Skill UI

Code Health MCP provides structural maintainability feedback and design-level health scores, going beyond standard linting. It is crucial for AI-assisted coding workflows, allowing users to review code quality, verify score deltas after changes, and gate commits or pull requests to prevent codebase regression. Use it when refactoring, updating legacy modules, or ensuring the integrity of large diffs.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

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.

Explains AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review handling, author feedback, and stage-by-stage planning for submissions.

This comprehensive guide details the rigorous, multi-stage peer review pipeline used by major academic conferences (like ACM CHI). It breaks down the roles of the 1AC, 2AC, and external reviewers, explaining screening mechanisms like Desk Reject (DR) and Assisted Desk Reject (ADR). It covers the entire journey from initial recommendations to the final PC meeting, providing authors with a framework to interpret feedback and strategically revise their work.

A comprehensive tool designed to help authors assess the suitability and optimal framing of their manuscripts for the IEEE/ACM International Conference on Automated Software Engineering (ASE). It guides the structuring of contributions, required evidence (runnable artifacts, benchmarks), and positioning relative to core SE concepts like program analysis, testing, and AI-assisted development. Use this guide to strategize conference submissions and strengthen the academic narrative.

This guide provides expert advice on positioning, framing, and validating medical imaging AI manuscripts for the MICCAI conference. It helps authors tailor their research to meet the venue's specific requirements, focusing on clinical-task grounding, robust validation, and alignment with core scopes like computational anatomy and image-guided intervention. Essential for optimizing conference submission strategy.

This tool analyzes your entire codebase, assessing project identity, team size, test maturity, and existing architectural signals. It then recommends the most appropriate, AI-assisted development methodology stack (e.g., Microservices, Enterprise-Gov) and asks targeted questions to refine the plan, providing a concrete quick start guide.

Builds a structured audit trail of AI-assisted legal tasks. Documents prompts, uploaded materials, outputs, human verification steps, and final reliance. Helps legal teams reconstruct workflows for supervision review and later defense without inventing facts or prejudging privilege.

Audits AI-assisted legal, compliance, governance, and institutional decisions before reliance to determine whether the responsible human or institution has enough access to evidence, reasoning, uncertainty, authority, and review path to genuinely own the decision rather than merely approve, sign, or transmit it.

An advanced, AI-assisted tool for generating high-quality multiple-choice questions (MCQs) suitable for various academic levels. It supports both light, gamified quizzes and deep, evaluative assessments with detailed feedback. The system ensures factual accuracy through rigorous document research and allows export to major platforms like Moodle, Kahoot!, and Wooclap.

An attorney-facing tool for conducting knockout/availability trademark searches across the United States (USPTO/TESS) and Canada (CIPO). This structured skill guides the user through collecting required intake data and analyzing search results to produce a preliminary availability memo (green/yellow/red verdict). It is designed strictly as assisted work product for licensed attorney review, not legal advice.