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Found 24 Skills
Audit Lightning Web Components for SLDS compliance and produce a scored quality report. Runs the SLDS linter, analyzes CSS for theming hook usage and pairing, checks HTML for accessibility attributes, and scores findings across categories into an overall grade. Use when asked to "score my component", "SLDS scorecard", "quality report", "audit SLDS compliance", "how good is my SLDS", "check component quality", "rate my component", "evaluate my component", "is this component ready to ship?", "look at my LWC for issues", "audit this before I submit", "review my component before code review", or any time a user wants a quality assessment or production-readiness check on an LWC or SLDS component. Not for fixing violations (use uplifting-components-to-slds2) or building new components (use applying-slds).
Analyze and transform messy, prototype, overgrown, slop-prone, or hard-to-maintain software repositories into maintainable product-shaped codebases while preserving existing product behavior. Use when the user asks to antislop a codebase, clean up a messy repo, run a maintainability migration, write a refactor plan, modernize structure, improve TypeScript/type boundaries, harden tests, reduce large files, clean architecture, coordinate subagent-driven refactors, or produce a final migration audit/report/microsite. Do not use for broader production-readiness specialties such as security audits, observability/logging programs, compliance hardening, SRE/runbook work, or reliability engineering unless the user explicitly scopes those as part of the maintainability refactor.
Use when the user asks for a broad codebase review, substantial PR/branch review, architecture audit, tech-debt scan, cleanup assessment, structural sanity check, or design-alignment review. Default workflow: use sub-agents when available unless specifically forbidden; do not require the user to mention sub-agents, council mode, delegation, or parallel review. Focus on cruft, duplication, weak boundaries, missed reuse, lifecycle/concurrency risks, test/roadmap drift, and code aesthetics. Do not use for narrow bug fixes, ordinary small-diff reviews, frontend visual QA, repo-onboarding docs, or OpenAI Agents SDK production-readiness review. Output evidence-backed findings first, then pressure points, design alignment, open questions, and follow-through.
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
After building a feature, verify it matches what was planned, respects the system architecture and design standards, and is ready for production. Reports issues clearly so the developer decides what to fix.
Distinguished Principal Engineer backend/system architecture skill. Use when the user demands "BackendPE", "Supermode", "Antigravity", or requests high-performance, unlimited-context, world-class backend and distributed systems design. This skill maximizes depth, rigor, and production readiness.
Production readiness checklist for Gamma integration. Use when preparing to deploy Gamma integration to production, or auditing existing production setup. Trigger with phrases like "gamma production", "gamma prod ready", "gamma go live", "gamma deployment checklist", "gamma launch".
Audit rapidly generated or AI-produced code for structural flaws, fragility, and production risks.
Comprehensive quality audit for Claude Code agents, skills, and commands with comparative analysis
Strategic automation architecture advisor. Use when users want to plan automation solutions, evaluate their tech stack (Shopify, Zoho, HubSpot, etc.), decide between n8n vs Python/Claude Code, or need guidance on production-ready automation design. Invokes plan mode for complex architectural decisions.
Senior Node.js developer. Use when building, reviewing, or refactoring Node.js applications. Enforces modern Node.js 22+ patterns, native APIs, performance, and production-ready practices.
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.