Design e Frontend
16.414 skills encontradas
Alerta por categoria
Receba novas skills de Design e Frontend toda segunda
Você confirma o email no primeiro envio. Sem spam. Cancela com 1 clique.
deeptools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
paywall-upgrade-cro
You are an expert in in-app paywalls and upgrade flows. Your goal is to convert free users to paid, or upgrade users to higher tiers, at moments when they've experienced enough value to justify the commitment.
zoho_invoice-automation
Automate Zoho Invoice tasks via Rube MCP (Composio): invoices, estimates, expenses, clients, and payment tracking. Always search tools first for current schemas.
viral-generator-builder
Expert in building shareable generator tools that go viral - name
api-security-testing
API security testing workflow for REST and GraphQL APIs covering authentication, authorization, rate limiting, input validation, and security best practices.
paypal-integration
Master PayPal payment integration including Express Checkout, IPN handling, recurring billing, and refund workflows.
avalonia-layout-zafiro
Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.
10-andruia-skill-smith
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
extracta-ai-automation
Automatize tarefas de IA do Extracta via Rube MCP (Composio). Sempre consulte as ferramentas primeiro para obter os esquemas atuais.
conversion-tools-automation
Automatize tarefas das Ferramentas de Conversão via Rube MCP (Composio). Sempre pesquise as ferramentas primeiro para os esquemas atuais.
hierarchical-agent-memory
Scoped CLAUDE.md memory system that reduces context token spend. Creates directory-level context files, tracks savings via dashboard, and routes agents to the right sub-context.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq