探索开发、内容、数据与办公效率等 AI 技能。先比较用途和仓库热度，再查看技能说明与来源。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building d...
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Fe...
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
Create and run custom background analysis workers with composable phases. Use when you need automated code analysis, security scanning, pattern learning, or API documentation generation.
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Comprehensive GitHub code review with AI-powered swarm coordination
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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 t...
Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill...
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling,...
Prompt engineering expert that helps users craft optimized prompts using 57 proven frameworks. Use when users want to optimize prompts, improve AI instructions, create better prompts for specific tasks, or need...
Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six t...
Python code style and formatting standards using Ruff. Use when writing or reviewing Python code.
Review a GitHub pull request for a Rust codebase. Focuses on security, performance, test coverage, and Rust idioms. Runs in GitHub Actions and posts comments directly to the PR.
Use when user asks 'how should I...' or 'what's the best approach...' after exploring code, OR when you've tried to solve something and are stuck, OR for unfamiliar workflows, OR when user references past work....
Superdesign is a design agent, where it specialised in frontend UI/UX design; Use this skill before you implement any UI that require some design thinking. Common commands: superdesign create-project --title "X...
Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For...
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failin...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。