What problem does it solve?
AI agents operating within the AIASys platform lack built-in context for its core concepts, tool ecosystem, environment management rules, and native feature workflows, leading to incorrect tool selection, failed task execution, and inability to leverage platform-specific capabilities like persistent workspaces, Auto Task loops, and knowledge management tools.
Core Features & Use Cases
- Comprehensive Platform Context: Detailed explanations of core AIASys concepts including workspaces, sessions, agents, skills, and Auto Task, so agents understand the platform's architecture and operating principles.
- Full Agent Tool Reference: Complete catalog of all built-in agent tools with usage guidelines, helping agents select the right tool for file operations, code execution, data querying, knowledge management, and task automation.
- Workflow Coordination Guides: Step-by-step instructions for common platform workflows including continuous Auto Task setup for long-running experiments, UV environment and Docker sandbox management, environment variable configuration, and multi-skill collaboration for research and data analysis tasks.
- Use Case Example: An agent building an automated competition research pipeline can use this skill to correctly configure continuous Auto Task for iterative experiment running, set up the required runtime environment for code execution, and coordinate research, PDF processing, and knowledge graph skills end-to-end.
Quick Start
Instruct your AI agent to load the AIASys Platform Guide skill to identify the correct tool and steps for setting up a scheduled automated task to process uploaded CSV files in your workspace.
