Define AI-friendly CLI specs with JSON output and safety guardrails.
npx skills add https://github.com/caobingsheng/skills --skill ai-native-cli
Please help me install this Agent Skill. Skill: ai-native-cli Source: https://github.com/caobingsheng/skills/tree/main/ai/ai-native-cli Command: npx skills add https://github.com/caobingsheng/skills --skill ai-native-cli
Designing AI-friendly CLIs that are safe, predictable, and discoverable requires a formal design spec that enforces safety, consistency, and discoverability.
Scaffold an AI-friendly CLI spec to enforce safe defaults, structured JSON responses, and clear self-description.
High-intent search queries and answers about installing and using this skill.
An AI-friendly CLI design specification enforces safety, predictability, and discoverability for command-line tools. You need it to ensure AI agents can reliably parse default JSON outputs, handle stable error contracts, and understand explicit help surfaces without guessing.
To build a CLI with stable error handling and JSON output, apply a structured safety and usability spec that establishes consistent output contracts, explicit flags, and guardrails. This ensures AI agents receive predictable, machine-readable responses across all commands.
The AI-native CLI specification supports three certification layers: Agent-Friendly, Agent-Ready, and Agent-Native. Each layer progressively enforces stricter requirements for output formats, self-description capabilities, and the built-in agent and skills ecosystem.
An AI-friendly CLI requires a disciplined file structure comprising agent/, rules/, and skills/ directories. This structure enables explicit self-description, organizes guardrails, and supports a built-in agent and skills ecosystem for discoverability.
You can apply this CLI specification to add guardrails and safety constraints by enforcing explicit flags, stable error contracts, and consistent JSON output. It provides structured guidance for making existing command-line tools safe and predictable for AI agents.
A structured CLI spec for AI agent interaction requires strict adherence to default JSON output, stable error contracts, and a disciplined file structure. Tools that cannot enforce these explicit flags and safety constraints will not pass certification.
vecearAudit API endpoints and apply validation schemas to prevent injection vulnerabilities.
civitas-cerebrumEvaluate adversarial AI behavior across triad interactions to uncover guardrail weaknesses.
coco-researchWrap command-line tools into agent skills with documented invocation and error contracts.
labs21-devThe provided code snippet does not contain enough information to determine authorship.
