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Control Cli

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cursor
control-cli

Build or adapt a local harness to drive, inspect, and profile an interactive CLI or TUI without external services. Use for CLI UX checks, startup regressions, memory leaks, hangs, prompt flows, or terminal demos.

Overview

Publishercursor
Repositoryplugins
Skill namecontrol-cli
Stars
8K
Forks
728
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cursor on GitHub. Read the source before you install it.

Installation

Install the Control Cli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cursor/plugins.git /tmp/plugins
mkdir -p .claude/skills
cp -r /tmp/plugins/cursor-team-kit/skills/control-cli .claude/skills/control-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Control Cli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Control Cli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Control Cli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Control CLI

Use a repeatable local harness to exercise an interactive CLI instead of poking at it manually. First reuse the repo's own test/demo harness if it exists; otherwise assemble a temporary harness from standard local tools.

What It Is Used For

  • Reproducing CLI/TUI bugs with deterministic input.
  • Verifying keyboard flows, prompts, interrupts, resize behavior, and terminal layout.
  • Capturing before/after transcripts for bug fixes.
  • Profiling startup time, slow operations, hangs, or memory growth.
  • Recording a short terminal demo when output is easier to show than explain.

Harness Loop

  1. Identify the command under test and the smallest reproducible workspace.
  2. Discover existing local harnesses: package scripts, e2e tests, demo recorders, expect scripts, or PTY helpers.
  3. If no harness exists, launch the CLI in an isolated terminal session with deterministic env vars.
  4. Capture the current screen before interacting.
  5. Send one action at a time: text, Enter, arrows, Escape, Ctrl-C, resize.
  6. Wait for a concrete screen pattern or prompt before the next action.
  7. Save the transcript and any profile artifacts.
  8. Kill the session cleanly.

Harness Options

  • Repo-native harness: prefer checked-in scripts because they know the app's startup, env, and prompts.
  • tmux: managed sessions, capture-pane, send-keys, attach/detach.
  • PTY probe: use a short Python, Node, or Expect script when tmux is unavailable.
  • Runtime inspector: use Node or Bun inspector for CPU profiles, heap snapshots, and live evaluation.
  • Terminal recorder: use repo-local demo tools or asciinema-compatible tools when the user asks for a demo.

Minimal tmux Harness

bash
SESSION="cli-harness-$(date +%s)"
tmux new-session -d -s "$SESSION" -- <command-under-test>
tmux capture-pane -pt "$SESSION"
tmux send-keys -t "$SESSION" "help" Enter
tmux capture-pane -pt "$SESSION"
tmux kill-session -t "$SESSION"

For Node CLIs:

bash
NODE_OPTIONS="--inspect=127.0.0.1:0" tmux new-session -d -s "$SESSION" -- <node-cli-command>

Read the terminal output to find the inspector URL, then use Chrome DevTools-compatible tooling if profiling is needed.

Minimal PTY Harness

Use a PTY script when you need deterministic waits in a repo that does not have tmux or a demo harness. Keep it temporary unless the user asks to add a reusable test.

python
import os
import pty
import select
import subprocess
import time

master_fd, slave_fd = pty.openpty()
proc = subprocess.Popen(
    ["<command>", "<arg>"],
    stdin=slave_fd,
    stdout=slave_fd,
    stderr=slave_fd,
    close_fds=True,
)
os.close(slave_fd)

deadline = time.time() + 30
buffer = b""
while time.time() < deadline:
    ready, _, _ = select.select([master_fd], [], [], 0.25)
    if not ready:
        continue
    chunk = os.read(master_fd, 4096)
    buffer += chunk
    if b"<ready text>" in buffer:
        os.write(master_fd, b"help\n")
        break

print(buffer.decode(errors="replace"))
proc.terminate()
os.close(master_fd)

If the CLI needs richer terminal control, use pty.fork() or an existing PTY library.

Profiling Recipes

  • Startup regression: capture baseline and treatment startup timings under the same machine, env, and command.
  • Slow operation: start a CPU profile, perform the operation, stop the profile, and compare top self-time functions.
  • Memory leak: force GC if available, take a heap snapshot, perform the operation repeatedly, force GC again, and take another snapshot.
  • Hang: capture the screen, active handles/resources, and a stack/CPU sample before interrupting.

Guardrails

  • Prefer deterministic waits over sleeps. If you must sleep, explain why.
  • Do not send credentials or destructive commands into a controlled session.
  • Keep the harness in /tmp unless the repo already has a testing/demo harness.
  • Do not hard-code paths from another repository. Adapt commands to the current repo's scripts and runtime.
  • Clean up tmux sessions, temp dirs, inspector processes, and demo artifacts unless the user asks to keep them.

Frequently asked questions

What does the Control Cli AI skill do?

Build or adapt a local harness to drive, inspect, and profile an interactive CLI or TUI without external services. Use for CLI UX checks, startup regressions, memory leaks, hangs, prompt flows, or terminal demos.

Why use Control Cli on TypingMind?

Because you install it once and use it with any model. Control Cli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Control Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cursor/plugins/tree/main/cursor-team-kit/skills/control-cli. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Control Cli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Control Cli?

As many as you like. As long as a model supports skills, you can use Control Cli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Control Cli AI skill free?

It is published on GitHub by cursor. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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