Agent Skill · OpenAI

cli-creator

Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read/write commands, return stable JSON, manage auth, and pair with a companion skill.

Provider: OpenAI Path in repo: skills/.curated/cli-creator/SKILL.md

Skill body

CLI Creator

Create a real CLI that future Codex threads can run by command name from any working directory.

This skill is for durable tools, not one-off scripts. If a short script in the current repo solves the task, write the script there instead.

Start

Name the target tool, its source, and the first real jobs it should do:

Prefer a new folder under ~/code/clis/<tool-name> when the user wants a personal tool and has not named a repo.

Before scaffolding, check whether the proposed command already exists:

command -v <tool-name> || true

If it exists, choose a clearer install name or ask the user.

Choose the Runtime

Before choosing, inspect the user’s machine and source material:

command -v cargo rustc node pnpm npm python3 uv || true

Then choose the least surprising toolchain:

Do not pick a language that adds setup friction unless it materially improves the CLI. If the best language is not installed, either install the missing toolchain with the user’s approval or choose the next-best installed option.

State the choice in one sentence before scaffolding, including the reason and the installed toolchain you found.

Command Contract

Sketch the command surface in chat before coding. Include the binary name, discovery commands, resolve or ID-lookup commands, read commands, write commands, raw escape hatch, auth/config choice, and PATH/install command.

When designing the command surface, read references/agent-cli-patterns.md for the expected composable CLI shape.

Build toward this surface:

Do not expose only a generic request command. Give Codex high-level verbs for the repeated jobs.

Document the JSON policy in the CLI README or equivalent: API pass-through versus CLI envelope, success shape, error shape, and one example for each command family. Under --json, errors must be machine-readable and must not contain credentials.

Auth and Config

Support the boring paths first, in this precedence order:

  1. Environment variable using the service’s standard name, such as GITHUB_TOKEN.
  2. User config under ~/.<tool-name>/config.toml or another simple documented path.
  3. --api-key or a tool-specific token flag only for explicit one-off tests. Prefer env/config for normal use because flags can leak into shell history or process listings.

Never print full tokens. doctor --json should say whether a token is available, the auth source category (flag, env, config, provider default, or missing), and what setup step is missing.

If the CLI can run without network or auth, make that explicit in doctor --json: report fixture/offline mode, whether fixture data was found, and whether auth is not required for that mode.

For internal web apps sourced from DevTools curls, create sanitized endpoint notes before implementing: resource name, method/path, required headers, auth mechanism, CSRF behavior, request body, response ID fields, pagination, errors, and one redacted sample response. Never commit copied cookies, bearer tokens, customer secrets, or full production payloads.

Use screenshots to infer workflow, UI vocabulary, fields, and confirmation points. Do not treat screenshots as API evidence unless they are paired with a network request, export, docs page, or fixture.

Build Workflow

  1. Read the source just enough to inventory resources, auth, pagination, IDs, media/file flows, rate limits, and dangerous write actions. If the docs expose OpenAPI, download or inspect it before naming commands.
  2. Sketch the command list in chat. Keep names short and shell-friendly.
  3. Scaffold the CLI with a README or equivalent repo-facing instructions.
  4. Implement doctor, discovery, resolve, read commands, one narrow draft or dry-run write path if requested, and the raw escape hatch.
  5. Install the CLI on PATH so tool-name ... works outside the source folder.
  6. Smoke test from another repo or /tmp, not only with cargo run or package-manager wrappers. Run command -v <tool-name>, <tool-name> --help, and <tool-name> --json doctor.
  7. Run format, typecheck/build, unit tests for request builders, pagination/request-body builders, no-auth doctor, help output, and at least one fixture, dry-run, or live read-only API call.

If a live write is needed for confidence, ask first and make it reversible or draft-only.

When the source is an existing script or shell history, split the working invocation into real phases: setup, discovery, download/export, transform/index, draft, upload, poll, live write. Preserve the flags, paths, and environment variables the user already relies on, then wrap the repeatable phases with stable IDs, bounded JSON, and file outputs.

For raw escape hatches, support read-only calls first. Do not run raw non-GET/HEAD requests against a live service unless the user asked for that specific write.

For media, artifact, or presigned upload flows, test each phase separately: create upload, transfer bytes, poll/read processing status, then attach or reference the resulting ID.

For fixture-backed prototypes, keep fixtures in a predictable project path and make the CLI locate them after installation. Smoke-test from /tmp to catch binaries that only work inside the source folder.

For log-oriented CLIs, keep deterministic snippet extraction separate from model interpretation. Prefer a command that emits filenames, line numbers or byte ranges, matched rules, and short excerpts.

Rust Defaults

When building in Rust, use established crates instead of custom parsers:

Add a Makefile target such as make install-local that builds release and installs the binary into ~/.local/bin.

TypeScript/Node Defaults

When building in TypeScript/Node, keep the CLI installable as a normal command:

Add an install path such as pnpm install, pnpm build, and pnpm link --global, or a Makefile target that installs a small wrapper into ~/.local/bin.

Python Defaults

When building in Python, prefer boring standard-library pieces unless the workflow needs more:

Add a Makefile target such as make install-local that installs the command on PATH and document whether it depends on uv, a virtualenv, or only system Python.

Companion Skill

After the CLI works, create or update a small skill for it. Use $skill-creator when it is available. Use $CODEX_HOME/skills/<tool-name>/SKILL.md for a personal companion skill unless the user names a repo-local .codex/skills/... path or another skill repo.

Write the companion skill in the order a future Codex thread should use the CLI, not as a tour of every feature. Explain:

Keep API reference details in the CLI docs or a skill reference file. Keep the skill focused on ordering, safety, and examples future Codex threads should actually run.