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cli-agent-onboard

Profile CLI tools into reusable environment artifacts for agent evaluations.

5|Updated Mar 14, 2026
Search Tags:#automation#onboarding#cli#artifact#os-detection#runtime-detection#environment-profile
One-click install
npx skills add https://github.com/romamo/cli-agent-spec --skill cli-agent-onboard
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cli-agent-onboard
Source: https://github.com/romamo/cli-agent-spec/tree/main/skills/cli-agent-onboard
Command: npx skills add https://github.com/romamo/cli-agent-spec --skill cli-agent-onboard

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Onboard and profile a CLI tool to create reusable environment profiles for AI agent evaluations, ensuring consistent runtime, OS constraints, and non-interactive flag discovery for reuse by evaluation skills.

Core Features & Use Cases

  • Read agent-facing docs (AGENTS.md, CODING_AGENTS.md, README.md) to determine canonical invocation, env vars, and non-interactive flags.
  • Detect runtime and toolchain from common manifests (pyproject.toml/setup.py for Python, package.json for Node, Cargo.toml for Rust, go.mod for Go) and map to a preferred runner.
  • Locate and validate the target binary by attempting <cli-name> --version and --help, then resolve path for artifact generation.
  • Discover non-interactive flags and config options to enable deterministic evaluations.
  • Save a local environment artifact named <cli-name>-environment containing OS, runtime, binary, version, non-interactive flags, and relevant config env vars.

Quick Start

Provide the CLI name or path, run the onboarding skill against it, and the tool will produce a reusable <cli-name>-environment artifact for subsequent evaluations.

Frequently Asked Questions about cli-agent-onboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile a CLI tool to create a reusable environment for agent evaluations?▼

CLI onboarding reads agent-facing docs like AGENTS.md and README.md to determine canonical invocation, environment variables, and non-interactive flags. It captures runtime details and saves them as a reusable local artifact for deterministic agent evaluations.

Does CLI onboarding support runtime detection for Python, Node, Rust, and Go projects?▼

Yes, CLI onboarding detects runtimes and toolchains from common manifests including pyproject.toml and setup.py for Python, package.json for Node, Cargo.toml for Rust, and go.mod for Go. It maps these manifests to a preferred runner for artifact generation.

Can I use this to discover non-interactive flags for deterministic CLI evaluations across different operating systems?▼

Yes, you can use this to discover non-interactive flags and config options across macOS, Linux, and Windows. It performs OS constraint checks and binary resolution by attempting --version and --help commands to enable deterministic CLI evaluations.

What is the best way to save a reusable environment profile after resolving a CLI binary path?▼

The best way to save a reusable environment profile is to generate a local artifact named <cli-name>-environment. This artifact captures the resolved binary path, version, OS, runtime, non-interactive flags, and relevant config environment variables for subsequent evaluations.

Why do I need to profile CLI tools before running automated agent evaluations?▼

You need to profile CLI tools before automated agent evaluations to ensure consistent runtime environments and discover non-interactive flags. This prevents execution failures caused by OS mismatches, missing binaries, or unexpected interactive prompts during evaluation.

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