strands
Build and run Python-based AI agents using the AWS Strands SDK. Use when you need to create autonomous agents, multi-agent workflows, custom tools, or integrate with MCP servers. Supports Ollama (local), Anthropic, OpenAI, Bedrock, and other model providers. Use for agent scaffolding, tool creation, and running agent tasks programmatically.
Why use this skill?
Develop autonomous Python agents with the Strands SDK. Support for Bedrock, Anthropic, OpenAI, and Ollama. Create custom tools and workflows easily.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/trippingkelsea/strandsWhat This Skill Does
The Strands skill provides a comprehensive interface for the AWS Strands SDK, enabling developers to build, orchestrate, and run autonomous AI agents directly from the OpenClaw environment. It simplifies the complexity of multi-agent workflows, tool-calling schema generation, and cross-provider model integration. Whether you are leveraging local Ollama instances for privacy or high-performance cloud models via Amazon Bedrock, Anthropic, or OpenAI, this skill acts as the bridge between your logic and the agent orchestration layer.
Installation
To integrate this skill into your OpenClaw environment, use the following command: clawhub install openclaw/skills/skills/trippingkelsea/strands
Ensure you have the necessary system dependencies installed:
- pipx install strands-agents-builder
- Alternatively: pip install strands-agents strands-agents-tools
Use Cases
- Multi-Agent Orchestration: Design complex workflows where agents delegate tasks to specialized sub-agents.
- Custom Tool Integration: Wrap local functions or system commands with the @tool decorator to give your agents read/write filesystem capabilities or external API access.
- Model Agnostic Development: Switch seamlessly between local open-source models (like qwen3 or llama3) and enterprise-grade models (like Claude Sonnet 4 or GPT-4) without changing your core application logic.
- Automated Pipeline Construction: Build agents that execute sequences of tasks, such as reading files, processing data, and writing results back to the filesystem.
Example Prompts
- "Initialize a new Strands agent using local Ollama with the qwen3 model and give it a read_file tool to analyze logs."
- "Create a multi-agent workflow where one agent gathers information from the web and another agent summarizes it using the Bedrock provider."
- "Build a tool-enabled agent that scans my current directory and writes a summary report of all Python files found."
Tips & Limitations
- Default Provider: Be aware that omitting the model argument defaults to AWS Bedrock in us-west-2, which requires pre-configured AWS credentials.
- Tool Compatibility: When using local models (Ollama), ensure the chosen model supports function calling; not all open-source models (especially abliterated ones) maintain this capability.
- Environment Variables: Always ensure your API keys (e.g., ANTHROPIC_API_KEY, OPENAI_API_KEY) are securely stored in your environment before initializing providers.
- Security: Because this skill allows for filesystem interaction and code execution, restrict agent access to sensitive system directories when running custom tools.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-trippingkelsea-strands": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, file-write, file-read, external-api, code-execution