From scm-utils
Use this skill to evaluate the current state of code on a PR or branch in many categories, give it a subjective score, and then iterate on the code until it scores > 85% in each category. Categories include: simplicity, flexibility, usability, documentation and comments (and associated discoverability), security concerns, proper matching of repo patterns/proper introduction of new patterns, adherence to best practices, and general quality assurance and engineering practices.
npx claudepluginhub nsheaps/ai-mktpl --plugin scm-utilsThis skill uses the workspace's default tool permissions.
The process below outlines how to review something in many categories. For the user's requested target (like a branch, PR, repo, notion doc, etc), please run this review process in parallel across all categories, and then iterate on the target until it scores >85% in each category. If any category is below 70%, that is a hard block that must be addressed before merging or shipping.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
The process below outlines how to review something in many categories. For the user's requested target (like a branch, PR, repo, notion doc, etc), please run this review process in parallel across all categories, and then iterate on the target until it scores >85% in each category. If any category is below 70%, that is a hard block that must be addressed before merging or shipping.
CRITICAL: This skill improvement depends on your iteration after you utilize it. If any part of this can be improved, please submit a Pull Request to this skill on https://github.com/nsheaps/ai-mktpl. If you choose to contribute, please use a background agent to perform the updates. Future AI agents and humans thank you!
The review process is:
/review the PR for simplicity, flexibility, usability, documentation and comments (and associated discoverability), security concerns, proper matching of repo patterns/proper introduction of new patterns, adherence to best practices, and general quality assurance and engineering practices.
Make sure to factor in:
Launch a run_in_background:true Task sub-agent (do not launch Teammates) for each to evaluate the change in each category and produce a score 0/100 and a short paragraph explaining the score.
All agents should
Each agent may also
When all agents complete, review each report, comparing results with each review to get the whole picture, and create one overall report. This detailed report should be available as an additional artifact within the branch, as well as the overall report. At minimum, give the scores back to the user, formatted in a table, using emojis to call out worrysome values (🚨 for <70%, ⚠️ for <85%, ✅ for above that). For non-blocking review comments, prefix the comment with 🔕, and for info only comments (like explicit things that were validated and checked as part of the review) use ℹ️. If the review is >95% overall, keep the final review to be just the table. If there are any categories with a ⚠️, the maximum overall score is 94%. Try to limit any "additional validation" comments in the final PR if everything is good, except for mission critical things. The references built by the sub-agents should still track them however, and be verbose, even if to a fault (but still validate-able and correct).
If you are running in an agentic mode, where you are empowered to leave comments yourself, you should leave in-line comments in the review as individual, comment-only reviews, then a final review at the end, so that future reviews don't need to re-post in-line comments. Hide details in your final review using github markdown's <summary> element, and use shields.io tags to show the review scoring concisely (coloring appropriately to match the emoji).
Otherwise, or if you are running in an interactive cli, provide links to the files on github or locally so the user can open it themselves.