Design agent-first CLIs with HATEOAS JSON responses and self-documenting command trees.
npx skills add https://github.com/joelhooks/joelclaw --skill cli-design-joelhooks
Please help me install this Agent Skill. Skill: cli-design Source: https://github.com/joelhooks/joelclaw/tree/main/.agents/skills/cli-design Command: npx skills add https://github.com/joelhooks/joelclaw --skill cli-design-joelhooks
This Skill provides a robust framework and set of principles for designing and building command-line interfaces (CLIs) that are optimized for agent interaction, ensuring structured, predictable, and actionable output.
next_actions for seamless agent integration.joelclaw, slog, or igs) to be easily consumable by AI agents, ensuring consistent and predictable interactions.Use the cli-design skill to build a new CLI tool with agent-friendly output.
High-intent search queries and answers about installing and using this skill.
Designing a CLI for AI agent interaction requires structured JSON output, HATEOAS-style next_actions for discoverability, and context-protecting truncation to respect context windows. This framework provides the principles to build self-documenting command trees optimized for machine consumption.
HATEOAS JSON responses in command-line interfaces provide machine-readable next_actions links within the output, allowing agents to discover available commands dynamically. This structured approach ensures seamless agent integration by making the command tree self-documenting and predictable.
Handling large CLI outputs without exceeding agent context windows requires context-protecting output that truncates data and provides pointers to full information. This approach ensures agents receive actionable summaries without being overwhelmed by excessive payload sizes.
Yes, you can add agent-friendly commands to an existing CLI tool by applying principles for structured JSON output, discoverable command trees, and standardized error responses. This framework supports enhancing current tools to ensure consistent and predictable agent interactions.
The best way to structure CLI error responses for AI agents is to include both machine-readable error codes and human-readable fixes. This standardized format allows agents to programmatically handle failures while providing actionable guidance for resolution.
Your CLI tool may fail when parsed by automated agents if it lacks structured JSON output, relies on unstructured text, or omits discoverable command trees. Redesigning it with agent-first principles ensures predictable, actionable responses optimized for machine interaction.
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