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Found 2,362 Skills
SQL and Python-based employee performance analytics with KPI aggregation, departmental insights, and HR dashboard generation
Guardião da arquitetura de software no SynkOS. Use esta skill quando o usuário pedir para propor ou revisar a arquitetura de um sistema, avaliar tradeoffs entre tecnologias ou abordagens, criar um ADR (Architecture Decision Record), desenhar um modelo de dados ou contrato de API, ou fazer perguntas como "qual stack usar para X?", "como estruturar esse serviço?", "quais são os tradeoffs de Y vs Z?", "documente as decisões técnicas", "revise essa arquitetura". Ative também para discovery brownfield (entender o que já existe antes de propor mudanças), para cross-cutting concerns como segurança e performance, e para revisar designs propostos pelas equipes de implementação.
Run an autonomous Humanize-governed vLLM SOTA performance loop for one LLM model: first perform the fixed fair vLLM/SGLang/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches vLLM code, optionally uses ncu-report-skill for kernel evidence, and revalidates until vLLM matches or beats the best observed framework under the same workload and SLA.
Writes, reviews, and debugs idiomatic Rust code with memory safety and zero-cost abstractions. Implements ownership patterns, manages lifetimes, designs trait hierarchies, builds async applications with tokio, and structures error handling with Result/Option. Use when building Rust applications, solving ownership or borrowing issues, designing trait-based APIs, implementing async/await concurrency, creating FFI bindings, or optimizing for performance and memory safety. Invoke for Rust, Cargo, ownership, borrowing, lifetimes, async Rust, tokio, zero-cost abstractions, memory safety, systems programming.
DORA (DevOps Research and Assessment) Core Model for measuring and improving software delivery performance. Use this skill to assess team performance tier, identify capability gaps, and connect delivery metrics to product release strategy.
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.
Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Reduce JavaScript and CSS bundle sizes through code splitting, tree shaking, and optimization techniques. Improve load times and overall application performance.
AAA-caliber engineering council for building production-quality games. Use when implementing game systems, writing game code, designing data architecture, building UI components, creating tutorials, optimizing performance, or any technical game development task. Covers game programming patterns, casino/card game implementation, reward systems, game UI engineering, tutorial design, code architecture, data infrastructure, security, and quality assurance. Triggers on requests for game code, system implementation, refactoring, performance optimization, data design, or technical architecture decisions.
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.