Build a composable CLI for Codex from various sources like API docs, OpenAPI specs, and existing scripts.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncli-creatorExecute the skills CLI command in your project's root directory to begin installation:
Fetches cli-creator from OWNER/REPO and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate cli-creator. Access via /cli-creator in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
0
upvotes
Run in your terminal
0
installs
0
this week
—
stars
| name | cli-creator |
| description | 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. |
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.
Name the target tool, its source, and the first real jobs it should do:
list drafts, download failed job logs, search messages, upload media, read queue schedule.ci-logs, slack-cli, sentry-cli, or buildkite-logs.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.
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:
~/.local/bin.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.
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:
tool-name --help shows every major capability.tool-name --json doctor verifies config, auth, version, endpoint reachability, and missing setup.tool-name init ... stores local config when env-only auth is painful.--limit, cursor, offset, or clearly documented default.--dry-run, draft, or preview first when the service allows it, and do not hide writes inside broad commands such as fix, debug, or auto.--json returns stable machine-readable output.request, tool-call, api, or the nearest honest name.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.
Support the boring paths first, in this precedence order:
GITHUB_TOKEN.~/.<tool-name>/config.toml or another simple documented path.--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.
doctor, discovery, resolve, read commands, one narrow draft or dry-run write path if requested, and the raw escape hatch.tool-name ... works outside the source folder./tmp, not only with cargo run or package-manager wrappers. Run command -v <tool-name>, <tool-name> --help, and <tool-name> --json doctor.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.
When building in Rust, use established crates instead of custom parsers:
clap for commands and helpreqwest for HTTPserde / serde_json for payloadstoml for small config filesanyhow for CLI-shaped error contextAdd a Makefile target such as make install-local that builds release and installs the binary into ~/.local/bin.
When building in TypeScript/Node, keep the CLI installable as a normal command:
commander or cac for commands and helpfetch, the official SDK, or the user's existing HTTP helper for API callszod only where external payload validation prevents real breakagepackage.json bin entry for the installed commandtsup, tsx, or tsc using the repo's existing conventionAdd 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.
When building in Python, prefer boring standard-library pieces unless the workflow needs more:
argparse for commands and help, or typer when subcommands would otherwise get messyurllib.request / urllib.parse, requests, or httpx for HTTP, matching what is already installed or already used nearbyjson, csv, sqlite3, pathlib, and subprocess for local files, exports, databases, and existing scriptspyproject.toml console script or a small executable wrapper for the installed commanduv or a virtualenv only when dependencies are actually neededAdd 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.
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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
googlecolab/google-colab-cli
OWNER/REPO
BuilderIO/skills
mattpocock/skills
openai/skills
kunchenguid/no-mistakes
cli-creator reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: cli-creator is focused, and the summary matches what you get after install.
Registry listing for cli-creator matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: cli-creator is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend cli-creator for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added cli-creator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added cli-creator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: cli-creator is focused, and the summary matches what you get after install.
cli-creator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in cli-creator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 66