Skill UI

A powerful Python skill for adding professional callouts, arrows, and labels to various images, including screenshots, diagrams, and architecture documents. It allows users to visually highlight specific areas or changes. The advanced `annotate.py` module provides algorithmic label placement, optimizing for readability and preventing overlaps. Ideal for creating clear PR descriptions, documenting changes, or building annotated demo assets.

A comprehensive guide on embedding before-and-after screenshots directly into pull request descriptions. This skill helps reviewers visualize UI changes, layout adjustments, and data visualizations instantly, without needing to check out the branch. Includes best practices for image sizing, structured Markdown patterns, and specific API/upload guidance for Azure DevOps and GitHub.

This skill serves as a compatibility demonstration to verify that the Hermes Agent can successfully parse and load an SKILL.md file generated from an MDA source. It confirms that the compiled structure, including specific frontmatter and reserved metadata namespaces, survives the round-trip compilation process, ensuring structural integrity for the agent's skill registry.

This skill demonstrates OpenCode's compatibility with MDA-compiled SKILL.md files. It verifies that the loader correctly parses crucial frontmatter fields (like name, description, and metadata) after the source has gone through the MDA compilation process, ensuring robust skill integration and data integrity.

This skill runs the Bumblebee scanner on macOS/Linux to perform read-only inventory and exposure checks. It detects compromised packages, developer tools, and host configurations against known security advisories. It is primarily used for supply-chain incident response and vulnerability assessment, providing developers with a clear picture of local system exposure.

A comprehensive toolkit for structuring prospective clinical study designs. It assists in selecting and classifying primary/secondary endpoints, estimating sample size and power for various designs (means, proportions, survival), and scoring overall study plan feasibility (GO/NO-GO). All outputs are estimates routed to specified human experts (clinicians, biostatisticians, regulatory owners), never presented as final facts or regulatory submissions.

A structured playbook for improving the time and space complexity (Big-O) of existing, slow code. This skill guides developers through systematic refactoring, focusing on identifying bottlenecks like nested loops or N+1 queries. It emphasizes applying transformations one at a time, maintaining test coverage, and measuring performance gains to ensure correct and efficient code.

This guide establishes the Conventional Branch specification for naming Git branches. It defines standardized prefixes (e.g., feature/, bugfix/, hotfix/) and enforces strict rules (lowercase, kebab-case, no underscores) to ensure consistency across development teams. Use it to validate, create, or understand the correct naming structure for any new branch, improving project traceability and collaboration.

This tool indexes local files or directories into a persistent FTS5 knowledge base. This allows subsequent search operations to efficiently retrieve highly relevant, focused snippets from the project source without needing to re-read the raw files every time. It is crucial for managing and searching large, complex codebases or detailed project documentation.

Master the design, debugging, and hardening of production-grade GitHub Actions CI/CD pipelines. This skill covers advanced topics including reusable workflows, matrix builds, self-hosted runners, OIDC authentication, secrets management, environment protection, and complex trigger patterns (e.g., workflow_call, schedule, release). Use this to automate complex build, test, and deployment processes.

This orchestrator acts as a central hub for Go development, managing the complexity of modern software projects. Instead of using single skills, it reads the task context (e.g., building gRPC, debugging a panic) and automatically loads the most relevant combination of specialized skills (like testing, error handling, safety, or security) simultaneously. It ensures comprehensive coverage throughout the entire Go development lifecycle, making it ideal for advanced AI coding agents.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.