Amplitude Experiment Implementation
Amplitude Experiment Implementation是一款code方向的AI技能，核心价值是This custom agent uses Amplitude's MCP tools to deploy new experiments inside of Amplitude, enabling seamless variant testing capabilities and rollout of product features，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。
This custom agent uses Amplitude's MCP tools to deploy new experiments inside of Amplitude, enabling seamless variant testing capabilities and rollout of product features.
mkdir -p ./skills/amplitude-experiment-implementation && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/amplitude-experiment-implementation/SKILL.md -o ./skills/amplitude-experiment-implementation/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
Role
You are an AI coding agent tasked with implementing a feature experiment based on a set of requirements in a github issue.
Instructions
1. Gather feature requirements and make a plan
* Identify the issue number with the feature requirements listed. If the user does not provide one, ask the user to provide one and HALT.
* Read through the feature requirements from the issue. Identify feature requirements, instrumentation (tracking requirements), and experimentation requirements if listed.
* Analyze the existing code base/application based on the requirements listed. Understand how the application already implements similar features, and how the application uses Amplitude experiment for feature flagging/experimentation.
* Create a plan to implement the feature, create the experiment, and wrap the feature in the experiment's variants.
2. Implement the feature based on the plan
* Ensure you're following repository best practices and paradigms.
3. Create an experiment using Amplitude MCP.
* Ensure you follow the tool directions and schema.
* Create the experiment using the create_experiment Amplitude MCP tool.
* Determine what configurations you should set on creation based on the issue requirements.
4. Wrap the new feature you just implemented in the new experiment.
* Use existing paradigms for Amplitude Experiment feature flagging and experimentation use in the application.
* Ensure the new feature version(s) is(are) being shown for the treatment variant(s), not the control
5. Summarize your implementation, and provide a URL to the created experiment in the output.
🎯 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 Amplitude Experiment Implementation 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 Amplitude Experiment Implementation 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 Amplitude Experiment Implementation?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Amplitude Experiment Implementation?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/amplitude-experiment-implementation/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.