Kaizen
Guide for continuous improvement, error proofing, and standardization. Use this skill when the user wants to improve code quality, refactor, or discuss process improvements.
Skills for your AI
Guide for continuous improvement, error proofing, and standardization. Use this skill when the user wants to improve code quality, refactor, or discuss process improvements.
Compare two code versions for semantic equivalence via semi-formal tracing of both versions side-by-side.
Autonomous repository-wide audit-and-fix pipeline: health → review → locate/explain → fix → diff-verify → iterate until clean. Starts with a mandatory consent prompt (token-intensive); after consent runs hands-free.
Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy).
Audits and strips Lovable scaffolding from Vite + React projects — removes lovable-tagger, swaps placeholder assets, prunes unused Radix deps, cleans generated docs, and neutralizes stale favicon/CDN caching so the codebase ships as yours.
Plan and execute large refactors with dependency-aware work packets and parallel analysis.
Review a git diff or explicit file scope for reuse, code quality, efficiency, clarity, and standards issues, then optionally apply safe Codex-driven fixes.
Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps.
Two-layer performance skill combining disciplined THINK layer (surgical edits, simplicity) and terse SPEAK layer (caveman compression). Triggers on requests for brevity, token efficiency, or disciplined coding.
Master ShellCheck static analysis configuration and usage for shell script quality. Use when setting up linting infrastructure, fixing code issues, or ensuring script portability.
Review a diff for clarity and safe simplifications, then optionally apply low-risk fixes.
Verifies code implements exactly what documentation specifies for blockchain audits. Use when comparing code against whitepapers, finding gaps between specs and implementation, or performing compliance checks for protocol implementations.
Standing house style to enforce dense, correct, and idiomatic code on all coding tasks. Minimizes code bloat and agent operation overhead.
Quick automated lint — detects common design system violations in seconds
Review UI code for design system compliance, accessibility, and best practices
Use when performing code review, writing or refactoring code, or discussing architecture; complements clean-code and does not replace project linter/formatter.
Rewrites code review comments so they read like a human teammate wrote them. Cuts corporate-AI throat-clearing ("I noticed...", "I was wondering if perhaps...", "It might be worth considering..."). Each comment is direct: location, the issue, a concrete fix.
Audit rapidly generated or AI-produced code for structural flaws, fragility, and production risks.
Human review workflow for AI-generated GitHub projects with spec-based feedback, security review, and follow-up PRs from the Vibers service.
Review generated or changed WordPress plugins, themes, and blocks for security, internationalization, performance, and API correctness.