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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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SKILL.md

ai-native-cli.SKILL.md
name: ai-native-cli
description: "Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description."
risk: safe
source: https://github.com/ChaosRealmsAI/agent-cli-spec
date_added: "2026-03-15"

Agent-Friendly CLI Spec v0.1

When building or modifying CLI tools, follow these rules to make them safe and reliable for AI agents to use.

Overview

A comprehensive design specification for building AI-native CLI tools. It defines 98 rules across three certification levels (Agent-Friendly, Agent-Ready, Agent-Native) with prioritized requirements (P0/P1/P2). The spec covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, self-description, and a feedback loop via a built-in issue system.

When to Use This Skill

  • Use when building a new CLI tool that AI agents will invoke
  • Use when retrofitting an existing CLI to be agent-friendly
  • Use when designing command-line interfaces for automation pipelines
  • Use when auditing a CLI tool's compliance with agent-safety standards

Core Philosophy

1. **Agent-first** -- default output is JSON; human-friendly is opt-in via `--human` 2. **Agent is untrusted** -- validate all input at the same level as a public API 3. **Fail-Closed** -- when validation logic itself errors, deny by default 4. **Verifiable** -- every rule is written so it can be automatically checked

Layer Model

This spec uses two orthogonal axes:

  • **Layer** answers rollout scope: `core`, `recommended`, `ecosystem`
  • **Priority** answers severity: `P0`, `P1`, `P2`

Use layers for migration and certification:

  • **core** -- execution contract: JSON, errors, exit codes, stdout/stderr, safety
  • **recommended** -- better machine UX: self-description, explicit modes, richer schemas
  • **ecosystem** -- agent-native integration: `agent/`, `skills`, `issue`, inline context

Certification maps to layers:

  • **Agent-Friendly** -- all `core` rules pass
  • **Agent-Ready** -- all `core` + `recommended` rules pass
  • **Agent-Native** -- all layers pass

How It Works

Step 1: Output Mode

Default is agent mode (JSON). Explicit flags to switch:

$ mycli list              # default = JSON output (agent mode)
$ mycli list --human      # human-friendly: colored, tables, formatted
$ mycli list --agent      # explicit agent mode (override config if needed)
  • **Default (no flag)** -- JSON to stdout. Agent never needs to add a flag.
  • **--human** -- human-friendly format (colors, tables, progress bars)
  • **--agent** -- explicit JSON mode (useful when env/config overrides default)

Step 2: agent/ Directory Convention

Every CLI tool MUST have an `agent/` directory at its project root. This is the tool's identity and behavior contract for AI agents.

agent/
  brief.md          # One paragraph: who am I, what can I do
  rules/            # Behavior constraints (auto-registered)
    trigger.md      # When should an agent use this tool
    workflow.md     # Step-by-step usage flow
    writeback.md    # How to write feedback back
  skills/           # Extended capabilities (auto-registered)
    getting-started.md

Step 3: Four Levels of Self-Description

1. **--brief** (business card, injected into agent config) 2. **Every Command Response** (always-on context: data + rules + skills + issue) 3. **--help** (full self-description: brief + commands + rules + skills + issue) 4. **skills \<name\>** (on-demand deep dive into a specific skill)

Certification Requirements

Each level includes all rules from the previous level. Priority tag `[P0]`=agent breaks without it, `[P1]`=agent works but poorly, `[P2]`=nice to have.

Level 1: Agent-Friendly (core -- 20 rules)

Goal: CLI is a stable, callable API. Agent can invoke, parse, and handle errors.

**Output** -- default is JSON, stable schema

  • `[P0]` O1: Default output is JSON. No `--json` flag needed
  • `[P0]` O2: JSON MUST pass `jq .` validation
  • `[P0]` O3: JSON schema MUST NOT change within same version

**Error** -- structured, to stderr, never interactive

  • `[P0]` E1: Errors -> `{"error":true, "code":"...", "message":"...", "suggestion":"..."}` to stderr
  • `[P0]` E4: Error has machine-readable `code` (e.g. `MISSING_REQUIRED`)
  • `[P0]` E5: Error has human-readable `message`
  • `[P0]` E7: On error, NEVER enter interactive mode -- exit immediately
  • `[P0]` E8: Error codes are API contracts -- MUST NOT rename across versions

**Exit Code** -- predictable failure signals

  • `[P0]` X3: Parameter/usage errors MUST exit 2
  • `[P0]` X9: Failures MUST exit non-zero -- never exit 0 then report error in stdout

**Composability** -- clean pipe semantics

  • `[P0]` C1: stdout is for data ONLY
  • `[P0]` C2: logs, progress, warnings go to stderr ONLY

**Input** -- fail fast on bad input

  • `[P1]` I4: Missing required param -> structured error, never interactive prompt
  • `[P1]` I5: Type mismatch -> exit 2 + structured error

**Safety** -- protect against agent mistakes

  • `[P1]` S1: Destructive ops require `--yes` confirmation
  • `[P1]` S4: Reject `../../` path traversal, control chars

**Guardrails** -- runtime input protection

  • `[P1]` G1: Unknown flags rejected with exit 2
  • `[P1]` G2: Detect API key / token patterns in args, reject execution
  • `[P1]` G3: Reject sensitive file paths (*.env, *.key, *.pem)
  • `[P1]` G8: Reject shell metacharacters in arguments (; | && $())

Level 2: Agent-Ready (+ recommended -- 59 rules)

Goal: CLI is self-describing, well-named, and pipe-friendly. Agent discovers capabilities and chains commands without trial and error.

**Self-Description** -- agent discovers what CLI can do

  • `[P1]` D1: `--help` outputs structured JSON with `commands[]`
  • `[P1]` D3: Schema has required fields (help, commands)
  • `[P1]` D4: All parameters have type declarations
  • `[P1]` D7: Parameters annotated as required/optional
  • `[P1]` D9: Every command
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Questions about ai-native-cli.

Which coding agents can use ai-native-cli?
ai-native-cli installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.