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Found 2,413 Skills
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, implementing data governance, or troubleshooting data issues.
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
Interactive LeetCode-style teacher for technical interview preparation. Generates coding playgrounds with real product challenges, teaches patterns and techniques, supports Python/TypeScript/Kotlin/Swift, and provides progressive difficulty training for data structures and algorithms.
Language-specific coding standards and validation rules. Provides Python, Go, Rust, TypeScript, Shell, YAML, JSON, and Markdown standards. Auto-loaded by /vibe, /implement, /doc, /bug-hunt, /complexity based on file types.
Analyzes code to identify untested functions, low coverage areas, and missing edge cases. Use when reviewing test coverage or planning test improvements. Generates specific test suggestions with example templates following amplihack's testing pyramid (60% unit, 30% integration, 10% E2E). Can use coverage.py for Python projects.
Creates custom Docker-based State Transition Functions (STFs) for D6E platform workflows. Use when building containerized business logic for D6E, implementing data processing steps, or creating workflow functions that need database access. Handles JSON input/output, SQL API integration, and multi-language implementations (Python, Node.js, Go).
Integrate and embed OpenAI ChatKit UI into TypeScript/JavaScript frontends (Next.js, React, or vanilla) using either hosted workflows or a custom backend (e.g. Python with the Agents SDK). Use this Skill whenever the user wants to add a ChatKit chat UI to a website or app, configure api.url, auth, domain keys, uploadStrategy, or debug blank/buggy ChatKit widgets.
Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.
Use when discussing or working with DeepEval (the python AI evaluation framework)
Build conversational AI agents using Pydantic AI + OpenRouter. Use when creating type-safe Python agents with tool calling, validation, and streaming.
Use when starting a new session without feature-list.json, setting up project structure, or breaking down requirements into atomic features. Load in INIT state. Detects project type (Python/Node/Django/FastAPI), creates feature-list.json with priorities, initializes .claude/progress/ tracking.
Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction. Originally from OpenAI's curated skills catalog.