io.github.Ed1s0nZ/CyberStrikeAI/sql-injection-testing

SQL注入测试的专业技能和方法论

io.github.Ed1s0nZ/CyberStrikeAI/mobile-app-security-testing

移动应用安全测试的专业技能和方法论

io.github.Ed1s0nZ/CyberStrikeAI/security-automation

安全自动化的专业技能和方法论

io.github.Ed1s0nZ/CyberStrikeAI/security-awareness-training

安全意识培训的专业技能和方法论

io.github.Ed1s0nZ/CyberStrikeAI/api-security-testing

API安全测试的专业技能和方法论

io.github.luongnv89/claude-howto/refactor

Systematic code refactoring based on Martin Fowler's methodology. Use when users ask to refactor code, improve code structure, reduce technical debt, clean up legacy code, eliminate code smells, or improve code maintainability. This skill guides through a phased approach with research, planning, and safe incremental implementation.

io.github.davila7/claude-code-templates/react-best-practices

Comprehensive React and Next.js performance optimization guide with 40+ rules for eliminating waterfalls, optimizing bundles, and improving rendering. Use when optimizing React apps, reviewing performance, or refactoring components.

io.github.rominirani/antigravity-skills/database-schema-validator

Validates SQL schema files for compliance with internal safety and naming policies.

io.github.ComposioHQ/awesome-codex-skills/mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

io.github.xenitV1/Antigravity-Workflows/mobile-design

Mobile-first design thinking and decision-making for iOS and Android apps. Touch interaction, performance patterns, platform conventions. Teaches principles, not fixed values. Use when building React Native, Flutter, or native mobile apps.

io.github.xenitV1/Antigravity-Workflows/nextjs-best-practices

Next.js App Router principles. Server Components, data fetching, routing patterns.

io.github.agentscope-ai/agentscope-java/writing-skill

A skill for writing and content creation

io.github.harperreed/dotfiles/swiftui-view-refactor

Refactor and review SwiftUI view files for consistent structure, dependency injection, and Observation usage. Use when asked to clean up a SwiftUI view’s layout/ordering, handle view models safely (non-optional when possible), or standardize how dependencies and @Observable state are initialized and passed.

io.github.ruvnet/RuView/github-workflow-automation

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

io.github.ruvnet/RuView/github-release-management

Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management

io.github.ruvnet/RuView/github-project-management

Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning

io.github.ruvnet/RuView/github-multi-repo

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

io.github.ruvnet/RuView/github-code-review

Comprehensive GitHub code review with AI-powered swarm coordination

io.github.ruvnet/RuView/v3-mcp-optimization

MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.

io.github.ruvnet/RuView/agentdb-vector-search

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

io.github.ruvnet/RuView/agentdb-optimization

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

io.github.ruvnet/RuView/agentdb-memory-patterns

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

io.github.ruvnet/RuView/agentdb-learning

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

io.github.ruvnet/RuView/agentdb-advanced

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.