Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/google-labs-code/design.md/agent-dx-cli-scalenpx skills add google-labs-code/design.md --skill agent-dx-cli-scalegit clone --depth 1 https://github.com/google-labs-code/design.mdWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00034 | $0.01427 |
| Opus 5 | $0.00017 | $0.00714 |
| Sonnet 5 | $0.00007 | $0.00285 |
| Haiku 4.5 | $0.00003 | $0.00143 |
Grade A, and why
agent-dx-cli-scale scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 115 lines · 34 tokens per session scan A 2020ed49b598
agent-dx-cli-scale is a skill published in the GitHub repository google-labs-code/design.md (27,650 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,427 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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