skills/dev-workflow/agent-dx-cli-scale/SKILL.md
A scoring scale for evaluating how well a CLI is designed for AI agents, based on the "Rewrite Your CLI for AI Agents" principles.
npx skillsauth add The-Utopia-Studio/skills agent-dx-cli-scaleInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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
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. |
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. |
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. |
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. |
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." |
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. |
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. |
| 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. |
Not scored, but note whether the CLI exposes multiple agent surfaces from the same binary:
development
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.
development
Updates a presentation with new numbers — quarterly refreshes, earnings updates, comp rolls, rebased market data. Use whenever the user asks to "update the deck with Q4 numbers", "refresh the comps", "roll this forward", "swap in the new earnings", "change all the $485M to $512M", or any request to swap figures across an existing deck without rebuilding it.
development
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
tools
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.