AI Skills for ML / AI Engineer

Routes plain-language requests for various coding agents into OpenClaw ACP runtime sessions or direct acpx-driven sessions.

Guides the creation of high-quality MCP servers for LLMs, enabling integration with external APIs using Python or TypeScript.

Facilitates the creation and optimization of AI skills, enabling users to develop, evaluate, and enhance their skill performance effectively.

Provides patterns for using the Anthropic Claude API in Python and TypeScript, enabling efficient application development.

Facilitates multi-agent orchestration using dmux for parallel workflows, enhancing productivity in AI development tasks.

Implements eval-driven development for Claude Code sessions, enhancing AI reliability through structured evaluation frameworks.

Enhances multi-agent workflows by refining context retrieval through iterative search patterns, improving task execution efficiency.

Covers best practices for designing REST APIs, including resource naming, status codes, and error handling for robust API development.

Provides best practices for ClickHouse database patterns, query optimization, and analytics for high-performance data engineering.

Automatically extracts reusable patterns from Claude Code sessions for future use, enhancing learning and efficiency.

Provides idiomatic Go patterns and best practices for building robust, efficient, and maintainable applications.

Enforces code quality in real-time using Plankton, automatically formatting and checking code with integrated linter support.

Provides a decision framework for choosing between regex and LLM for structured text parsing, optimizing accuracy and cost.

Optimizes LLM API costs with model routing, budget tracking, retry logic, and prompt caching for efficient usage.

Integrates Apple's FoundationModels framework for on-device LLM capabilities, enabling text generation and structured output in iOS apps.

Facilitates testable Swift code through protocol-based dependency injection, enabling simulation of file systems, networks, and APIs.

Provides best practices and idioms for building robust and maintainable Python applications, focusing on readability and type hints.

Enables AI agents to operate efficiently through prioritized execution, task decomposition, and cost-aware model routing.

Enables autonomous workflows in Claude Code, facilitating CI/CD pipelines and multi-agent orchestration for efficient development.

Enforces modern C++ coding standards based on core guidelines to ensure safe and idiomatic practices in C++ development.