Analyze code, UI, and architecture with severity-tracked issues across tech stacks.
npx skills add https://github.com/Gakuseei/brutal-honest-Skill --skill brutal-honest
Please help me install this Agent Skill. Skill: brutal-honest Source: https://github.com/Gakuseei/brutal-honest-Skill/tree/main Command: npx skills add https://github.com/Gakuseei/brutal-honest-Skill --skill brutal-honest
Brutal-honest provides brutally honest, senior-level analysis of code, UI, and architecture across any tech stack, surfacing critical issues and actionable improvements.
Paste this SKILL.md as the system prompt in your agent to load embedded checks and begin a brutal, stack-aware review of your repository.
High-intent search queries and answers about installing and using this skill.
Stack-aware code review detects architecture and UI pattern issues by applying stack-specific checks across web, mobile, or game projects. It categorizes findings into CRITICAL, MAJOR, MEDIUM, and MINOR severities using embedded references and a skeptical validation pipeline.
Yes, you can review monorepos with multi-stack configurations. The review process enforces stack-specific checks and tracks critical issues across all included technologies, ensuring consistent analysis for complex project structures.
Code review tracks issues against git history using a -check workflow. This verifies code changes against previous commits, ensuring traceable recommendations and validating that architectural decisions align with the repository's evolution.
The best way to ensure traceable recommendations during an architecture review is to load embedded references and checklists. This enforces consistent, traceable findings by validating suggestions through a skeptical pipeline before outputting actionable fix prompts.
Yes, automated code review generates fix prompts for critical issues found. After surfacing problems with clear severities, it uses a skeptical validation pipeline to ensure recommendations are accurate before providing actionable improvement prompts.
The limitation of a skeptical pipeline is that it validates recommendations before suggesting changes, which may filter out aggressive architectural shifts. It prioritizes traceable, high-confidence findings over speculative refactoring ideas.
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