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Running Julia Test

OrganizationPopular
hashgraph-online
running-julia-test

Use when you run tests

Overview

Publisherhashgraph-online
Repositoryawesome-codex-plugins
Skill namerunning-julia-test
Stars
1.3K
Forks
360
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 hashgraph-online on GitHub. Read the source before you install it.

Installation

Install the Running Julia Test 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/hashgraph-online/awesome-codex-plugins/tree/main/plugins/AtelierArith/atelier-arith-julia-development-skills/skills/running-julia-test
  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/hashgraph-online/awesome-codex-plugins.git /tmp/awesome-codex-plugins
mkdir -p .claude/skills
cp -r /tmp/awesome-codex-plugins/plugins/AtelierArith/atelier-arith-julia-development-skills/skills/running-julia-test .claude/skills/running-julia-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Running Julia Test 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 Running Julia Test 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 Running Julia Test 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.

Running Julia tests

Running all tests with Pkg.test()

Before running tests for a new package, make sure the test environment is already set up. If the package does not have tests yet, follow creating-julia-test-env first. Tests that use using Test must have Test available from the package or test project.

To run all of your package's tests, use the following command:

sh
$ julia --project -e 'using Pkg; Pkg.test()'

Running specific test sets only

To run only particular test sets (for example, those defined as @testset "label" begin ... end), use the ~/.julia/bin/testrunner tool.

If testrunner is already installed, this command prints its path:

sh
$ command -v testrunner

If you do not have the testrunner command, install it with:

sh
$ julia -e 'using Pkg; Pkg.activate(); Pkg.Apps.add(url="https://github.com/aviatesk/TestRunner.jl")'

Basic Usage

bash
$ testrunner demo.jl "basic tests"                   # Run the testset named "basic tests"

$ testrunner demo.jl "basic tests" "struct tests"    # Run multiple named testsets

$ testrunner demo.jl '(:(@test startswith(inner_func2(), "inner")))'  # Run a standalone test case

See the guide to learn more.

Caveat: There are cases where name or regex filters do not work as expected

Sometimes, even after specifying a testset name or a regex, all tests may run instead of just the intended ones (this seems to be due to a pattern matching behavior in TestRunner.jl). Example:

sh
# Expected: Only "calculate: basic operations" should run (4 tests), but actually all 20 tests run
$ testrunner --project=. test/runtests.jl "calculate: basic operations"
$ testrunner --project=. test/runtests.jl 'r"^calculate: basic operations$"'
$ testrunner --project=. test/runtests.jl L6:11   # Even if you include the @testset declaration line, all tests are executed

As a workaround, specify the line range including only the @test lines (do not include the @testset ... begin declaration line). This way, you can ensure that only the intended testset is executed:

sh
# In the case where the target @test lines are in lines 7-10 of runtests.jl
$ testrunner --project=. test/runtests.jl L7:10

Always confirm whether your filter worked as intended by checking the Total count in the Test Summary or the n_passed field in the --json output.

Keeping sessions warm with WarmTestRunner.jl

WarmTestRunner.jl is a test runner that accelerates local test iterations while developing Julia packages.

Whereas standard Pkg.test() runs your tests in a fresh Julia process every time, WarmTestRunner.jl reuses a daemon process and a pool of warm workers, spreading the loading and compilation cost across multiple test runs.

As a general guideline, use it as follows:

  • For everyday edit/test cycles: using WarmTestRunner; runtests()
  • For final checks before merges, releases, or CI-style runs: Pkg.test()

To install WarmTestRunner.jl:

sh
$ julia -e 'using Pkg; Pkg.activate(); Pkg.develop(url="https://github.com/terasakisatoshi/WarmTestRunner.jl")'

Basic Usage

sh
$ cd path/to/target/package
$ julia --project -e 'using WarmTestRunner; runtests()'

To run testsets in parallel, start the WarmTestRunner daemon with multiple jobs in one terminal, then run tests with split_testsets = true from another terminal:

sh
# Terminal 1
$ cd path/to/target/package
$ julia --project -e 'using WarmTestRunner; serve(jobs=4)'

# Terminal 2
$ cd path/to/target/package
$ julia --project -e 'using WarmTestRunner; runtests(split_testsets = true)'

This splits testsets and executes them across the warm worker pool.

When runtests() starts the daemon automatically, a daemon process runs in the background. To manually stop any WarmTestRunner daemon, run the following:

sh
$ cd path/to/target/package
$ julia --project -e 'using WarmTestRunner; stop()'

If you run Pkg.build() for the target package, stop the daemon (stop()) first and rerun runtests() afterwards.

This is especially important when developing a Julia package with a C interface. In that workflow, Pkg.build() is often used to rebuild or replace a shared library, and WarmTestRunner workers may still have the old shared library loaded. Stop the daemon before rebuilding so the next test run starts from fresh workers:

sh
$ cd path/to/target/package
$ julia --project -e 'using WarmTestRunner; stop()'
$ julia --project -e 'using Pkg; Pkg.build()'
$ julia --project -e 'using WarmTestRunner; runtests()'
# update files in ./src or ./test/
$ julia --project -e 'using WarmTestRunner; runtests()'
# update files in ./src or ./test/ ... repeat the cycle

Caveat: When the workspace structure changes, run stop() first

The daemon caches the path and contents of Project.toml into its worker pool at startup, so runtests() may fail to start with an error after any of the following workspace structure changes:

  • Deleting or regenerating Project.toml / Manifest.toml
  • Moving or renaming the package directory
  • Editing the [workspace] / [deps] / [sources] sections
  • Removing files the worker is still referencing from outside the daemon

Example errors (recorded in the controller log ~/.julia/warmtestrunner/logs/controller-*.err.log):

nested task error: ".../path/to/Project.toml": No such file
ERROR: timed out waiting for server record for ...

The recovery procedure is the same as for Pkg.build(): first stop(), then run runtests() again:

sh
$ julia --project -e 'using WarmTestRunner; stop()'
$ julia --project -e 'using WarmTestRunner; runtests()'

Frequently asked questions

What does the Running Julia Test AI skill do?

Use when you run tests

Why use Running Julia Test on TypingMind?

Because you install it once and use it with any model. Running Julia Test 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 Running Julia Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/AtelierArith/atelier-arith-julia-development-skills/skills/running-julia-test. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Running Julia Test?

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 Running Julia Test?

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

Is the Running Julia Test AI skill free?

Yes. It is published on GitHub by hashgraph-online 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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