Skill UI

Use the provided helper scripts to track and manage OpenCode sessions, list tasks, and inspect messaging status from the workspace; run them via `uv run python skills/opencode/<script>.py` with Python 3 available.

A structured methodology for initiating complex software projects or features. Instead of writing code immediately, this process mandates creating a detailed, shared specification (Spec) first. It guides users through four phases (Specify, Plan, Tasks, Implement) to clarify ambiguous requirements, define system boundaries, and establish measurable success criteria before any implementation begins.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.

This framework provides a rigorous, multi-dimensional guide for conducting code reviews. It evaluates five critical axes: correctness, readability, architecture, security, and performance. Use this process before merging any code change, especially when reviewing PRs, refactoring, or evaluating code produced by AI agents, to ensure continuous improvement and maintain high code health.

This tool generates highly standardized and ultra-compressed commit messages following the Conventional Commits specification. It ensures that commits are concise, professional, and adhere to strict formats (e.g., type(scope): summary). It intelligently generates a body only when the context (like breaking changes or complex reasoning) requires it, significantly reducing noise and maintaining a clean, clear project history.

Use the gh CLI to inspect PRs, issues, workflow runs, and logs while querying GitHub resources via the API or structured JSON filters.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

This advanced prompt acts as a structured guide for generating comprehensive, enterprise-grade technical implementation plans. It forces the detailed breakdown of a feature based on a PRD, covering system architecture (Mermaid diagrams), database schema design, API specifications, frontend component hierarchy, and deployment strategies, all while adhering to a monorepo structure. Ideal for large-scale SaaS development.

This advanced prompt simulates a Senior Project Manager and DevOps specialist. It takes comprehensive feature artifacts (PRD, technical breakdowns, testing plans) and generates a structured, automated GitHub project plan. It enforces Agile best practices (Epic > Feature > Story > Enabler > Task) and includes templates for issue creation, dependency management, risk assessment, and Definition of Done, ensuring robust project tracking within GitHub.

This comprehensive template and guide facilitate the containerization of ASP.NET Core applications. It provides a structured prompt and checklist to ensure that the resulting Dockerfile adheres to industry best practices for performance, security, and maintainability when running on Linux containers. It covers all necessary aspects, including dependency management, port exposure, build stages, and custom configuration.

This workflow guides developers through generating standardized, descriptive commit messages using the Conventional Commits specification. It provides a structured XML template (including type, scope, description, body, and footer) and integrates directly into the Git workflow, ensuring project history is clean, traceable, and machine-readable. Ideal for maintaining large, collaborative codebases.

Copilot Spaces allow users to consolidate and manage project-specific knowledge, including documentation, code snippets, architectural standards, and operational instructions. By loading a Space, Copilot grounds its responses not just on general knowledge, but on the curated, authoritative context of the entire project, enabling accurate Q&A, code generation, and workflow execution based on team standards. Ideal for onboarding, large-scale projects, and maintaining consistency.