Skill UI

A comprehensive tool for designing, generating schemas, and evaluating complex multi-agent systems. It guides users through defining system requirements, selecting optimal orchestration patterns (Supervisor, Swarm, Pipeline), generating provider-ready tool schemas (Anthropic/OpenAI), and analyzing execution logs for performance bottlenecks, cost, and latency. This ensures robust, production-grade autonomous workflows.

This framework provides a comprehensive, structured approach for making critical architectural decisions. It guides users through requirements analysis, detailed trade-off evaluation, and documenting decisions using Architecture Decision Records (ADRs). It is essential when analyzing or designing complex system architectures to ensure simplicity, robustness, and maintainability.

Guides CTOs and tech leaders through strategy, architecture governance, team scaling, and crisis response, with workflows for technical debt assessment, ADR creation, and vendor evaluation to keep engineering healthy.

Enterprise NeMo Evaluator SDK runs over 100 benchmarks from 18+ harnesses with multi-backend execution (Docker, Slurm, cloud) to give reproducible, scalable LLM evaluation workflows.

Provides a comprehensive workflow for managing feature flags, running A/B/multivariate experiments, and analyzing user cohorts using PostHog. The guide covers implementing flag evaluation both client-side (browser) and server-side (Node.js), and demonstrates how to programmatically set up, track, and query experiment results via API calls (cURL/Bash).

Guidelines for reviewing UI code against web interface best practices, covering accessibility, performance, responsive design, component patterns, CSS practices, security, and internationalization during audits or UX evaluations.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

Guides Odoo users through configuring FIFO/AVCO valuation, reordering rules, putaway strategies, warehouse routes and multi-step flows to ensure accurate stock levels and traceable receipts/shipments.

A comprehensive, canonical workflow for defining, writing, and improving complex AI skills. It guides users through a structured, multi-step process, including resolving targets, synthesizing information, enforcing depth gates, authoring artifacts, optimizing descriptions, and conducting thorough evaluation and validation.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

This skill initializes a new autoresearch experiment. It guides the user through defining all necessary parameters, including the domain, target files, evaluation commands, metrics, and optimization direction. It is the essential starting point for automated optimization and performance benchmarking cycles.