hf.test-adequacy

Analyze production code diffs to identify test coverage gaps.

5|2|Updated Feb 18, 2026

Search Tags:#verification #code-quality #quality-gate #ci #pull-request #test-coverage #diff-analysis

Ready to run

npx skills add https://github.com/T-rav/hydraflow --skill hf-test-adequacy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hf.test-adequacy
Source: https://github.com/T-rav/hydraflow/tree/main/.codex/skills/hf.test-adequacy
Command: npx skills add https://github.com/T-rav/hydraflow --skill hf-test-adequacy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Assess whether changed production code has adequate test coverage for functionality, regressions, and edge cases.

Core Features & Use Cases

Read production diffs between branches and map to relevant tests.
Check coverage for new/changed functions, edge cases, and regression paths.
Generate a concise suitability report.

Quick Start

Run the assessment against your current branch by diffing with origin/main and report whether production code tests are adequate.

Frequently Asked Questions about hf.test-adequacy

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

FAQPage Schema

How do I check if my production code changes have adequate test coverage?▼

To check test coverage adequacy for production code changes, analyze branch diffs alongside existing tests to identify missing tests for new functions, edge cases, and regression paths. This generates a concise suitability report detailing gaps and suggested actions.

How do I identify missing tests for edge cases in a pull request diff?▼

Identify missing edge case tests in a pull request diff by mapping changed production code to relevant existing tests. Analyzing this diff coverage reveals untested functionality and regression paths, outputting a report of missing tests and recommended actions.

Can I use diff analysis as a CI quality gate for release readiness?▼

Yes, you can use diff analysis as a CI quality gate for release readiness. By evaluating branch diffs against test suite coverage data, the assessment verifies whether functionality and edge cases are well-tested before allowing a pull request to merge.

What do I need to assess test coverage gaps across different modules?▼

To assess test coverage gaps across modules, you need access to diffs between branches and a test suite with coverage data. The analysis maps production changes to relevant tests and reports missing coverage for regressions and edge cases.

What is the best way to find untested functionality after modifying code?▼

The best way to find untested functionality after modifying code is to analyze the production diff against existing tests. This process maps changed lines to test coverage data, highlighting missing tests for new functionality and edge cases in a suitability report.

When should I not rely on automated test coverage analysis for code review?▼

Automated test coverage analysis for code review requires a test suite with coverage data and accessible branch diffs. If these prerequisites are missing, or if changes do not touch functionality, edge cases, or regressions, the analysis cannot report missing tests accurately.