Generate, validate, and visualize multi-agent topology .at files.
npx skills add https://github.com/agentopology/agentopology --skill agentopology
Please help me install this Agent Skill. Skill: agentopology Source: https://github.com/agentopology/agentopology/tree/main/.claude/skills/agentopology-skill Command: npx skills add https://github.com/agentopology/agentopology --skill agentopology
Coordinating multiple AI agents across platforms is complex and error-prone, requiring manual configuration, scattered files, and inconsistent tooling across environments.
Describe your topology goal and let the system generate, validate, and scaffold the .at file.
High-intent search queries and answers about installing and using this skill.
To scaffold multi-agent topology configurations, you can generate a .at file encoding your agent structure and validate it to automatically output platform configs for environments like Claude Code, OpenClaw, Codex, and Cursor.
The 29 validation rules are a strict set of constraints applied to your .at files via a CLI loop to ensure your multi-agent topology structure is structurally sound and ready for platform scaffolding.
Yes, you can visualize dependencies for a 2-6 agent architecture. The system automatically generates topology graphs from your .at files to clearly communicate architecture to stakeholders.
Yes, the tool works without external dependencies. It operates entirely via a strict CLI loop using internal scripts to query syntax, validate rules, and generate your .at files on demand.
Manual multi-agent coordination fails due to scattered files, inconsistent tooling, and error-prone manual configuration across different environments. Automating generation and validation solves this.
batFormatExecute multi-iteration Reflex Loops with PLAN, PRODUCE, EVALUATE, CRITIQUE, and REFINE phases.
renan-garciaMigrate AI agent configs into a unified .agents/ directory structure.
nopperabboValidate Bash and POSIX sh scripts with syntax and ShellCheck analysis.
hitoshisekiCreate and validate multi-agent AI squads for the AIOX ecosystem.
