Skill UI

This skill provides a comprehensive guide to implementing robust API versioning strategies, supporting methods like URL path, custom headers, and query parameters. It covers building backward compatibility layers, adding standard deprecation notices (e.g., Sunset Header), and utilizing OpenAPI specifications to detect breaking changes, ensuring stable API evolution over time.

This skill enables AI-assisted API development workflows. It can generate type-safe API clients, comprehensive documentation, and SDKs for multiple languages (such as Python or TypeScript) directly from OpenAPI/Swagger specifications. It is essential for developers building complex integrations or needing to standardize API usage across multiple platforms.

This skill guides users through a comprehensive, multi-stage process for creating high-quality documentation. It systematically moves through Context Gathering (understanding the background and audience), Refinement & Structure (iteratively building and editing sections), and Reader Testing (simulating external review to catch blind spots). Ideal for drafting complex specifications, proposals, or design documents that need to be robust and clear for a wide audience.

A powerful tool to scaffold and ship production-ready Model Context Protocol (MCP) servers directly from OpenAPI specifications. It supports Python and TypeScript, ensuring schema validation, safe evolution, and robust tool definition generation, eliminating the need for manual wrapper coding. Use it to expose existing REST APIs to LLM agents or build shared tool integrations.

Creates detailed implementation plans for multi-step tasks by outlining file responsibilities, concrete coding/testing steps, and commit guidance so developers can execute with no prior context.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

A rigorous, multi-phase auditing skill designed for blockchain protocols. It verifies whether the implemented smart contract code strictly adheres to the detailed specifications provided in whitepapers, design documents, or technical specifications. It performs exhaustive, line-by-line semantic analysis to identify gaps, inconsistencies, and deviations across logic, invariants, and security guarantees, making it essential for formal compliance checking in decentralized systems.

Generates a complete product feature specification by orchestrating the Prometeo PM agent, covering acceptance criteria, scope, dependencies, risks, and structured handoff notes so teams can confidently implement new work.

Validates SKILL.md files against the agentskills specification and Anthropic best practices, catching structure, semantic, naming, and quality issues in a single read-only pass before publishing or debugging skills.

This comprehensive tool validates Claude Code plugins against official Anthropic specifications and internal enterprise standards. It performs two main types of checks: structural validation (checking `plugin.json` fields, file references, and permissions) and content validation (conducting deep grading using `SKILL.md`). Use this when building, auditing, or preparing plugins for marketplace submission to ensure compliance and quality.

This skill automates the complex and tedious process of porting academic papers written in LaTeX from one publisher's journal template (e.g., Springer) to another (e.g., MDPI, IEEE). It executes a structured multi-stage workflow, including content extraction, structural mapping, systematic fixing of common LaTeX issues (like float placement and math environments), and final compilation to ensure the output adheres to the target journal's specifications.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.