complex-task-methodology

What problem does it solve?

This Skill enables AI agents to dynamically assess and adapt their approach to tasks of varying complexity, preventing wasted resources on simple requests and ensuring robust planning for intricate ones.

Core Features & Use Cases

• Layered Filtering (S0-S3): From zero-cost pre-screening to phased execution with quality control.
• Dynamic Complexity Assessment: Uses rule-based filtering (S0) and a five-dimensional scoring system (S1) to determine the appropriate execution path.
• Robust Planning & Execution: Employs DAG-based planning (S2) for parallel execution and multi-stage quality assurance (S3).
• Recursive Nesting: Supports sub-agents executing the same methodology, up to 3 layers deep, ensuring consistent complexity handling across distributed tasks.
• Use Case: An AI agent receiving a request to "design a new marketing campaign" will use this Skill to first pre-screen it (S0), then assess its complexity (S1), plan the campaign phases (S2), and finally execute and monitor each phase with quality checks (S3). If a sub-task is assigned to another agent, that agent will also run its own S0-S3 assessment.

Quick Start

Use the complex-task-methodology skill to process a user request by ensuring it follows the S0-S3 layered assessment and execution framework.