Long Command Check logo

Long Command Check

Organization
MiniMax-AI
long-command-check

Audit how an agent harness runs long shell commands, covering foreground wait limits, moving to background without re-running, timeouts, completion notification, polling, output size, cleanup and behaviour after compaction. Use when building or reviewing an agent's shell or background-task tool, or when an agent hangs on dev servers, long builds or test runs.

Overview

PublisherMiniMax-AI
Repositorymcode-stack
Skill namelong-command-check
Stars
464
Forks
36
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 MiniMax-AI on GitHub. Read the source before you install it.

Installation

Install the Long Command Check 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/MiniMax-AI/mcode-stack/tree/main/stacks/ronny/skills/long-command-check
  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/MiniMax-AI/mcode-stack.git /tmp/mcode-stack
mkdir -p .claude/skills
cp -r /tmp/mcode-stack/stacks/ronny/skills/long-command-check .claude/skills/long-command-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Long Command Check 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 Long Command Check 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 Long Command Check 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.

Long Command Check

For people building agents. The question is simple: when the agent runs a shell command, should it wait for it to finish? Following that question leads to a chain of design decisions. Foreground is for fast commands, background is for long ones, and ideally the same process connects the two.

Companion to MCode Engineering Notes 04, "How long should one bash command wait". Plain-text checklist: checklists/long-command.md.

Why waiting is expensive

Two costs while the agent blocks on a command:

  • The visible one: the user waits and nothing else happens.
  • The hidden one: the provider's prompt cache can expire. While a command runs in the foreground there are no new model requests. If the wait is longer than the cache lifetime of the model service you use, the next request re-processes the whole prefix: slower first token, higher cost. Look up the cache lifetime in your provider's documentation.

Moving work to the background does not keep the cache warm forever. It saves the time the agent would otherwise spend idle when it had other work to do.

How to run the audit

Read the shell tool and background-task code of the target agent. For each item, record the answer with evidence (file and function, config, or a test you ran). Do not guess; write "unknown" if the code does not show it.

#ItemQuestion
1Foreground limitHow long does the foreground wait at most? Is that comfortably below the cache lifetime of your model service, leaving room for generation time?
2No re-runWhen a command moves from foreground to background, is it the same process, or is it killed and started again?
3Timeout accountingAfter moving to background and reading output, is the deadline reset? (It should not be, or a stuck command can run forever.)
4Result deliveryWhen a background task finishes, does the model learn it from a notification, or only by polling?
5Notification positionIs the notification appended at the end, or does it modify an already cached prefix?
6Anti-pollingIs blocking wait time capped, and does repeated reading with no change produce a hint to stop polling?
7Output sizeIs large output truncated or written to a file and read incrementally, rather than pasted whole into context? Is incomplete output labelled as incomplete?
8CleanupWho stops background tasks when the user stops the agent, when a one-shot headless run exits, and when the process restarts? Are orphaned tasks marked instead of showing "running" forever?
9After compactionIs "a task finished and its result was not read" tracked outside the conversation history (for example a task table), so it survives compaction?
10PreconditionIf the current environment cannot read background results, is backgrounding disabled?

Quick behavioural tests

If you can run the agent, try these and record what actually happened:

  1. Ask it to start a dev server and then do something else. Does it hang?
  2. Run a command that takes several minutes. Does it move to the background without starting a second copy?
  3. Start a background task, then stop the agent. Is the task stopped?
  4. Let a background task finish, then trigger compaction before reading the result. Is the unread result still surfaced afterwards?

Output

A table of the ten items with pass / fail / unknown and evidence, the results of any behavioural tests, and the top three fixes.

Frequently asked questions

What does the Long Command Check AI skill do?

Audit how an agent harness runs long shell commands, covering foreground wait limits, moving to background without re-running, timeouts, completion notification, polling, output size, cleanup and behaviour after compaction. Use when building or reviewing an agent's shell or background-task tool, or when an agent hangs on dev servers, long builds or test runs.

Why use Long Command Check on TypingMind?

Because you install it once and use it with any model. Long Command Check 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 Long Command Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MiniMax-AI/mcode-stack/tree/main/stacks/ronny/skills/long-command-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Long Command Check?

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 Long Command Check?

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

Is the Long Command Check AI skill free?

Yes. It is published on GitHub by MiniMax-AI 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