ai-native-cli

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.

Best use case

ai-native-cli is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using ai-native-cli should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/ai-native-cli/SKILL.md --create-dirs "https://raw.githubusercontent.com/ratnesh-maurya/cursor-claude-personas/main/ai-agent-developer/.claude/skills/ai-native-cli/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/ai-native-cli/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How ai-native-cli Compares

Feature / Agentai-native-cliStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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 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 has a description
- `[P1]` D11: `--help` outputs JSON with help, rules, skills, commands
- `[P1]` D15: `--brief` outputs `agent/brief.md` content
- `[P1]` D16: Default JSON (agent mode), `--human` for human-friendly
- `[P2]` D2/D5/D6/D8/D10: per-command help, enums, defaults, output schema, version

**Input** -- unambiguous calling convention
- `[P1]` I1: All flags use `--long-name` format
- `[P1]` I2: No positional argument ambiguity
- `[P2]` I3/I6/I7: --json-input, boolean --no-X, array params

**Error**
- `[P1]` E6: Error includes `suggestion` field
- `[P2]` E2/E3: errors to stderr, error JSON valid

**Safety**
- `[P1]` S8: `--sanitize` flag for external input
- `[P2]` S2/S3/S5/S6/S7: default deny, --dry-run, no auto-update, destructive marking

**Exit Code**
- `[P1]` X1: 0 = success
- `[P2]` X2/X4-X8: 1=general, 10=auth, 11=permission, 20=not-found, 30=conflict

**Composability**
- `[P1]` C6: No interactive prompts in pipe mode
- `[P2]` C3/C4/C5/C7: pipe-friendly, --quiet, pipe chain, idempotency

**Naming** -- predictable flag conventions
- `[P1]` N4: Reserved flags (--agent, --human, --brief, --help, --version, --yes, --dry-run, --quiet, --fields)
- `[P2]` N1/N2/N3/N5/N6: consistent naming, kebab-case, max 3 levels, --version semver

**Guardrails**
- `[P1]` I8/I9: no implicit state, non-interactive auth
- `[P1]` G6/G9: precondition checks, fail-closed
- `[P2]` G4/G5/G7: permission levels, PII redaction, batch limits

#### Reserved Flags

| Flag | Semantics | Notes |
|------|-----------|-------|
| `--agent` | JSON output (default) | Explicit override |
| `--human` | Human-friendly output | Colors, tables, formatted |
| `--brief` | One-paragraph identity | For sync into agent config |
| `--help` | Full self-description JSON | Brief + commands + rules + skills + issue |
| `--version` | Semver version string | |
| `--yes` | Confirm destructive ops | Required for delete/destroy |
| `--dry-run` | Preview without executing | |
| `--quiet` | Suppress stderr output | |
| `--fields` | Filter output fields | Save tokens |

### Level 3: Agent-Native (+ ecosystem -- 19 rules)

Goal: CLI has identity, behavior contract, skill system, and feedback loop. Agent can learn the tool, extend its use, and report problems -- full closed-loop collaboration.

**Agent Directory** -- tool identity and behavior contract
- `[P1]` D12: `agent/brief.md` exists
- `[P1]` D13: `agent/rules/` has trigger.md, workflow.md, writeback.md
- `[P1]` D17: agent/rules/*.md have YAML frontmatter (name, description)
- `[P1]` D18: agent/skills/*.md have YAML frontmatter (name, description)
- `[P2]` D14: `agent/skills/` directory + `skills` subcommand

**Response Structure** -- inline context on every call
- `[P1]` R1: Every response includes `rules[]` (full content from agent/rules/)
- `[P1]` R2: Every response includes `skills[]` (name + description + command)
- `[P1]` R3: Every response includes `issue` (feedback guide)

**Meta** -- project-level integration
- `[P2]` M1: AGENTS.md at project root
- `[P2]` M2: Optional MCP tool schema export
- `[P2]` M3: CHANGELOG.md marks breaking changes

**Feedback** -- built-in issue system
- `[P2]` F1: `issue` subcommand (create/list/show)
- `[P2]` F2: Structured submission with version/context/exit_code
- `[P2]` F3: Categories: bug / requirement / suggestion / bad-output
- `[P2]` F4: Issues stored locally, no external service dependency
- `[P2]` F5: `issue list` / `issue show <id>` queryable
- `[P2]` F6: Issues have status tracking (open/in-progress/resolved/closed)
- `[P2]` F7: Issue JSON has all required fields (id, type, status, message, created_at, updated_at)
- `[P2]` F8: All issues have status field

## Examples

### Example 1: JSON Output (Agent Mode)

```bash
$ mycli list
{"result": [{"id": 1, "title": "Buy milk", "status": "todo"}], "rules": [...], "skills": [...], "issue": "..."}
```

### Example 2: Structured Error

```json
{
  "error": true,
  "code": "AUTH_EXPIRED",
  "message": "Access token expired 2 hours ago",
  "suggestion": "Run 'mycli auth refresh' to get a new token"
}
```

### Example 3: Exit Code Table

```
0   success         10  auth failed       20  resource not found
1   general error   11  permission denied 30  conflict/precondition
2   param/usage error
```

## Quick Implementation Checklist

Implement by layer -- each phase gets you the next certification level.

