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baseline-precheck

Measure pytest baselines and generate pass, block, or abort verdicts.

1|Updated Jul 3, 2026
Search Tags:#automation#workflow#testing#pytest#quality-gate#baseline
One-click install
npx skills add https://github.com/apokamo/kaji-starter-python --skill baseline-precheck
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: baseline-precheck
Source: https://github.com/apokamo/kaji-starter-python/tree/main/.claude/skills/baseline-precheck
Command: npx skills add https://github.com/apokamo/kaji-starter-python --skill baseline-precheck

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the uncertainty of code changes by establishing a deterministic pytest baseline before implementation begins, ensuring that developers have a clear understanding of the current system state.

Core Features & Use Cases

  • Deterministic Measurement: Captures a structured pytest baseline to serve as a reliable reference point.
  • Workflow Verdicts: Automatically evaluates the baseline against defined policies to return a pass, block, or abort status.
  • Use Case: Before starting a new feature implementation, run this skill to verify that the current codebase is stable and to document the baseline state for future regression testing.

Quick Start

Execute the baseline precheck skill to measure the current pytest baseline and generate a verdict for the active issue.

Frequently Asked Questions about baseline-precheck

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I establish a deterministic pytest baseline before starting new feature implementation?▼

To establish a deterministic pytest baseline, you can run a precheck skill that measures and structures current test results, generating a structured JSON artifact that serves as a reliable reference point for regression testing.

Can I automate workflow decisions based on pytest test results?▼

Yes, you can automate workflow decisions by evaluating the pytest baseline against defined project policies, which automatically returns a pass, block, or abort status to facilitate downstream workflow decision-making.

Do I need a specific workflow environment to run pytest baseline validation?▼

Yes, baseline validation requires integration with the kaji workflow environment to process worktree data and generate the structured JSON artifacts needed for deterministic verification of code states.

What is the best way to verify codebase stability before implementation begins?▼

The best way to verify codebase stability is to measure and structure a pytest baseline, capturing the current test results to provide deterministic verification of the system state before implementation begins.

How does baseline precheck handle regression testing for active development issues?▼

Baseline precheck handles regression testing by measuring the current pytest baseline for an active issue, structuring the test results to document the baseline state and returning a workflow verdict against project policies.

Are there limitations when using automated pytest baseline validation for quality gates?▼

A limitation of automated pytest baseline validation is its dependency on the kaji workflow environment, meaning it cannot process worktree data or generate structured JSON artifacts outside of this specific integrated workflow context.

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