Publisher: Jesse Vincent / Prime Radiant · Editorial profile by AI Tools Ratings · Review method
What task is this skill designed for?
Prepare a review of a synthetic validation change where a missing authorization condition is deliberately left in the diff.
What input and output should you expect?
Input: The requirement, exact base and head commits, change summary and relevant tests. Include the supported environment and any intentional tradeoffs.
Output: A review package and findings prioritized by impact, with enough code context to reproduce important issues and distinguish blockers from suggestions.
What does it need to run?
A Git repository and a host capable of invoking a review agent for the workflow described in the source. The folder references a separate code-reviewer prompt template.
Publisher README documents Claude Code, Codex and Cursor installation paths, among other harnesses. Tool availability and behavior depend on the host; this is publisher-declared support, not a cross-platform test result.
How should you approach installation?
- Review the pinned folder and its referenced files before enabling the workflow. The folder belongs to the broader Superpowers plugin, not an independently verified standalone installer.
- Follow the publisher README for your chosen coding-agent platform. The publisher documents several harnesses; use the instructions for your installed version and review plugin hooks and permissions.
- Try the suggested task in a disposable repository with synthetic inputs. Check the acceptance list before applying the workflow to shared work.
Installation guide: discovery, dependencies and runtime checks
What files, accounts or data can it access?
Reads diffs and repository files and sends selected context to a review agent. That agent may inspect additional files or run commands depending on its host permissions. Reviewing does not itself authorize merge or publication.
What does the skill cost?
No separate skill purchase is specified in the MIT-licensed source. Coding-agent subscriptions, model tokens, test compute and any external services have their own charges. Review or repeated execution can increase model usage.
What are its limits and alternatives?
A fresh agent is not a guarantee of correctness. Wrong base commits or missing requirements can make the review assess the wrong change. Findings need validation; do not fix a suggestion solely because it has a high severity label.
Use human review for product intent and high-impact decisions. Compare an ordinary pull-request review when the repository already has a reliable process and no separate agent handoff is needed.
What could you ask in a small trial?
Try this with a small sample:
Prepare a review package for this exact base-to-head range and the supplied requirement. Ask the reviewer to inspect authorization behavior and report reproducible findings. Do not merge or publish.
How would you verify the result?
- The package names the exact revision range and required behavior.
- Findings reference affected behavior and supporting code rather than general style preferences.
- The author verifies important findings before claiming they are fixed; the report states whether a separate reviewer actually ran.
Use a baseline and record acceptance evidence before treating a successful task as a reliable workflow.
Which source and license were checked?
Read the reviewed SKILL.md at its pinned commit · Complete reviewed folder
Repository and current publisher guidance
Checked:
MIT — repository LICENSE at the reviewed commit. Dependencies and external services retain their own terms. License evidence
Version details and review scope
Source: obra/superpowers/skills/requesting-code-review
Reviewed commit: 2ce8aa15d05bd57213e6f746f953aafb7ded7a8b
SKILL.md SHA-256: 2180e14a448e9410bec88e6646a2d1f0ce7fdd58de27388c1e052e929679d2bc
Source-document review. This profile covers the source workflow, stated dependencies and license evidence. See our editorial methodology for how we distinguish source reviews and hands-on evaluations.