Send prompts to NanoClaw agent containers via a Python CLI.
npx skills add https://github.com/thmtz/nanoclaw-fleet --skill claw-thmtz
Please help me install this Agent Skill. Skill: claw Source: https://github.com/thmtz/nanoclaw-fleet/tree/main/.claude/skills/claw Command: npx skills add https://github.com/thmtz/nanoclaw-fleet --skill claw-thmtz
CLI-based access to manage NanoClaw agent containers, enabling prompt delivery without a chat app.
Run claw to interact with NanoClaw agent containers from the terminal.
High-intent search queries and answers about installing and using this skill.
You can send prompts to agent containers from the terminal using a Python CLI that delivers messages directly to NanoClaw deployments. It bypasses chat applications by routing prompts and printing agent responses to stdout for immediate scripting use.
Yes, you can pipe prompts from stdin to automate NanoClaw agent interactions. The CLI supports reading prompts directly from standard input, allowing you to seamlessly integrate automated prompt delivery into your existing shell scripts and workflows.
Targeting specific agent groups by name, folder, or JID is handled directly through CLI arguments. You can specify the desired group identifier when executing the command to ensure the prompt reaches the correct NanoClaw agent container.
Yes, the claw CLI auto-detects the container runtime to ensure seamless scripting. It automatically identifies the available runtime environment, removing the need for manual configuration when executing local prompts against NanoClaw agent containers.
Running a Python CLI for NanoClaw agent containers requires Python 3.8 or later, an active NanoClaw deployment with a tagged agent image, and access to the NanoClaw database. These components ensure reliable local execution and accurate error reporting.
You can resume previous sessions with NanoClaw agents from the command line using the CLI's session resume feature. This allows you to continue interactions with a targeted agent container exactly where the previous conversation left off.
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