Skill UI

Design multi-agent system architectures using patterns like supervisor, swarm, and pipeline. Generate provider-ready tool schemas for Anthropic and OpenAI. Evaluate execution logs to identify bottlenecks, latency, and costs. Use for complex agent workflows requiring structured design and optimization, not simple automation scripts.

This guide details advanced architectural patterns for building complex AI systems using multiple Language Model instances (agents). It addresses the single-agent context bottleneck by distributing work, enabling parallelism, and facilitating specialization across different subtasks. Key patterns include Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical designs, providing guidelines for context isolation and effective coordination protocols.

A systematic, non-mutating investigation designed for complex software failures, including bugs, regressions, crashes, and flaky behavior. This skill launches four specialized, read-only sub-agents in parallel to perform deep root-cause analysis, trace code paths, pinpoint the smallest failing seam, and predict the quickest path to confirming or fixing the issue. It is designed purely for diagnosis and hypothesis generation.

This skill performs a comprehensive, read-only, multi-agent review of a git diff or file scope. It launches four specialized sub-agents—covering Regression, Security, Performance, and Contracts—in parallel. The main agent then aggregates, filters, and summarizes critical findings, ensuring the code change is robust, secure, and maintainable. Ideal for pre-commit or Pull Request review workflows.

This utility executes an autonomous, iterative loop, simulating a comprehensive agent workflow. It systematically manages tasks by checking current status, predicting the optimal next action using specialized tools, executing the required task (e.g., coding, testing), logging the progress, and scheduling the next iteration. It supports task discovery from multiple sources, including team tasks, swarm tasks, and file checklists, ensuring continuous, automated progress towards a defined goal.

Uses the Monitor tool to stream real-time events from a swarm system instead of inefficient polling. It provides live observability for key actions like agent spawns, task completions, memory writes, and health status. This is ideal for continuous monitoring and debugging of complex, distributed processes.

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.

Ruflo is a powerful, cross-agent orchestration layer designed to manage complex, multi-step AI coding workflows. It supports coordinating large swarms of AI agents, managing persistent memory through semantic search, and executing sophisticated pipelines using 30+ specialized plugins. Ideal for large-scale development tasks, system benchmarking, and deep research requiring coordinated agent effort.

Multi-agent decision swarm that activates 20 legendary Tianya forum experts in parallel for a user objective. A coordinator assigns the task, each expert provides specialized insights, and a synthesis agent merges consensus and disagreements into a structured report with risks, recommendations and action plans.

This skill enables parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, or unexplained failures. It deploys four specialized sub-agents to analyze reproduction, code paths, recent changes, and proof plans simultaneously. The main agent synthesizes findings into ranked hypotheses without editing files. Ideal for diagnosing complex software issues efficiently.

This skill covers designing multi-agent systems to overcome single-agent context limits. It includes patterns like supervisor/orchestrator, swarm, and hierarchical structures. Key concepts involve context isolation, parallel execution, token economics, and specialization for complex tasks.

Review supervisor, swarm, planner-worker and parallel agent workflows as distributed state machines: task ownership, shared state, branch joins, retries, cancellation, context handoffs, budgets, deadlocks and human escalation before rollout.