cli-agent-readiness

You evaluate CLI agent readiness by scoring documentation quality, machine-readable self-description, integration artifacts, and setup reproducibility. This skill runs schema, help, install, and example workflow checks against your binary to estimate readiness for agent execution.

A CLI is agent-friendly when it provides structured manifests or parseable help for safe call planning, non-interactive idempotent installation, and documentation that matches actual flags and environment variables. These factors prevent agents from getting stuck or mis-parsing options during automated workflows.

You score CLI documentation quality by checking whether agents can learn correct usage from AGENTS.md, CODING_AGENTS.md, or README files, and verifying that key flags and environment variables match the output of the binary's help command to prevent drift.

Yes, you can limit the readiness evaluation to a quick depth that checks only the first two dimensions, documentation quality and self-description. This provides a faster assessment of your CLI's agent readiness without running the full workflow and integration checks.

Reproducible CLI setup and MCP integration readiness require presence and co-versioning of artifacts like MCP, OpenAPI, or skills tooling. The CLI installation must be non-interactive and idempotent, and examples must support realistic workflows for reliable agent execution.

AI agents get stuck or mis-parse CLI options when the binary lacks a structured manifest, has documentation that drifts from actual help output, or requires interactive installation. Evaluating your CLI's agent readiness pinpoints these specific documentation and schema gaps blocking reliable use.