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. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "ai-native-cli" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-native-cli/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-native-cli Compares
| Feature / Agent | ai-native-cli | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
AI Agent for YouTube Script Writing
Find AI agent skills for YouTube script writing, video research, content outlining, and repeatable channel production workflows.
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
building-native-ui
Complete guide for building beautiful apps with Expo Router. Covers fundamentals, styling, components, navigation, animations, patterns, and native tabs.
react-native-architecture
Production-ready patterns for React Native development with Expo, including navigation, state management, native modules, and offline-first architecture.
nextjs-best-practices
Next.js App Router principles. Server Components, data fetching, routing patterns.
network-101
Configure and test common network services (HTTP, HTTPS, SNMP, SMB) for penetration testing lab environments. Enable hands-on practice with service enumeration, log analysis, and security testing against properly configured target systems.
neon-postgres
Expert patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integration
nanobanana-ppt-skills
AI-powered PPT generation with document analysis and styled images
multi-agent-patterns
This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.
monorepo-management
Build efficient, scalable monorepos that enable code sharing, consistent tooling, and atomic changes across multiple packages and applications.
monetization
Estrategia e implementacao de monetizacao para produtos digitais - Stripe, subscriptions, pricing experiments, freemium, upgrade flows, churn prevention, revenue optimization e modelos de negocio SaaS.
modern-javascript-patterns
Comprehensive guide for mastering modern JavaScript (ES6+) features, functional programming patterns, and best practices for writing clean, maintainable, and performant code.
microservices-patterns
Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.
mcp-builder
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.