mcpbeat Sign in

AI Native CLI Agent Skill

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.

3k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
223
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/lingxling/awesome-skills-cn --skill ai-native-cli

The instruction itself

24 sections, as written by the author

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

  • Agent-first -- default output is JSON; human-friendly is opt-in via --human
  • Agent is untrusted -- validate all input at the same level as a public API
  • Fail-Closed -- when validation logic itself errors, deny by default
  • 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

  • --brief (business card, injected into agent config)
  • Every Command Response (always-on context: data + rules + skills + issue)
  • --help (full self-description: brief + commands + rules + skills + issue)
  • 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 (; | && $())

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)

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

Example 2: Structured Error

{
  "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)

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

Phase 2: Agent-Ready (+ recommended)

  • --help returns structured JSON (help, commands[], rules[], skills[])
  • --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

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

Additional Resources

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Other skills for the same job

different authors, same section of the catalogue
MCP Builder
by anthropics
vendor ×13

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

30k tokens scripts
Changelog Generator
by frostant
×9

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

774 tokens
Finishing A Development Branch
by ZhanlinCui
×7

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

1k tokens
MCP Builder
by JayZeeDesign
×7

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

37k tokens scripts
Vercel React Native Skills
by vercel-labs
vendor ×6

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

39k tokens
Vercel React Best Practices
by ratacat
×5

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

34k tokens
Next Best Practices
by vercel-labs
vendor ×4

Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling

20k tokens
Using Git Worktrees
by ZhanlinCui
×4

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

1k tokens

How to use it

Copy the folder

Take lingxling/ai-native-cli from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.