external/react-best-practices/SKILL.md
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
npx skillsauth add seikaikyo/dash-skills react-best-practicesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Comprehensive performance optimization guide for React and Next.js applications, containing 40+ rules across 8 categories. Rules are prioritized by impact to guide automated refactoring and code generation.
Reference these guidelines when:
Rules are prioritized by impact:
| Priority | Category | Impact | |----------|----------|--------| | 1 | Eliminating Waterfalls | CRITICAL | | 2 | Bundle Size Optimization | CRITICAL | | 3 | Server-Side Performance | HIGH | | 4 | Client-Side Data Fetching | MEDIUM-HIGH | | 5 | Re-render Optimization | MEDIUM | | 6 | Rendering Performance | MEDIUM | | 7 | JavaScript Performance | LOW-MEDIUM | | 8 | Advanced Patterns | LOW |
Eliminate Waterfalls:
Promise.all() for independent async operationsbetter-all for partial dependenciesReduce Bundle Size:
next/dynamic for heavy componentsReact.cache() for per-request deduplicationstartTransition for non-urgent updatescontent-visibility: auto for long lists? : not &&)toSorted() instead of sort() for immutabilityFull documentation with code examples is available in:
references/react-performance-guidelines.md - Complete guide with all patternsreferences/rules/ - Individual rule files organized by categoryTo look up a specific pattern, grep the rules directory:
grep -l "suspense" references/rules/
grep -l "barrel" references/rules/
grep -l "swr" references/rules/
references/rules/async-* - Waterfall elimination patternsbundle-* - Bundle size optimizationserver-* - Server-side performanceclient-* - Client-side data fetchingrerender-* - Re-render optimizationrendering-* - DOM rendering performancejs-* - JavaScript micro-optimizationsadvanced-* - Advanced patternstools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.