# Test CLI Usability

> Write scenario tests that verify your CLI tool is usable by AI agents. Ensures commands work non-interactively, provide clear output, and don't hang on prompts. Use when you want to prove your CLI is agent-friendly.

- Skill: `langwatch/test-cli-usability` (Agent Skill)
- Install (CLI): `npx skillmds@latest add langwatch/test-cli-usability`
- Raw SKILL.md: https://api.skillmd.com/api/skills/langwatch/test-cli-usability/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: langwatch (https://skillmd.com/u/langwatch)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/langwatch/test-cli-usability

---


# Test Your CLI's Agent Usability

This recipe helps you write scenario tests that verify your CLI tool works well when operated by AI agents (Claude Code, Cursor, Codex, etc.). A CLI that's agent-friendly means:

- All commands can run non-interactively (no stdin prompts that hang)
- Output is parseable and informative
- Error messages are clear enough for an agent to self-correct
- Help text enables discovery (`--help` works on every subcommand)

## Prerequisites

Install the Scenario SDK:

```bash
npm install @langwatch/scenario vitest @ai-sdk/openai
# or: pip install langwatch-scenario pytest
```

## Step 1: Identify Your CLI Commands

List every command your CLI supports. For each, note:

- Does it require interactive input? (MUST have a non-interactive alternative)
- What flags/options does it accept?
- What does it output on success/failure?

## Step 2: Write Scenario Tests

For each command, write a scenario test where an AI agent discovers and uses it:

```typescript
import scenario, { type AgentAdapter, AgentRole } from "@langwatch/scenario";
import { openai } from "@ai-sdk/openai";
import { describe, expect, it } from "vitest";

const myAgent: AgentAdapter = {
  role: AgentRole.AGENT,
  call: async (input) => {
    // Your Claude Code adapter here
  },
};

describe("CLI agent usability", () => {
  it("discovers and uses the command non-interactively", async () => {
    const result = await scenario.run({
      name: "CLI command discovery",
      description: "Agent discovers and uses the CLI to accomplish a task",
      agents: [
        myAgent,
        scenario.userSimulatorAgent({ model: openai("gpt-5-mini") }),
        scenario.judgeAgent({
          model: openai("gpt-5-mini"),
          criteria: [
            "Agent used the CLI command correctly",
            "Agent did not get stuck on interactive prompts",
            "Agent did not need to pipe 'yes' or use 'expect' scripting",
          ],
        }),
      ],
    });
    expect(result.success).toBe(true);
  });
});
```

## Step 3: Assert No Interactive Workarounds

Add this assertion to every test:

```typescript
function assertNoInteractiveWorkarounds(state) {
  const output = state.messages.map(m =>
    typeof m.content === 'string' ? m.content : JSON.stringify(m.content)
  ).join('\n');

  expect(output).not.toMatch(/echo\s+["']?[yY](?:es)?["']?\s*\|/);
  expect(output).not.toMatch(/\byes\s*\|/);
  expect(output).not.toMatch(/expect\s+-c/);
  expect(output).not.toMatch(/printf\s+["']\\n["']\s*\|/);
}
```

If this assertion fails, your CLI has an interactivity bug -- add `--yes`, `--force`, or `--non-interactive` flags to the offending commands.

## Step 4: Test Error Recovery

Write scenarios where the agent makes a mistake and must recover:

- Wrong command name -> agent reads `--help` and self-corrects
- Missing required argument -> agent reads error message and retries
- Authentication failure -> agent follows instructions in error output

## Common Mistakes

- Do NOT make commands that require stdin for essential operations -- always provide flag alternatives
- Do NOT use interactive prompts for confirmation without a `--yes` or `--force` flag
- Do NOT output errors without actionable guidance (the agent needs to know how to fix it)
- DO make `--help` comprehensive on every subcommand
- DO use non-zero exit codes for failures (agents check exit codes)
- DO output structured information (the agent can parse it)