superpowers-plan
Writes an implementation plan with small steps, exact files to touch, and verification commands. Use before making non-trivial changes.
Browse published AI skills maintained by anthonylee991. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
9Repository stars across indexed skills
4,581Repository forks
468Average repository stars per indexed skill
509Repository stars may repeat across skills from the same repository.
Writes an implementation plan with small steps, exact files to touch, and verification commands. Use before making non-trivial changes.
Implements reliable automations in Python for REST APIs: httpx/requests patterns, retries, timeouts, pagination, typing, config, logging, and tests. Use when writing Python scripts/services that call external A...
Reviews changes for correctness, edge cases, style, security, and maintainability with severity levels (Blocker/Major/Minor/Nit). Use before finalizing changes.
Builds reliable automations that integrate with REST APIs: auth, pagination, retries, rate limits, idempotency, webhooks, data mapping, and safe error handling. Use when calling external APIs, syncing systems,...
Systematic debugging: reproduce, isolate, form hypotheses, instrument, fix, and add regression tests. Use when troubleshooting errors, failing tests, or unexpected behavior.
Produces a structured brainstorm: goals, constraints, risks, options, recommendation, and acceptance criteria. Use before non-trivial implementation or design changes.
Applies tests-first discipline (red/green/refactor) and adds regression tests for bugs. Use when implementing features, fixing bugs, or refactoring.
Finalizes work: runs verification, summarizes changes, notes follow-ups, and ensures repo hygiene. Use at the end of an implementation or debugging session.
Enforces a disciplined workflow for coding, debugging, refactoring, and automation: brainstorm -> plan -> implement with verification (prefer TDD) -> review -> finish. Use for almost any non-trivial change.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.