ai-native-cli
Design spec with 98 rules for building CLI tools that AI agents can safely use.
Skill by manu14357
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Use cases: design, agent-building
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What's inside
- 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.
- License
- MIT
- Instructions
- 309 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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: Type mismatch -> exit 2 + structured error
**Safety** -- protect against agent mistakesFrom manu14357/zskills, MIT.
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Signals
- Stars
- 16
- Forks
- 2
- Open issues
- 0
- Last commit (this folder)
- May 6, 2026 (5 months ago)
- Last release
- Apr 26, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/manu14357/zskills
- Website
- zskills.vercel.app
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
ai-native-cli skill. Agent Mart. https://agentmart.dev/skills/manu14357-ai-native-cli (last updated 2026-10-24)