strands-spec

What problem does it solves? This Skill eliminates the complexity of manually writing and debugging strands-cli workflow specifications (YAML/JSON) for AI agent orchestration. It provides expert guidance, reduces errors, and significantly cuts down development time, especially for complex, multi-agent systems. It also optimizes LLM context usage through a progressive loading architecture.

Core Features & Use Cases

• Progressive Loading: Dynamically loads only relevant documentation, drastically reducing token usage and improving response times for AI agents.
• Comprehensive Spec Generation: Guides users through defining multi-step agent workflows, configuring tools, setting up runtime providers (Bedrock, OpenAI, Ollama), and handling inputs/outputs.
• Advanced Troubleshooting: Offers detailed solutions for common schema validation errors, runtime issues, and performance bottlenecks, ensuring robust workflow deployment.
• Use Case: A developer needs to build a complex data analysis pipeline using multiple AI agents with specific dependencies. This skill guides them step-by-step to generate a valid strands-cli YAML spec, including tool integration and budget management, without needing to memorize the entire schema.

Quick Start

Use the strands-spec skill to create a data analysis workflow with parallel processing and a custom Python tool. Ensure the spec includes Bedrock as the provider and a budget of 100,000 tokens.