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Agent skill

Running Tests

by brendanhasz in brendanhasz/probflow

Run Python unit test suites strictly using the uv package manager and pytest.

MITAuto-check passedTesting & QA

Install Running Tests

skills CLI
$ npx skills add brendanhasz/probflow --skill running-tests -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brendanhasz/probflow running-tests --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brendanhasz/probflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/running-tests .claude/skills/running-tests && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
running-tests
GitHub stars
175
Token cost
~657 tokens
SKILL.md length
232 words
Files
1
Skills in repo
2
Repo updated
First seen
Licence
MIT

At a glance

Run Python unit test suites strictly using the uv package manager and pytest.

  • Works in 3 steps: Running the Unit Test Suite → Ad-hoc/Isolated Script Checks → Backend Selection and Setup
  • This whenever testing is requested
  • SKILL.md covers Core Directives and Execution Rules
  • Calls uv

What it does

Running Tests is an agent skill from brendanhasz/probflow. Run Python unit test suites strictly using the uv package manager and pytest. Trigger this whenever testing is requested.

Its SKILL.md is about 660 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Unit testing and Deep learning. It works with Python, pytest, TensorFlow and PyTorch. The repository describes itself as: A Python package for building Bayesian models with TensorFlow or PyTorch. The licence is MIT.

When your agent uses it

  • This whenever testing is requested
  • Tasks that involve Unit testing
  • Tasks that involve Deep learning

Example prompts

  • “/running-tests”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Running the Unit Test Suite
  2. Ad-hoc/Isolated Script Checks
  3. Backend Selection and Setup

What it can do on your machine

Read from SKILL.md and the folder at commit e2b3c71. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Running Tests loads about 657 tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 232 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~657

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brendanhasz/probflow at commit e2b3c71, republished under its MIT licence (© brendanhasz). 232 words, ~657 tokens.

Download SKILL.mdSave it as .claude/skills/running-tests/SKILL.md (or your agent's skills folder).
name
running-tests
description
Run Python unit test suites strictly using the uv package manager and pytest. Trigger this whenever testing is requested.
domain
development.testing
version
1.0.0

Python Testing Skill via uv

This skill ensures that all Python test suites and ad-hoc scripts are strictly executed inside the uv environment. You must never invoke testing framework commands (pytest) directly on the host system.

Core Directives

  • Always prepend test executions with uv run.
  • Never bypass the virtual environment or use system-level python binaries directly for project tasks.
  • Maintain local isolation by ensuring uv.lock and project dependencies remain active during verification.

Execution Rules

1. Running the Unit Test Suite

When executing tests, always target the root workspace or specified test file using uv run pytest path/to/test.py -v --color=no.

bash
# To run the entire shared unit test suite
uv run pytest tests/unit/shared -v --color=no

# To run unit tests within a specific directory
uv run pytest path/to/test_directory -v --color=no

# To run a specific test file
uv run pytest path/to/test_file.py -v --color=no

# To run a specific test within a specific file
uv run pytest path/to/test_file.py::name_of_specific_test -v --color=no
2. Ad-hoc/Isolated Script Checks

If you need to execute temporary scripts or evaluate Python object behaviors to diagnose a failing test, always spin them up using the project's pinned virtual environment context:

bash
uv run python path/to/temporary_script.py
3. Backend Selection and Setup

If the desired test being run requires using a specific "backend" (i.e., Tensorflow, PyTorch, or JAX), ensure that the appropriate dependencies are installed and activated within the uv environment before executing the tests.

bash
# Example: Running a test with Tensorflow backend
uv sync --extra tensorflow
uv run pytest path/to/test_file.py -v --color=no

# Example: Running a test with PyTorch backend
uv sync --extra pytorch
uv run pytest path/to/test_file.py -v --color=no

# Example: Running a test with JAX backend
uv sync --extra jax
uv run pytest path/to/test_file.py -v --color=no

If no backend is required or specified, assume one has already been installed, and simply run the tests as usual within the current uv environment.

If it turns out backend dependencies are not installed, and a specific backend is required, use the TensorFlow backend by default (uv sync --extra tensorflow), and then proceed to run the tests within the uv environment as usual.

© brendanhasz, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/running-tests of brendanhasz/probflow.

Open the folder on GitHubat commit e2b3c71

Compare with similar skills

Running Tests next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Running Tests compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Running Tests this skillbrendanhasz/probflow175—~657Automated safety check: PassMIT
ExecuTorch Cortex-M Backendpytorch/executorch5.1k—~872Automated safety check: PassCustom licence
Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs13k2 repos~2.7kAutomated safety check: PassMIT
Technology Selectiondotnet/skills5.6k1 repos~2.1kAutomated safety check: PassMIT
Re AI Modeldslsdzc/rev-skills130—~2.4kAutomated safety check: PassApache-2.0
Test Modemirage-project/mirage2.5k—~4.6kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Running Tests

What does Running Tests do?

Run Python unit test suites strictly using the uv package manager and pytest. Running Tests is an agent skill from brendanhasz/probflow. Run Python unit test suites strictly using the uv package manager and pytest.

When should I use Running Tests?

Running Tests fits situations like: this whenever testing is requested; tasks that involve Unit testing; tasks that involve Deep learning.

How do I install Running Tests in Claude Code?

Run `npx skills add brendanhasz/probflow --skill running-tests -a claude-code`. Or copy the skill folder (.github/skills/running-tests in brendanhasz/probflow) into .claude/skills/running-tests in your project. Claude Code loads it when a task matches its description.

How do I install Running Tests in Codex?

Run `npx skills add brendanhasz/probflow --skill running-tests -a codex`. Or copy the skill folder (.github/skills/running-tests in brendanhasz/probflow) into .agents/skills/running-tests in your project. Codex loads it when a task matches its description.

Can I use Running Tests in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brendanhasz/probflow --skill running-tests -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/running-tests, .gemini/skills/running-tests, .github/skills/running-tests and .opencode/skills/running-tests in your project.

What does Running Tests need to run?

Going by SKILL.md and its folder, Running Tests needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Running Tests access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Running Tests safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Running Tests use?

Running Tests is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Running Tests use?

About 657 tokens (SKILL.md is roughly 2.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Running Tests?

Skills that share tags, products or a category with Running Tests: ExecuTorch Cortex-M Backend (pytorch/executorch, 5.1k stars), Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars), Technology Selection (dotnet/skills, 5.6k stars) and Re AI Model (dslsdzc/rev-skills, 130 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Running Tests?

brendanhasz (a GitHub user) maintains it in brendanhasz/probflow, which has 175 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on September 26, 2026.

Source: brendanhasz/probflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.