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lingxling

5.681 skills 801.021 estrelas no total GitHub ↗

Skills publicadas

Mostrando 12 de 5.681

azure-resource-manager-redis-dotnet

141

Azure Resource Manager SDK for Redis in .NET.

Design e Frontend por lingxling

apify-competitor-intelligence

141

Analyze competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, YouTube, and TikTok.

Design e Frontend #api por lingxling

ai-seo

141

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

Design e Frontend #ai #llm por lingxling

zenserp-automation

141

Automate Zenserp tasks via Rube MCP (Composio). Always search tools first for current schemas.

Design e Frontend #mcp por lingxling

framework-migration-legacy-modernize

141

Orchestrate a comprehensive legacy system modernization using the strangler fig pattern, enabling gradual replacement of outdated components while maintaining continuous business operations through ex

Design e Frontend #ai por lingxling

multi-agent-task-orchestrator

141

Route tasks to specialized AI agents with anti-duplication, quality gates, and 30-minute heartbeat monitoring

Design e Frontend #ai por lingxling

linkedin-cli

141

Use when automating LinkedIn via CLI: fetch profiles, search people/companies, send messages, manage connections, create posts, and Sales Navigator.

Design e Frontend por lingxling

stripe-integration

141

Master Stripe payment processing integration for robust, PCI-compliant payment flows including checkout, subscriptions, webhooks, and refunds.

Design e Frontend por lingxling

seo-aeo-schema-generator

141

Generates valid JSON-LD structured data for 10 schema types with rich result eligibility validation and implementation-ready script blocks. Activate when the user wants to generate schema markup, JSON-LD, or structured data for any page.

Design e Frontend #seo por lingxling

stable-baselines3

141

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

Design e Frontend #ai #api por lingxling

personal-tool-builder

141

Expert in building custom tools that solve your own problems first.

Design e Frontend por lingxling

cdk-patterns

141

Common AWS CDK patterns and constructs for building cloud infrastructure with TypeScript, Python, or Java. Use when designing reusable CDK stacks and L3 constructs.

DevOps e Infra #python #typescript por lingxling