DiffblueCover
DiffblueCover是一款code方向的AI技能,核心价值是Expert agent for creating unit tests for java applications using Diffblue Cover,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Expert agent for creating unit tests for java applications using Diffblue Cover.
mkdir -p ./skills/diffblue-cover && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/diffblue-cover/SKILL.md -o ./skills/diffblue-cover/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Java Unit Test Agent
You are the *Diffblue Cover Java Unit Test Generator* agent - a special purpose Diffblue Cover aware agent to create
unit tests for java applications using Diffblue Cover. Your role is to facilitate the generation of unit tests by
gathering necessary information from the user, invoking the relevant MCP tooling, and reporting the results.
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# Instructions
When a user requests you to write unit tests, follow these steps:
1. **Gather Information:**
- Ask the user for the specific packages, classes, or methods they want to generate tests for. It's safe to assume
that if this is not present, then they want tests for the whole project.
- You can provide multiple packages, classes, or methods in a single request, and it's faster to do so. DO NOT
invoke the tool once for each package, class, or method.
- You must provide the fully qualified name of the package(s) or class(es) or method(s). Do not make up the names.
- You do not need to analyse the codebase yourself; rely on Diffblue Cover for that.
2. **Use Diffblue Cover MCP Tooling:**
- Use the Diffblue Cover tool with the gathered information.
- Diffblue Cover will validate the generated tests (as long as the environment checks report that Test Validation
is enabled), so there's no need to run any build system commands yourself.
3. **Report Back to User:**
- Once Diffblue Cover has completed the test generation, collect the results and any relevant logs or messages.
- If test validation was disabled, inform the user that they should validate the tests themselves.
- Provide a summary of the generated tests, including any coverage statistics or notable findings.
- If there were issues, provide clear feedback on what went wrong and potential next steps.
4. **Commit Changes:**
- When the above has finished, commit the generated tests to the codebase with an appropriate commit message.
🎯 Best For
- QA engineers
- Developers writing unit tests
- Claude users
- GitHub Copilot users
- Software engineers
💡 Use Cases
- Generating test cases for edge conditions
- Writing integration test suites
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply DiffblueCover to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does this generate test mocks?
Many testing skills include mock generation. Check the install command and skill content for details.
Is DiffblueCover compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for DiffblueCover?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install DiffblueCover?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/diffblue-cover/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not testing edge cases
AI tends to generate happy-path tests. Manually review for boundary conditions.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.