A skill that evaluates whether a CLI is reliably usable by AI agents and helps you design CLIs that serve humans, agents, and orchestration systems at the same time. Built around seven principles, a 14-criterion rubric, and a structured refactor playbook.
- Evaluates whether an existing CLI is reliably usable by AI agents
- Designs CLI interfaces that serve humans, agents, and orchestration systems simultaneously
- Converts REST APIs and SDKs into agent-native CLI command trees
- Reviews stdout contracts, exit code semantics, and error envelope design
- Designs schema-driven self-description, dry-run previews, and schema introspection
- Defines safety tiers (open / warned / hidden) for graduated command visibility
- Designs delegated authentication so agents never own the auth lifecycle
- Produces prioritized refactor plans with concrete interface examples
| Doc | What's inside |
|---|---|
| docs/install.md | Per-platform install (Claude Code / OpenClaw / Hermes / pi-mono / Codex / SkillsMP) and path summary |
| docs/changelog.md | Version history from v1.1.0 through v1.3.5 |
| SKILL.md | Workflow guide loaded by the agent |
| references/ | On-demand reference material — design patterns, rubric, checklists, examples, testing recipes, citations |
The core SKILL.md is portable, and this repository includes metadata for the platforms listed below:
| Platform | Status | Details |
|---|---|---|
| Claude Code | Full support | Native SKILL.md format |
| OpenClaw / ClawHub | Full support | metadata.openclaw namespace |
| Hermes Agent | Full support | metadata.hermes namespace, category: engineering |
| pi-mono | Full support | metadata.pimo namespace |
| OpenAI Codex | Full support | agents/openai.yaml sidecar |
| SkillsMP | Indexed | GitHub topics configured |
| Capability | Native agent | This skill |
|---|---|---|
| Evaluate whether a CLI is agent-native | No | Yes — structured diagnosis across 7 principles |
| Design stdout JSON contract | Inconsistent | Always — stable envelope with ok, data, error |
| Define exit code semantics | Ad hoc | Yes — documented, deterministic per failure class |
Design layered --help and schema introspection |
No | Yes — full self-description pattern |
| Design dry-run previews | Rarely | Always — request shape preview without execution |
| Define safety tiers for commands | No | Yes — open / warned / hidden tiers |
| Design delegated authentication | No | Yes — human manages auth lifecycle; agent uses token |
| Separate trust levels for env vs. CLI args | No | Yes — directional trust model |
| Produce prioritized refactor plan | Rarely | Always — P0 / P1 / P2 with examples |
| Score CLI across 14-criterion rubric | No | Yes — 0–2 per criterion with verdict |
- Evaluating whether an existing CLI is usable by an AI agent
- Designing a new CLI interface for an API or SDK
- Refactoring a human-first CLI to be machine-readable
- Reviewing stdout, stderr, and exit code contract design
- Defining dry-run, schema introspection, and self-description layers
- Designing auth delegation and trust boundaries for agent safety
- Producing a SKILL.md or skill docs from a CLI schema
See docs/install.md for per-platform install commands (Claude Code, OpenClaw / ClawHub, Hermes, pi-mono, OpenAI Codex, SkillsMP) and the installation paths summary.
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Agents365-ai
- Bilibili: https://space.bilibili.com/441831884
- GitHub: https://github.com/Agents365-ai
CC BY-NC 4.0