agent-dx-cli-scale

oleh google-labs-code

Skala penilaian untuk mengevaluasi seberapa baik CLI dirancang untuk agen AI, berdasarkan prinsip "Rewrite Your CLI for AI Agents".

npx skills add https://github.com/google-labs-code/design.md --skill agent-dx-cli-scale

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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:

MCP (stdio JSON-RPC) — typed tool invocation, no shell escaping
Extension / plugin install — agent treats the CLI as a native capability
Headless auth — env vars for tokens/credentials, no browser redirect required