Skill UI

This skill defines structured behavioral frameworks (e.g., Brainstorm, Implement, Debug, Review, Teach, Ship) to guide the AI's output and approach based on the specific task phase. It optimizes performance by ensuring the AI adopts the correct mindset and output format—from initial ideation to final deployment—guaranteeing systematic, high-quality results across the entire development lifecycle.

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

Designs systematic ablation studies for ML paper submissions. Helps isolate component contributions, test hyperparameters, and answer reviewer questions. Codex plans experiments while CC implements them, ensuring rigorous validation before submission.

This skill orchestrates the complete academic research workflow from initial exploration to final manuscript. It coordinates deep research, paper writing, and review processes into a seamless 10-stage pipeline. It manages transitions, tracks state, and ensures integrity through mandatory checks and peer reviews. Suitable for end-to-end paper generation and systematic research management.

A domain-agnostic 13-agent pipeline for rigorous academic research on any topic. Eight modes cover full research, quick briefs, paper review, literature review, fact-checking, three-way literature scans, Socratic research dialogue, and systematic review with optional meta-analysis, from question formulation to APA 7.0 reporting.

A comprehensive audit tool designed to ensure academic paper compliance before submitting to major AI conferences like NeurIPS. It systematically checks adherence to the CFP, double-blind anonymity across all assets (PDF, code, data, supplement), correct track selection, and avoids common desk-rejection pitfalls, ensuring the submission is ready for OpenReview.

A comprehensive skill designed to transform contested questions or complex decisions into a structured, auditable evidence map. It systematically tracks all supporting, contradicting, qualifying, and missing evidence, ensuring that reasoning relationships and source regions are preserved for rigorous review.

A skill that models AI educator Baoyu's approach to creating accessible AI content, systematic prompt engineering teaching (Level 1-10), tool reviews, open-source sharing, and daily knowledge updates. Ideal for writing tutorials, translating tech articles, and building AI knowledge bases without hype or anxiety.