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superpowers-lab

superpowers-lab

github.com/sickn33Agent workflows48.7k1.4k installsUpdated today
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About superpowers-lab

superpowers-lab is a skill for evaluating experimental workflows from the upstream Superpowers lab against a baseline. It guides controlled, isolated runs and evidence-based decisions before any production adoption.

Key features

  1. 01Controlled baseline and skill comparison
  2. 02Isolated runs with bounded access
  3. 03Output and failure analysis
  4. 04Adoption and rollback planning

Use cases

  1. 01Assessing an experimental agent workflow
  2. 02Comparing task results against a baseline
  3. 03Deciding whether to retain a lab experiment

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill superpowers-lab

After installing, type/superpowers-labin Claude Code to confirm it loaded.

Details

From the project README

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

  1. 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.
  2. 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.
  3. Record the baseline agent/tool configuration and run the task without the experimental skill. Capture outputs, observed failures, and validation results.
  4. 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.
  5. Compare the actual outputs and failures. Separate observed improvements from subjective preferences; record changed model/tool settings as confounders.
  6. 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.

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