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Conception de CLI pour agents IA (98 règles)

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Spécification technique comprenant 98 règles pour la création d'outils en ligne de commande (CLI) que les agents IA peuvent utiliser en toute sécurité

sickn33sickn33
45.0k
22 mai 2026
MIT License
// contenu du skill

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:

bash
$ 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:
// source originale publique
sickn33/antigravity-awesome-skills
/skills/ai-native-cli/SKILL.md
Licence : MIT License
Projet indépendant, non affilié à Anthropic. Ce skill reste la propriété de son auteur original.
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mkdir -p .claude/commands && curl -o ".claude/commands/SKILL.md" "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/ai-native-cli/SKILL.md"
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// informations
Créateursickn33
Étoiles 45.0k
LicenceMIT License
Mis à jour22 mai 2026
Format.md
AccèsGratuit
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