Repository source code setup

repository-source-code-setup

Summary

Clone a scientific software repository and install its runtime dependencies and test fixtures to prepare the codebase for execution or testing. This skill ensures a reproducible local environment matching the project's configuration before running workflows, unit tests, or analyses.

When to use

You need to validate that a published software tool (e.g., MassQL) executes correctly in your environment, reproduce published results, or contribute to development. Apply this skill when you have a GitHub repository URL and want to verify the unit test suite or run the tool end-to-end on your own machine.

When NOT to use

• The tool is already installed in your environment (e.g., via pip install massql) and you do not need to reproduce or develop against the source repository.
• You only need to use the tool as a published package, not inspect or modify its source code.
• The repository is private or inaccessible to you; instead, rely on pre-built Docker images or containerized distributions.

Inputs

• GitHub repository URL or clone path (string)
• Repository README or configuration files (text/markdown)
• Requirements files (requirements.txt, setup.py, environment.yml, or equivalent)

Outputs

• Cloned local repository directory with all source code
• Installed Python package with dependencies available in the environment
• Fetched test fixtures and data files (if required by the test suite)
• Test execution report (pass/fail status, test counts, error logs)
• Verification that the tool CLI or API is callable

How to apply

First, clone the repository from GitHub (e.g., mwang87/MassQueryLanguage) to a local directory. Next, examine the repository structure and README to identify runtime and test dependencies—these may be specified in requirements files (e.g., requirements.txt, requirements_test.txt) or in documentation. Install the core package and its dependencies using the recommended package manager (pip for Python). If the project includes a test suite, fetch additional test fixtures or data (e.g., via scripts like get_data.sh) that are not bundled with the git repo. Verify installation by checking that the tool's API or CLI is available (e.g., from massql import msql_engine or massql --help). Finally, run the test suite (e.g., via a CI workflow definition like test-unit.yml) to confirm that the installation succeeded and the codebase is functional.

Related tools

• MassQL (Reference implementation of a domain-specific query language for mass spectrometry data; the tool being installed and tested) — https://github.com/mwang87/MassQueryLanguage
• GitHub Actions (test-unit.yml workflow) (CI workflow definition that specifies how to execute the unit test suite; consulted to understand the test execution steps) — https://github.com/mwang87/MassQueryLanguage
• pytest (inferred from test suite structure) (Test runner implied by the presence of a test suite; typically specified in requirements_test.txt)

Examples

cd /tmp && git clone https://github.com/mwang87/MassQueryLanguage.git && cd MassQueryLanguage && pip install -r requirements_test.txt && cd tests && sh ./get_data.sh && cd .. && python -m pytest tests/

Evaluation signals

• Repository successfully clones without authentication errors and contains the expected directory structure (Language Grammar, Reference Implementation, CLI, workflows).
• All core and test dependencies install without errors or unresolved version conflicts; pip show massql or from massql import msql_engine confirms the package is available.
• Test fixtures are fetched completely (e.g., cd tests && sh ./get_data.sh completes without errors); data files are present in the test directory.
• Unit test suite executes and the test-unit.yml CI badge shows a passing status (green check); test output reports zero failures or expected/acceptable failure counts.
• The command-line tool is callable (e.g., massql test.mzML "QUERY scaninfo(MS2DATA)" --output_file results.tsv produces output without import or command-not-found errors).

Limitations

• Test fixtures and large data files may not be bundled with the git repository; they must be fetched separately via scripts (e.g., get_data.sh), which may require network access or authentication.
• Python version compatibility may vary; the README states the package is tested on Python 3.9 but compatibility with other versions is uncertain.
• CI workflows (test-unit.yml, test-package.yml) are defined for GitHub Actions; execution locally requires a compatible test runner setup, not automatic replication of the CI environment.

Evidence

• [other] Clone the MassQL repository, install dependencies, execute unit tests, collect results: "1. Clone the MassQL repository (mwang87/MassQueryLanguage) from GitHub. 2. Install dependencies and runtime environment as specified in the repository configuration. 3. Execute the unit test suite"
• [readme] Test fixtures are not bundled and must be fetched separately: "To run tests, you'll need to first fetch some fixtures that are not bundled with the git repo: cd tests && sh ./get_data.sh"
• [readme] Test suite requires additional dependencies specified in a separate requirements file: "You will also want to install the extra requirements for the test suite: pip install -r requirements_test.txt"
• [readme] Python 3.9 is the tested version; other versions are uncertain: "We currently test massql in python 3.9, but are figuring out other versions if they work or not."
• [readme] Basic Python API invocation after installation: "from massql import msql_engine results_df = msql_engine.process_query(input_query, input_filename)"