel with FastAPI for CRUD APIs, (4) Writing queries with select, where, joins, and filtering, (5) Implementing best practices for session management, migrations, and performance optimization. Covers the multiple model pattern (Base, Table, Create, Public, Update), dependency injection, pagination, error handling, and production-ready patterns.
Provides patterns and guidance for implementing user-scoped data filtering and multi-tenancy in web applications. Use this skill when you need to: (1) Restrict data access based on user identity, (2) Implement ownership checks for database operations, (3) Build multi-tenant applications with organization-level data scoping, (4) Implement admin bypass for viewing all data, (5) Create audit trails for data access. This skill focuses on Python, FastAPI, and SQLAlchemy.
Documentation for Kubernetes Agent Sandbox - a CRD-based system for managing isolated AI agent execution environments. Use for queries about Sandbox CRDs (Sandbox, SandboxTemplate, SandboxClaim, SandboxWarmPool), Python SDK (SandboxClient, SandboxRouter, ComputerUseExtension), network policies, security configurations, and implementation examples. Keywords kubernetes sandbox, agent sandbox, CRD, python sdk, agentic-sandbox-client, isolated environment, gvisor, network policy.
Guide implementation of production-ready Python RAG components by enforcing correct abstractions, error handling, observability, and safety at coding time.
