Skill UI

Reasoning-Driven Image Briefs skill transforms agent prompts into logic-based Gemini 3 style instructions for muapi.ai, adding structured creative briefs (subject/action/context/composition/lighting), precise text cues, and physics-grounded constraints before invoking generation scripts.

Combines ESM3 and ESM C protein language models to generate sequences, predict structures, condition functions, and extract embeddings via local or Forge API inference for protein design and engineering workflows.

AgentMail provides a comprehensive REST API email infrastructure specifically designed for AI agents. Agents can provision dedicated inboxes, send and receive emails, handle external service verifications (e.g., GitHub signups), and manage real-time inbound webhooks. The system includes a 'karma' mechanism to ensure high domain reputation and prevent abuse, making it reliable for complex automation workflows.

This comprehensive guide provides step-by-step instructions for integrating the Google Gemini API into applications using Python and Node.js. It covers the entire lifecycle, from basic text generation to advanced use cases like multimodal inputs (text + image), real-time streaming responses, function calling/tool use, and multi-turn chat. Includes best practices for model selection, error handling, and production deployment.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.

An advanced reliability wrapper for AI agents that monitors, manages, and recovers from tool call failures. It automatically handles common issues such as truncated JSON responses, API timeouts, rate limits, and mid-chain process breakdowns. By implementing sophisticated failure classification, checkpointing, and learning mechanisms, Guardian ensures complex, multi-step agent workflows remain stable and resilient.

This skill executes an autonomous, iterative research review loop, simulating a rigorous peer-review process. It repeatedly uses an external OpenAI-compatible LLM via API calls to score, critique, and identify weaknesses in a given research topic or document. The process continues to refine the content by implementing suggested fixes until the LLM provides a positive assessment or the maximum round limit is reached. Ideal for academic writing, technical documentation, and refining complex proposals.

Uses the Shodan and InternetDB APIs in Python to enrich IP addresses, detail open ports/services, pull CVEs, and compute reputation scores to support threat triage and SOC workflows.

This skill systematically probes an AI model's core behavioral patterns across six critical dimensions, including reasoning, refusal, formatting, and grounding. It generates a comprehensive, styled HTML report with radar charts and actionable insights, allowing users to understand a model's true capabilities, compare different models, or perform compliance and red-team assessments without needing an external API key.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.

Explains how to build LLM-powered apps using Claude, the Anthropic SDKs, or Agent SDK—covering language detection, recommended surfaces, and default model/tooling choices for reliable integrations.

A comprehensive tool and API wrapper for accessing, parsing, and analyzing research papers published on Hugging Face and arXiv. Users can input various URLs or IDs (e.g., arXiv ID) to retrieve structured metadata, full markdown content, and automatically link associated resources such as models, datasets, and application spaces. Ideal for researchers and developers needing structured access to cutting-edge AI knowledge.