**Phase 1: Agent-Friendly (core)**
1. Default output is JSON -- no `--json` flag needed
2. Error handler: `{ error, code, message, suggestion }` to stderr
3. Exit codes: 0 success, 2 param error, 1 general
4. stdout = data only, stderr = logs only
5. Missing param -> structured error (never interactive)
6. `--yes` guard on destructive operations
7. Guardrails: reject secrets, path traversal, shell metacharacters

**Phase 2: Agent-Ready (+ recommended)**
8. `--help` returns structured JSON (help, commands[], rules[], skills[])
9. `--brief` reads and outputs `agent/brief.md` content
10. `--human` flag switches to human-friendly format
11. Reserved flags: --agent, --version, --dry-run, --quiet, --fields
12. Exit codes: 20 not found, 30 conflict, 10 auth, 11 permission

**Phase 3: Agent-Native (+ ecosystem)**
13. Create `agent/` directory: `brief.md`, `rules/trigger.md`, `rules/workflow.md`, `rules/writeback.md`
14. Every command response appends: rules[] + skills[] + issue
15. `skills` subcommand: list all / show one with full content
16. `issue` subcommand for feedback (create/list/show/close/transition)
17. AGENTS.md at project root

## Best Practices

- Do: Default to JSON output so agents never need to add flags
- Do: Include `suggestion` field in every error response
- Do: Use the three-level certification model for incremental adoption
- Do: Keep `agent/brief.md` to one paragraph for token efficiency
- Don't: Enter interactive mode on errors -- always exit immediately
- Don't: Change JSON schema or error codes within the same version
- Don't: Put logs or progress info on stdout -- use stderr only
- Don't: Accept unknown flags silently -- reject with exit code 2

## Common Pitfalls

- **Problem:** CLI outputs human-readable text by default, breaking agent parsing
  **Solution:** Make JSON the default output format; add `--human` flag for human-friendly mode

- **Problem:** Errors reported in stdout with exit code 0
  **Solution:** Always exit non-zero on failure and write structured error JSON to stderr

- **Problem:** CLI prompts for missing input interactively
  **Solution:** Return structured error with suggestion field and exit immediately

## Related Skills

- `@cli-best-practices` - General CLI design patterns (this skill focuses specifically on AI agent compatibility)

## Additional Resources

- [Agent CLI Spec Repository](https://github.com/ChaosRealmsAI/agent-cli-spec)

Related Skills

competitor-alternatives

5
from ratnesh-maurya/cursor-claude-personas

When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'compa...

react-native-architecture

5
from ratnesh-maurya/cursor-claude-personas

Build production React Native apps with Expo, navigation, native modules, offline sync, and cross-platform patterns. Use when developing mobile apps, implementing native integrations, or architecti...

building-native-ui

5
from ratnesh-maurya/cursor-claude-personas

Complete guide for building beautiful apps with Expo Router. Covers fundamentals, styling, components, navigation, animations, patterns, and native tabs.

native-data-fetching

5
from ratnesh-maurya/cursor-claude-personas

Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, and Expo Router data loaders (useLoaderData).

wordpress-penetration-testing

5
from ratnesh-maurya/cursor-claude-personas

This skill should be used when the user asks to "pentest WordPress sites", "scan WordPress for vulnerabilities", "enumerate WordPress users, themes, or plugins", "exploit WordPress vu...

php-pro

5
from ratnesh-maurya/cursor-claude-personas

Write idiomatic PHP code with generators, iterators, SPL data structures, and modern OOP features. Use PROACTIVELY for high-performance PHP applications.

moodle-external-api-development

5
from ratnesh-maurya/cursor-claude-personas

Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter va...

laravel-expert

5
from ratnesh-maurya/cursor-claude-personas

Senior Laravel Engineer role for production-grade, maintainable, and idiomatic Laravel solutions. Focuses on clean architecture, security, performance, and modern standards (Laravel 10/11+).

voice-ai-engine-development

5
from ratnesh-maurya/cursor-claude-personas

Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support

voice-ai-development

5
from ratnesh-maurya/cursor-claude-personas

Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis...

voice-agents

5
from ratnesh-maurya/cursor-claude-personas

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flo...

lex

5
from ratnesh-maurya/cursor-claude-personas

Centralized 'Truth Engine' for cross-jurisdictional legal context (US, EU, CA) and contract scaffolding.