Pytest Optimizer
- 187 installs
- 2 repo stars
- Updated January 25, 2026
- jorgealves/agent_skills
Speed up flaky or slow pytest suites via fixture scope, parametrization, markers, parallel runs, and isolation fixes before CI merge gates.
About
Optimizes pytest suites by restructuring fixtures, parametrization, markers, and parallel execution to cut CI time, eliminate flaky failures, and keep backend and API test gates reliable before release.
- Fixture scope tuning
- Flaky test isolation
- Parametrize and mark strategy
- Parallel and xdist setup
- CI runtime reduction
Pytest Optimizer by the numbers
- 187 all-time installs (skills.sh)
- +8 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #822 of 2,153 Testing & QA skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 187 |
|---|---|
| repo stars | ★ 2 |
| Last updated | January 25, 2026 |
| Repository | jorgealves/agent_skills ↗ |
What it does
Speed up flaky or slow pytest suites via fixture scope, parametrization, markers, parallel runs, and isolation fixes before CI merge gates.
Files
pytest Optimizer
Purpose and Intent
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
When to Use
- Project Setup: When initializing a new Python project.
- Continuous Integration: As part of automated build and test pipelines.
- Legacy Refactoring: When updating older Python codebases to modern standards.
When NOT to Use
- Non-Python Projects: This tool is specialized for the Python ecosystem.
Error Conditions and Edge Cases
- Missing Requirements: If the project lacks a requirements.txt or pyproject.toml.
- Incompatible Versions: If the project uses a Python version not supported by the tools.
Security and Data-Handling Considerations
- All analysis is performed locally.
- No source code or credentials are ever transmitted externally.
name: pytest-optimizer
version: 1.0.0
description: Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
inputs:
project_path:
type: string
description: Path to the Python project directory.
required: true
outputs:
report:
type: string
description: A detailed report of the analysis or actions performed.
capabilities:
- Automated analysis of Python project structure.
- Integration with standard Python tooling.
constraints:
- Requires a valid Python environment.
security:
- Operates locally on source files.
examples:
- input:
project_path: "."
output:
report: "Analysis complete. No issues found."
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