superpowers-lab
Allows your agent to test new skills in a safe environment. It observes results and sets criteria for using new functions. It helps improve the agent's intelligence.
What it does
This lab lets you safely test new AI ideas in a controlled way. You can compare how the AI performs with and without a new experimental skill, ensuring it works as expected before using it for real.
When to use it
- ✓To test a new experimental skill before adopting it.
- ✓To compare the AI's performance before and after a change.
- ✓To ensure a new idea works reliably on specific tasks.
Example of what to ask your AI
“Compare this new writing skill with the current version, using a sample text, and tell me if there are clear improvements.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You tell me which experiment you want to test and what simple task it should perform. I run the task without the new skill, note the result, and then run it again with the skill. This way, we can see if there was improvement.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill superpowers-labOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/superpowers-lab/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Superpowers Lab
When to Use
- You want to evaluate an experimental workflow from the upstream Superpowers lab before adopting it.
- You need a reproducible comparison of baseline behavior and a proposed skill.
Workflow
- Select one experiment from the upstream lab. Read its instructions, status, prerequisites, and license before using it; this entry does not bundle the lab's experiments.
- Define a small representative task, success criteria, time/cost limits, and the files/tools each run may access. Use disposable fixtures and no production credentials.
- Record the baseline agent/tool configuration and run the task without the experimental skill. Capture outputs, observed failures, and validation results.
- Review the experiment's complete content and any commands before loading it. Run the same task with the experimental skill in an isolated workspace under the same permissions and acceptance criteria.
- Compare the actual outputs and failures. Separate observed improvements from subjective preferences; record changed model/tool settings as confounders.
- Decide whether to retain, revise, or reject the experiment. Keep production adoption separate from the lab run, and include a rollback/removal plan if adoption is proposed.
Example
Compare one lab workflow against our baseline on a disposable bug-fix fixture. Report tests and failure modes without installing it globally.
Limitations
- Experimental guidance is not a production guarantee. Do not infer compatibility or improvement without a controlled run.
- The original experiment and its fixtures are external prerequisites; report unavailable materials.
- Do not execute unreviewed scripts, expand permissions, or use private production data for the experiment.
FAQ
What are 'experimental skills'?
These are new features or ways for the AI to work that are still being tested and haven't been approved for general use.
Why use a 'lab'?
To test new ideas in a safe environment, without risking important data or the AI's normal operation.
What happens if the new skill doesn't work well?
You can decide not to use it, or ask for it to be adjusted. The lab helps make this decision based on real results.
Details
Original description by the author
Evaluate experimental Claude workflow skills in an isolated lab with reproducible tasks, observed results, and explicit adoption criteria.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4289
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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