Automate backend PR review with grep scans and LLM analysis.
npx skills add https://github.com/parhumm/jaan-to --skill backend-pr-review
Please help me install this Agent Skill. Skill: backend-pr-review Source: https://github.com/parhumm/jaan-to/tree/main/adapters/codex/skillpack/skills/backend-pr-review Command: npx skills add https://github.com/parhumm/jaan-to --skill backend-pr-review
Review backend PRs for security, performance, code quality, and testing gaps across any stack.
Analyze the provided PR diff to surface security, performance, and code-quality findings and generate a structured review report.
High-intent search queries and answers about installing and using this skill.
Automating backend PR review involves analyzing changed files to identify security, performance, and code-quality issues. This Skill uses deterministic grep-based scans and a two-pass LLM analysis to generate a structured Markdown review report from your PR diffs.
Yes, you can review GitLab MRs or local diffs for backend performance issues. The Skill processes changed backend files from GitHub PRs, GitLab MRs, or local diffs to assess performance, security, and code quality across any tech stack.
Static analysis on backend PR diffs works by performing deterministic grep-based scans on changed backend files followed by a two-pass LLM analysis. It enforces stack-aware patterns to identify security vulnerabilities and prioritize findings based on severity thresholds.
The best way to enforce stack-aware patterns is by using a review tool that assesses changed backend files against reference patterns. This Skill applies stack-aware rules during its two-pass LLM analysis to generate structured reports and improvement context.
Reference files are used to enforce stack-aware patterns during the review process. The Skill reads these references to conduct deterministic scans and LLM analysis, ensuring backend code is audited for testing gaps and quality issues accurately.
Automated PR review prioritizes findings using severity thresholds applied during a two-pass LLM analysis. This risk-based prioritization ensures critical security, performance, and code-quality issues are highlighted first in the structured Markdown report.
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