Skill UI

This skill covers the comprehensive architecture design and construction of autonomous AI agents. Experts master advanced concepts including tool use, hierarchical memory systems (episodic, semantic, working), sophisticated planning strategies (ReAct, Plan-and-Execute), and multi-agent orchestration. It focuses on building systems that achieve high levels of autonomy while maintaining critical human oversight, designing for graceful degradation and rigorous error handling.

This skill covers the comprehensive architecture design and construction of autonomous AI agents. Experts master advanced concepts including tool use, hierarchical memory systems (episodic, semantic, working), sophisticated planning strategies (ReAct, Plan-and-Execute), and multi-agent orchestration. It focuses on building systems that achieve high levels of autonomy while maintaining critical human oversight, designing for graceful degradation and rigorous error handling.

Guides building reliable autonomous agents by covering agent loops, goal decomposition, reflection, and tooling patterns while stressing guardrails, human review, and auditability to curb compounding failure rates in production.

This advanced workflow uses Claygent AI to perform deep research on target companies by scraping websites. It extracts critical data points such as funding rounds, tech stacks, and potential pain points. These insights are then leveraged to generate highly personalized, human-sounding email openers at scale, ensuring high relevance for targeted outreach campaigns.

Guides setting up Constitutional AI by combining self-critique SL with RLAIF training to align generative models without human labels and reduce harmful outputs in systems like Claude.

A comprehensive toolkit for building production-grade AI agents focused on crypto business development. This skill guides users through the entire token listing lifecycle: autonomously discovering promising assets across multiple chains, applying a weighted 100-point scoring model, verifying safety via advanced wallet forensics, and managing outreach pipelines with necessary human oversight and LLM cascade patterns.

An elite AI-powered specialist mastering end-to-end customer support. This skill guides the implementation of advanced conversational AI, automated ticketing systems, and comprehensive omnichannel strategies. It covers sentiment analysis, knowledge base optimization, e-commerce support, and advanced CX analytics, ensuring seamless, human-centered, and efficient customer journeys across all platforms.

This guide provides comprehensive templates and best practices for structuring all types of technical documentation. It covers essential formats including README files, REST API endpoints, code comments (JSDoc), Changelogs, Architecture Decision Records (ADR), and AI-optimized documentation. Use these templates to ensure your project documentation is standardized, scannable, and easily consumable by both human developers and AI agents.

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

Evaluates large language models across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with standardized prompts and metrics, making it easy to benchmark HuggingFace or API models, compare releases, and track training progress.

A comprehensive workflow for implementing advanced search and retrieval features within Evernote. It allows users to build complex queries using search grammar, filter notes by tags, notebooks, dates, and completion status, and paginates through large result sets. The service also enriches results by mapping GUIDs to human-readable names for notebooks and tags.

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.