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superpowers-lab

Evaluate experimental Claude workflow skills in an isolated lab with reproducible tasks, observed results, and explicit adoption criteria.

Published by sickn33 ↗sickn33/agentic-awesome-skills

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superpowers-lab

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Use superpowers-lab to prepare a source-version-scoped semantic-duplication audit plan using the linked finding-duplicate-functions workflow and [codebase/function-inventory summary]. Focus the initial extraction scope on exported functions/public methods and high-risk utilities; keep tests excluded unless explicitly requested. Preserve the documented extraction → domain categorization → per-category comparison → report → human review sequence and avoid treating syntactic matches as semantic proof. Return scope, required inputs and review criteria only; do not dispatch the source’s named models/agents, run scripts, create reports/files, consolidate/delete functions or claim tests passed. Wait for my explicit confirmation before any project read, command, model call or change.

Required inputs

  • [codebase/function-inventory summary]

User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗

SKILL.md

name: superpowers-labdescription: "Evaluate experimental Claude workflow skills in an isolated lab with reproducible tasks, observed results, and explicit adoption criteria."risk: safesource: "https://github.com/obra/superpowers-lab"date_added: "2026-02-27"

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.
superpowers-lab by sickn33 · The Skill Leaderboard