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Remote Tests

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deonmenezes
remote-tests

Testing against remote executors in integration tests.

Overview

Publisherdeonmenezes
Repositorymantishack
Skill nameremote-tests
Stars
503
Forks
75
Bundled files
Instructions only
LicenseApache-2.0
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 deonmenezes on GitHub. Read the source before you install it.

Installation

Install the Remote Tests 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/deonmenezes/mantishack/tree/main/.codex/skills/remote-tests
  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/deonmenezes/mantishack.git /tmp/mantishack
mkdir -p .claude/skills
cp -r /tmp/mantishack/.codex/skills/remote-tests .claude/skills/remote-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Remote Tests 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 Remote Tests 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 Remote Tests 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.

Remote executor tests exercise the app-server/exec-server split to ensure that agent features work in both local and remote execution environments.

Remote executor tests currently require an x86_64 Linux host machine. There are two flavors:

  1. Docker (Linux exec-server)
  2. Wine (Windows exec-server)

Test Fixtures

Individual test cases must opt-in to being run against a remote executor.

codex_core

Use TestCodexBuilder::build_with_auto_env() to opt-in to remote execution in core integration tests unless the test needs more precise control over its executor.

app-server

Start the server with TestAppServer::new_with_auto_env() unless the test defines its own $CODEX_HOME/environments.toml or will define custom environments at runtime.

Start threads with TestAppServer::send_thread_start_request_with_auto_env() if you've created the server with the auto_env approach. Omit ThreadStartParams.environments (leave it as None) when doing so.

Test Skips

If a test doesn't pass in a particular remote executor configuration you can skip it in just that configuration. Include a string reason for future readers when the selected skip macro supports one.

Choose the skip macro by what causes the test to fail:

  • skip_if_target_windows!: Windows target behavior.
  • skip_if_wine_exec!: Wine-exec runner constraints.
  • skip_if_host_windows!: Windows host constraints.
  • skip_if_remote!: Local-only test behavior.
  • skip_if_no_remote_env!: Remote-only test behavior.

Prefer defining tests that run in all host/target configurations by default. See the $path-types skill for the most common changes required to make tests compatible.

Docker

Docker container is built and initialized via ./scripts/test-remote-env.sh. Sourcing this script in bash also provides the codex_remote_env_cleanup function to use after testing.

To run core integration tests against a Docker remote executor:

bash
bash -c '
  set -euo pipefail
  unset CODEX_TEST_REMOTE_EXEC_SERVER_URL
  source scripts/test-remote-env.sh
  trap codex_remote_env_cleanup EXIT

  cd codex-rs
  just test -p codex-core --test all
'

To run app-server integration tests against a Docker remote executor:

bash
bash -c '
  set -euo pipefail
  unset CODEX_TEST_REMOTE_EXEC_SERVER_URL
  source scripts/test-remote-env.sh
  trap codex_remote_env_cleanup EXIT

  cd codex-rs
  just test -p codex-app-server --test all
'

Wine

These tests build an exec-server for Windows and run it under Wine, with the app-server staying on the Linux host. The cross-platform build dependency means they only run in Bazel.

For core integration tests:

sh
bazel test //codex-rs/core:core-all-wine-exec-test

For app-server integration tests:

sh
bazel test //codex-rs/app-server:app-server-all-wine-exec-test

Devboxes

You can use a devbox to run these tests if you are running on a macOS machine.

You can list devboxes via applied_devbox ls, pick the one with codex in the name. Connect to devbox via ssh <devbox_name>. Reuse the same checkout of codex in ~/code/codex. Reset files if needed. Multiple checkouts take longer to build and take up more space. Check whether the SHA and modified files are in sync between remote and local.

Frequently asked questions

What does the Remote Tests AI skill do?

Testing against remote executors in integration tests.

Why use Remote Tests on TypingMind?

Because you install it once and use it with any model. Remote Tests 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 Remote Tests in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deonmenezes/mantishack/tree/main/.codex/skills/remote-tests. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Remote Tests?

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 Remote Tests?

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

Is the Remote Tests AI skill free?

Yes. It is published on GitHub by deonmenezes under the Apache-2.0 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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