اپنے ایجنٹ کے لیے موزوں صلاحیت تلاش کریں۔
Pytest testing patterns for data pipelines, Airflow DAGs, and BigQuery queries. Use when writing unit tests for DAG tasks, integration tests with BigQuery, property-based tests with Hypothesis, data contract validation with Pydantic/JSON schemas, performance tests with pytest-benchmark, or snapshot/visual regression tests for data outputs.
Use when working with DuckDB databases, Makefiles, or building/deploying notebooks. Triggers on DuckDB queries, database creation, Makefile editing, make targets (build, data, etl), GitHub Actions workflows, CI/CD, and creating new notebook repositories.
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.
BigQuery query optimization, feature engineering, and BigQuery ML models for recommendation systems. Use when writing optimized SQL queries, implementing partitioning/clustering, creating materialized views, engineering user/product/interaction features, handling schema evolution, setting up streaming with Storage Write API, or building Matrix Factorization models.
Expert guidance for Apache NiFi Registry including flow versioning, buckets, Git integration, security, and registry client configuration. Use this when working with flow version control and registry management.
Apache Airflow DAG development with TaskFlow API, Google Cloud operators (BigQuery, GCS), dbt integration, and dynamic DAG generation. Use when creating or modifying Airflow DAGs, implementing data pipeline orchestration, setting up cross-DAG dependencies with ExternalTaskSensor, adding deferrable operators, or configuring error handling and retries.
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.
Guide for creating database tables with Drizzle ORM migrations and repository pattern
Jenkins CI/CD pipelines for data platforms with Kubernetes pod agents. Use when creating Jenkinsfiles, implementing parallel linting stages (Ruff, Black, Mypy), DAG validation with DagBag, security scanning (Bandit, Safety, detect-secrets), deployment strategies (canary with error monitoring, blue-green), or setting up artifact management and DORA metrics tracking.
Flutter + fpdart環境でのDriftデータベース管理の包括的なスキル。以下の場合に使用：(1) 型安全なクエリを持つDriftデータベースのセットアップ、(2) EitherとTaskEitherを使った関数型エラーハンドリングの実装、(3) fpdartの合成を使ったリポジトリパターンの作成、(4) データベースマイグレーションとスキーマ変更の管理、(5) 関数型変換を使ったリアクティブストリームの記述、(6) 関数型ラッパーを使ったトランザクション管理の実装、(7) 例外を使わない関数型データベースエラーハンドリング、(8) 依存性注入を使ったテスト可能なデータベースレイヤーの作成、(9) Driftのクエリビルダーを使った複雑なクエリの構築、(10) リレーションシップ管理の実装、(11) JSONカラムを使った柔軟なデータ構造の保存と取得
Use when designing, implementing, optimizing, or troubleshooting AF.ECT.Database schemas, queries, stored procedures, performance tuning, data integrity, or operational database tasks while following Microsoft best practices
Complex task decomposition and workflow orchestration specialist. Use PROACTIVELY for multi-step projects, ChromaDB integration, semantic search architecture, tool/agent coordination, and knowledge base design. (project)
Firebase Data Connect integration for GraphQL-based data access with PostgreSQL. Use when building GraphQL schemas, queries, mutations, or integrating Firebase Data Connect with Angular applications. Supports type-safe generated SDKs, real-time subscriptions, and server-side data validation.
Instructions for generating synthetic airline data with Synth CLI and loading it into SQLite.
Run the full ParserZamaActive validation suite to ensure database schema is synchronized, parser logic matches golden set expectations, and block type classification is accurate. CRITICAL - Use this skill BEFORE committing any code changes to git. Use when you need to: (1) Validate database schema version and table structure, (2) Test parser accuracy against golden set truth data, (3) Verify block type normalization and classification, (4) Ensure system integrity after migrations or parser changes, or (5) Prepare for git commit with confidence that all tests pass
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.
Expert guidance for creating, implementing, and troubleshooting Sync Jobs in the Frappe Tweaks framework. Use when working with Sync Job Types, Sync Job controllers, sync job enqueueing, debugging sync job issues, implementing sync logic, or understanding sync job lifecycle and hooks.
Understand data structures in the GCACW parser pipeline (raw tables, parsed units, web JSON). Use when debugging data issues, understanding field mappings between stages, checking column configurations, or investigating why units are missing or malformed. Includes inspection utilities (inspect_raw.py, inspect_parsed.py, compare_data.py). Essential for work involving raw_table_extractor.py, parse_raw_tables.py, convert_to_web.py, or game_configs.json.
