Skill UI

Get a deep, multi-round, and adversarial critical review of your research work, ideas, or drafts. This skill simulates the perspective of a senior ML reviewer (e.g., NeurIPS/ICML level) using dedicated external backends (Codex or Manual). It is designed to find logical gaps, missing experiments, and narrative weaknesses, providing brutal honesty to elevate your academic contribution.

Handles IMO/Putnam/USAMO/AIME style problems by generating refined proofs, then running a fresh-context adversarial verifier to catch flaws, finally producing honest confidence reports and verified PDFs when possible.

A comprehensive Python framework utilizing deep generative models and variational inference for advanced single-cell genomics analysis. It supports the integration and analysis of diverse multi-modal data types, including single-cell RNA-seq, ATAC-seq, CITE-seq, and spatial transcriptomics. Key applications include probabilistic batch correction, cell type annotation, and multi-omics integration.

A comprehensive guide detailing five robust architectural patterns for building applications powered by Claude, including Chatbot, RAG, Agent, Content Pipeline, and Evaluation. It provides complete code examples and decision matrices to help developers select the optimal architecture based on project requirements, cost, and complexity.

This guide provides comprehensive, step-by-step instructions for installing and authenticating the Anthropic SDK. It covers setup for both Python and Node.js environments, detailing how to securely configure API keys using environment variables. Essential for developers starting new projects that integrate with Claude models.

A comprehensive guide for setting up and authenticating access to the Fathom AI API. This skill walks users through generating API keys, configuring environment variables, and testing connectivity to retrieve meeting transcripts, summaries, and action items. Essential for integrating Fathom's meeting intelligence features into custom applications.

This guide provides comprehensive instructions for installing and configuring the official Anthropic SDK for both Python and TypeScript. It covers setting up the necessary environment variables, managing API keys securely, and verifying the connection to the Claude Messages API endpoint. Essential for all new Claude API integrations.

This comprehensive guide details the process of installing and configuring the Cohere SDK (v2) in both Node.js and Python environments. It covers initial setup, setting API keys via environment variables, and provides full code examples for verifying connectivity across various Cohere endpoints, including Chat, Embed, and Rerank, enabling immediate integration into your AI projects.

This comprehensive guide details how to install and properly configure the ElevenLabs SDK for both Node.js and Python environments. It covers setting up the required API keys using environment variables, initializing the client connection, and verifying the setup by listing available voices. Use this skill when starting a new project that requires advanced text-to-speech (TTS) or voice synthesis capabilities.

This tool audits the integrity of machine learning experiments by checking for common fraud patterns such as fake ground truth, score normalization fraud, and phantom results. It utilizes an external reviewer backend (like Codex or manual review) to perform an adversarial review across all project artifacts, ensuring reported results are trustworthy and methodologically sound.

A comprehensive offensive checklist for assessing the security and robustness of AI and Large Language Model (LLM) applications. It covers advanced adversarial techniques such as prompt injection, jailbreaking, model extraction, data poisoning, and analyzing system vulnerabilities across components. Essential for red-teaming and security assessment.

This skill performs rigorous mathematical proof verification using an adversarial, cross-model review process. It reads a LaTeX proof, systematically identifies logical gaps (e.g., unjustified assertions, quantifier errors) and theoretical flaws (e.g., illegal interchanges, non-uniform convergence). It then iteratively fixes these gaps with full derivations, re-reviews, and generates a comprehensive audit report, ensuring mathematical rigor and completeness. Use this when seeking deep, academic verification of a theory paper.