Skill UI

An autonomous factory for generating production-ready, cross-platform AI agent skills. It accepts raw, unstructured human input—such as transcripts, PDFs, screenshots, vague ideas, or code snippets—and reverse-engineers the complete workflow logic. It bridges the gap between human intent and functional agent specifications, allowing users to automate complex processes without writing code.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

This technique helps locate video games when the primary information provided is not the game title, but rather indirect clues about the game's developing company. Clues can include the company's name meaning, founder background, corporate history, or foundation activities. You must first decode these clues to identify the company, and then search the company's associated game catalog.

Master building robust AI workflows with Anthropic's core APIs. This guide details two powerful patterns: real-time streaming via SSE events for interactive chat UIs, and the cost-efficient Message Batches API for asynchronously processing up to 100,000 requests. Includes comprehensive code examples for Python and TypeScript.

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.

This comprehensive guide serves as a quick reference for diagnosing and resolving common Cohere API v2 errors and exceptions. It covers specific error codes (400, 429, 401, etc.), details the root causes, and provides actionable code fixes for various tasks, including Chat, Embed, and Rerank. Use this when debugging failed Cohere requests or encountering rate limits.

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 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.

Lambda is a compact, machine-native communication protocol specifically designed for agent-to-agent (A2A) messaging. It functions as a shared, unambiguous vocabulary built from 'atoms' (2-character codes), completely bypassing the need for human natural language interpretation. It is ideal for orchestrators, task delegation, and logging structured coordination signals (like heartbeats or state changes) in multi-agent systems. Crucially, Lambda is intended only for machine communication, not for user-facing dialogue.