| name | agent-dx-cli-scale |
| description | A scoring scale for evaluating how well a CLI is designed for AI agents, based on the "Rewrite Your CLI for AI Agents" principles. |
Agent DX CLI Scale
Use this skill to evaluate any CLI against the principles of agent-first design. Score each axis from 0–3, then sum for a total between 0–21.
Human DX optimizes for discoverability and forgiveness.
Agent DX optimizes for predictability and defense-in-depth.
— You Need to Rewrite Your CLI for AI Agents
Scoring Axes
1. Machine-Readable Output
Can an agent parse the CLI's output without heuristics?
| Score | Criteria |
|---|
| 0 | Human-only output (tables, color codes, prose). No structured format available. |
| 1 | --output json or equivalent exists but is incomplete or inconsistent across commands. |
| 2 | Consistent JSON output across all commands. Errors also return structured JSON. |
| 3 | NDJSON streaming for paginated results. Structured output is the default in non-TTY (piped) contexts. |
2. Raw Payload Input
Can an agent send the full API payload without translation through bespoke flags?
| Score | Criteria |
|---|
| 0 | Only bespoke flags. No way to pass structured input. |
| 1 | Accepts --json or stdin JSON for some commands, but most require flags. |
| 2 | All mutating commands accept a raw JSON payload that maps directly to the underlying API schema. |
| 3 | Raw payload is first-class alongside convenience flags. The agent can use the API schema as documentation with zero translation loss. |
3. Schema Introspection
Can an agent discover what the CLI accepts at runtime without pre-stuffed documentation?
| Score | Criteria |
|---|
| 0 | Only --help text. No machine-readable schema. |
| 1 | --help --json or a describe command for some surfaces, but incomplete. |
| 2 | Full schema introspection for all commands — params, types, required fields — as JSON. |
| 3 | Live, runtime-resolved schemas (e.g., from a discovery document) that always reflect the current API version. Includes scopes, enums, and nested types. |
4. Context Window Discipline
Does the CLI help agents control response size to protect their context window?
| Score | Criteria |
|---|
| 0 | Returns full API responses with no way to limit fields or paginate. |
| 1 | Supports --fields or field masks on some commands. |
| 2 | Field masks on all read commands. Pagination with --page-all or equivalent. |
| 3 | Streaming pagination (NDJSON per page). Explicit guidance in context/skill files on field mask usage. The CLI actively protects the agent from token waste. |
5. Input Hardening
Does the CLI defend against the specific ways agents fail (hallucinations, not typos)?
| Score | Criteria |
|---|
| 0 | No input validation beyond basic type checks. |
| 1 | Validates some inputs, but does not cover agent-specific hallucination patterns (path traversals, embedded query params, double encoding). |
| 2 | Rejects control characters, path traversals (../), percent-encoded segments (%2e), and embedded query params (?, #) in resource IDs. |
| 3 | Comprehensive hardening: all of the above, plus output path sandboxing to CWD, HTTP-layer percent-encoding, and an explicit security posture — "The agent is not a trusted operator." |
6. Safety Rails
Can agents validate before acting, and are responses sanitized against prompt injection?
| Score | Criteria |
|---|
| 0 | No dry-run mode. No response sanitization. |
| 1 | --dry-run exists for some mutating commands. |
| 2 | --dry-run for all mutating commands. Agent can validate requests without side effects. |
| 3 | Dry-run plus response sanitization (e.g., via Model Armor) to defend against prompt injection embedded in API data. The full request→response loop is defended. |
7. Agent Knowledge Packaging
Does the CLI ship knowledge in formats agents can consume at conversation start?
| Score | Criteria |
|---|
| 0 | Only --help and a docs site. No agent-specific context files. |
| 1 | A CONTEXT.md or AGENTS.md with basic usage guidance. |
| 2 | Structured skill files (YAML frontmatter + Markdown) covering per-command or per-API-surface workflows and invariants. |
| 3 | Comprehensive skill library encoding agent-specific guardrails ("always use --dry-run", "always use --fields"). Skills are versioned, discoverable, and follow a standard like OpenClaw. |
Interpreting the Total
| Range | Rating | Description |
|---|
| 0–5 | Human-only | Built for humans. Agents will struggle with parsing, hallucinate inputs, and lack safety rails. |
| 6–10 | Agent-tolerant | Agents can use it, but they'll waste tokens, make avoidable errors, and require heavy prompt engineering to compensate. |
| 11–15 | Agent-ready | Solid agent support. Structured I/O, input validation, and some introspection. A few gaps remain. |
| 16–21 | Agent-first | Purpose-built for agents. Full schema introspection, comprehensive input hardening, safety rails, and packaged agent knowledge. |
Bonus: Multi-Surface Readiness
Not scored, but note whether the CLI exposes multiple agent surfaces from the same binary:
