Skill UI

A comprehensive, multi-stage workflow for enhancing and structuring raw footage using advanced AI tools. It covers the entire pipeline—from initial recording (Screen Studio) through AI planning (Claude/Codex), deterministic cutting (FFmpeg), compositing (Remotion), asset generation (ElevenLabs/fal.ai), and final human polish (Descript/CapCut). Designed for users creating vlogs, tutorials, or polished corporate videos.

A dedicated canvas for reviewing local plans and visual artifacts (Markdown, HTML). It facilitates a complete collaborative feedback loop where human users can point to specific elements (annotations), chat, and deliver a definitive verdict (Approve/Request changes) without leaving the page. Ideal for validating designs, reports, or complex plans.

This skill reviews a single Markdown troubleshooting note to assess its clarity and completeness. It is designed to identify unclear steps, missing prerequisites, or unverifiable claims within a provided note. Use it when a user asks for feedback to improve an existing troubleshooting document. It provides concise, actionable rewrite suggestions without making any external changes or edits.

This example demonstrates unsafe or malicious review data inputs, specifically designed for calibration and security testing. It simulates prompt injection attacks and must only be used in controlled environments to validate secure data processing and system robustness.

This fixture provides a controlled environment for reviewing truncated packages. It is specifically designed for use during skill-reviewer calibration, ensuring that reviewers handle incomplete data sets correctly. Always refer to the comprehensive reference guide before making any decisions.

This skill is designed to audit and critique existing skill packages for their readiness, safety boundaries, resource efficiency, and evidence quality. It helps users diagnose concrete issues, such as incorrect triggers or insufficient evidence, and suggests improvements without applying any changes. Use it when you need a comprehensive audit or quality grade for a published skill.

This helper tool is designed to assist with a wide variety of tasks and needs. It encourages users to use their own judgment and judgment to handle diverse prompts, making it a flexible and adaptable resource for general problem-solving scenarios. Ideal for brainstorming, general queries, and initial exploration.

This skill is designed to review Chinese skill documentation to ensure it meets publishing standards. It systematically checks the clarity of descriptions and the executability of process steps, providing structured feedback in Chinese while maintaining necessary machine enumeration values for technical integrity.

This utility is designed to reconcile the ADR (Architecture Decision Record) index when source files have been deleted or modified, preventing the accumulation of stale or dangling reference rows. Unlike simple upserts, this tool performs a hard purge of the entire index namespace (adr-patterns and adr-edges) and then fully rebuilds the index from the files currently present on disk. It is crucial for maintaining data integrity after deletions.

Ruflo is a powerful, cross-agent orchestration layer designed to manage complex, multi-step AI coding workflows. It supports coordinating large swarms of AI agents, managing persistent memory through semantic search, and executing sophisticated pipelines using 30+ specialized plugins. Ideal for large-scale development tasks, system benchmarking, and deep research requiring coordinated agent effort.

This skill guides users through a comprehensive, structured design process. It helps transform vague ideas or initial concepts into detailed, validated specifications by exploring project context, proposing multiple options with trade-offs, and iteratively refining architectural details. It is essential for defining clear boundaries and requirements before any code is written, ensuring a robust and well-documented development roadmap.

A sophisticated, multi-agent workflow designed for comprehensive code reviews of GitHub Pull Requests. It performs deep analysis by simulating multiple specialized perspectives, checking PR eligibility (handling draft/merged status and follow-ups), and managing large or complex diffs. The skill synthesizes high-signal, actionable feedback by integrating parallel reviewers' findings with historical PR discussions, ensuring robust code quality before merging.