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Control Cli

Community
AnastasiyaW
control-cli

Drive and inspect an interactive CLI or TUI with a repeatable local harness, deterministic input, transcripts, and optional profiling. Use for CLI UX checks, prompt flows, startup regressions, hangs, interrupts, resize behavior, or memory growth. Do not use for a non-interactive command that a normal test can cover.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill namecontrol-cli
Stars
212
Forks
32
Bundled files
Instructions only
LicenseMIT
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 AnastasiyaW 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.
    https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/control-cli
  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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/development/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

Exercise an interactive terminal program through a small, repeatable harness. Prefer a repository-native demo or test harness; only assemble a temporary PTY or terminal session when the project has no suitable one.

Harness Loop

  1. Identify the command, smallest fixture, expected ready marker, and cleanup condition.
  2. Discover existing package scripts, PTY helpers, expect scripts, demo recorders, or TUI tests.
  3. Launch in an isolated environment with deterministic variables and local disposable data.
  4. Capture the initial screen or transcript.
  5. Send one action at a time and wait for a concrete prompt or screen marker.
  6. Capture the resulting transcript and any requested profile artifact.
  7. Stop the process and remove temporary sessions, ports, and profiles.

On Windows, prefer the project's own test runner or a checked-in Python/Node probe. Use ConPTY or an already-installed PTY helper when available; do not add a dependency just to run a one-off probe. On other systems, tmux, pty, or a repository-supported terminal harness may be appropriate.

Evidence

For a bug fix or regression, run the same deterministic interaction against the baseline and treatment and pass the captures to verify-this. For a hang, keep the last screen, process exit state, timeout, and a stack/CPU sample when available. For memory growth, use equal repetitions and record before/after snapshots or a bounded allocation metric.

Prefer stable text markers and accessibility-aware terminal probes over sleeps. If a sleep is unavoidable, state why and keep it bounded.

Safety

  • Never send credentials, destructive commands, or production paths into the controlled session.
  • Do not rely on stale screen state after navigation, resize, or a prompt change.
  • Do not hard-code paths, ports, or commands from another repository.
  • Keep transcripts and profiles private when they contain prompts, source, or user data.

Gotchas

  • A process that exits successfully before receiving input is not proof that the interactive flow works; assert the ready marker and the expected state change.
  • Fixed sleeps hide race conditions and make a green run non-repeatable.
  • A terminal transcript can miss rendering defects; use a real UI surface for graphical claims.
  • Cleanup must be verified, especially after a timeout or forced interrupt.

Troubleshooting

SymptomLikely causeAction
Harness hangsWrong ready marker or child process owns the terminalCapture the screen, inspect the process tree, then terminate cleanly
Input is ignoredProgram is not in the expected prompt stateWait for a fresh marker and send one action only
Works manually, fails in harnessHidden environment, terminal size, or timing dependencyRecord env/size and replace sleeps with state-based waits
Transcript is emptyOutput is on another stream or the PTY was detachedCapture stdout and stderr through the repo-native harness and verify file size

Source

Adapted from Cursor Team Kit's MIT-licensed control-cli workflow: https://github.com/cursor/plugins/tree/main/cursor-team-kit/skills/control-cli

Frequently asked questions

What does the Control Cli AI skill do?

Drive and inspect an interactive CLI or TUI with a repeatable local harness, deterministic input, transcripts, and optional profiling. Use for CLI UX checks, prompt flows, startup regressions, hangs, interrupts, resize behavior, or memory growth. Do not use for a non-interactive command that a normal test can cover.

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/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/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?

Yes. It is published on GitHub by AnastasiyaW under the MIT license. 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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