create-cli

Design command-line interface parameters and UX: arguments, flags, subcommands, help text, output formats, error messages, exit codes, prompts, config/env precedence, and safe/dry-run behavior. Use when you’re designing a CLI spec (before implementation) or refactoring an existing CLI’s surface area for consistency, composability, and discoverability.

5 stars

Best use case

create-cli is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Design command-line interface parameters and UX: arguments, flags, subcommands, help text, output formats, error messages, exit codes, prompts, config/env precedence, and safe/dry-run behavior. Use when you’re designing a CLI spec (before implementation) or refactoring an existing CLI’s surface area for consistency, composability, and discoverability.

Teams using create-cli should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/create-cli/SKILL.md --create-dirs "https://raw.githubusercontent.com/marchatton/agent-skills/main/.agents/skills/00-utilities/create-cli/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/create-cli/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How create-cli Compares

Feature / Agentcreate-cliStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Design command-line interface parameters and UX: arguments, flags, subcommands, help text, output formats, error messages, exit codes, prompts, config/env precedence, and safe/dry-run behavior. Use when you’re designing a CLI spec (before implementation) or refactoring an existing CLI’s surface area for consistency, composability, and discoverability.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Create CLI

Design CLI surface area (syntax + behavior), human-first, script-friendly.

## Do This First

- Read `references/cli-guidelines.md` and apply it as the default rubric.
- Upstream/full guidelines: https://clig.dev/ (propose changes: https://github.com/cli-guidelines/cli-guidelines)
- Ask only the minimum clarifying questions needed to lock the interface.

## Clarify (fast)

Ask, then proceed with best-guess defaults if user is unsure:

- Command name + one-sentence purpose.
- Primary user: humans, scripts, or both.
- Input sources: args vs stdin; files vs URLs; secrets (never via flags).
- Output contract: human text, `--json`, `--plain`, exit codes.
- Interactivity: prompts allowed? need `--no-input`? confirmations for destructive ops?
- Config model: flags/env/config-file; precedence; XDG vs repo-local.
- Platform/runtime constraints: macOS/Linux/Windows; single binary vs runtime.

## Deliverables (what to output)

When designing a CLI, produce a compact spec the user can implement:

- Command tree + USAGE synopsis.
- Args/flags table (types, defaults, required/optional, examples).
- Subcommand semantics (what each does; idempotence; state changes).
- Output rules: stdout vs stderr; TTY detection; `--json`/`--plain`; `--quiet`/`--verbose`.
- Error + exit code map (top failure modes).
- Safety rules: `--dry-run`, confirmations, `--force`, `--no-input`.
- Config/env rules + precedence (flags > env > project config > user config > system).
- Shell completion story (if relevant): install/discoverability; generation command or bundled scripts.
- 5–10 example invocations (common flows; include piped/stdin examples).

## Default Conventions (unless user says otherwise)

- `-h/--help` always shows help and ignores other args.
- `--version` prints version to stdout.
- Primary data to stdout; diagnostics/errors to stderr.
- Add `--json` for machine output; consider `--plain` for stable line-based text.
- Prompts only when stdin is a TTY; `--no-input` disables prompts.
- Destructive operations: interactive confirmation + non-interactive requires `--force` or explicit `--confirm=...`.
- Respect `NO_COLOR`, `TERM=dumb`; provide `--no-color`.
- Handle Ctrl-C: exit fast; bounded cleanup; be crash-only when possible.

## Templates (copy into your answer)

### CLI spec skeleton

Fill these sections, drop anything irrelevant:

1. **Name**: `mycmd`
2. **One-liner**: `...`
3. **USAGE**:
   - `mycmd [global flags] <subcommand> [args]`
4. **Subcommands**:
   - `mycmd init ...`
   - `mycmd run ...`
5. **Global flags**:
   - `-h, --help`
   - `--version`
   - `-q, --quiet` / `-v, --verbose` (define exactly)
   - `--json` / `--plain` (if applicable)
6. **I/O contract**:
   - stdout:
   - stderr:
7. **Exit codes**:
   - `0` success
   - `1` generic failure
   - `2` invalid usage (parse/validation)
   - (add command-specific codes only when actually useful)
8. **Env/config**:
   - env vars:
   - config file path + precedence:
9. **Examples**:
   - …

## Notes

- Prefer recommending a parsing library (language-specific) only when asked; otherwise keep this skill language-agnostic.
- If the request is “design parameters”, do not drift into implementation.

Related Skills

create-agent-skills

5
from marchatton/agent-skills

Expert guidance for creating, writing, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.

create-prd

5
from marchatton/agent-skills

Draft PRD with scope, stories, acceptance criteria, verification. Use when shaping a new feature or spec.

create-json-prd

5
from marchatton/agent-skills

Generate a Product Requirements Document (PRD) as JSON for Ralph by converting an existing PRD markdown file. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out.

skill-creator

5
from marchatton/agent-skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

modular-skills-architect

5
from marchatton/agent-skills

Map and refactor an agent context ecosystem: skills, commands/workflows, hooks, agent files, AGENTS.md templates, and docs. Output system map, module/dependency design, Register updates, and a concrete split/consolidate/rename/delete plan. Use when routing or ownership is messy.

heal-skill

5
from marchatton/agent-skills

This skill should be used when fixing incorrect SKILL.md files with outdated instructions or APIs.

agent-native-audit

5
from marchatton/agent-skills

Comprehensive agent-native architecture audit with scored principles and multi-slice review. Use for system-wide health checks or periodic audits.

write-judge-prompt

5
from marchatton/agent-skills

Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests). Do NOT use when you need to validate or calibrate the judge — use validate-evaluator instead.

validate-evaluator

5
from marchatton/agent-skills

Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).

generate-synthetic-data

5
from marchatton/agent-skills

Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.

evaluate-rag

5
from marchatton/agent-skills

Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.

eval-audit

5
from marchatton/agent-skills

Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).