agent-native-cli
Use when designing, reviewing, or refactoring a CLI that must serve AI agents alongside humans, or when converting an API or SDK into an agent-usable CLI interface.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/agents365-ai/agent-native-cliagent-native-cli
Purpose
This skill helps analyze, design, and refactor command-line tools so they can reliably serve humans, AI agents, and orchestration systems at the same time.
It is not a skill for merely using a CLI. It is a skill for designing and reviewing a CLI as an agent-native interface.
The skill focuses on four goals:
- Make CLI behavior predictable for AI agents.
- Make CLI output readable and recoverable for humans.
- Make CLI execution manageable for systems and orchestrators.
- Define a complete interaction loop from authentication to error routing.
When to use this skill
Use this skill when the user wants to:
- evaluate whether an existing CLI is agent-friendly
- redesign a CLI to better support AI agents
- convert an API or SDK into an agent-native CLI
- review help output, schema design, exit codes, or JSON contracts
- design dry-run, auth delegation, or safety boundaries
- generate CLI skills, docs, or interface conventions from schema
- refactor a human-oriented CLI into a machine-friendly one
- define how a CLI should interact with an agent runtime
Typical prompts include:
- "Review this CLI and tell me whether it is agent-native."
- "Design a CLI for this API that an AI agent can use reliably."
- "Refactor this tool so stdout is machine-readable and safer for agents."
- "Help me define schema introspection, dry-run, and exit code semantics."
- "Turn these design principles into a practical CLI contract."
When not to use this skill
Do not use this skill when the user only wants:
- help running a specific command
- installation help for a CLI
- shell troubleshooting unrelated to interface design
- generic Linux or terminal tutorials
- agent planning or memory design unrelated to tools
- API business logic review without any CLI/tooling layer
Core model
An agent-native CLI must simultaneously serve three audiences.
2026 Context: Recent benchmarks confirm this approach is optimal. Production data shows CLI-based agents achieve 28% higher task completion vs. MCP-only agents with the same token budget, and enjoy a 33% token efficiency advantage. However, the emerging best practice is a hybrid approach: CLIs for local/scriptable workflows, MCP servers for multi-tenant SaaS and per-user auth. The largest agents (Claude Code, Cursor, Gemini CLI) use both. This skill teaches CLI design; for the complementary MCP patterns, see the decision framework in When CLI is the right answer below.
1. Human
Needs: readable output, friendly error messages, onboarding guidance
Channels: stderr, optional --format table, interactive TUI when appropriate
2. AI Agent
Needs: structured data, stable contracts, self-description
Channels: stdout as JSON, stable exit codes, schema introspection, dry-run previews, generated skills/docs
3. System / Orchestrator
Needs: delegated authentication, process management, deterministic error routing
Metadata
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{
"plugins": {
"official-agents365-ai-agent-native-cli": {
"enabled": true,
"auto_update": true
}
}
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