nx-workspace
Explore and understand Nx workspaces. USE WHEN answering any questions about the nx workspace, the projects in it or tasks to run. EXAMPLES: 'What projects are in this workspace?', 'How is project X configured?', 'What targets can I run?', 'What's affected by my changes?', 'Which projects depend on library Y?', or any questions about Nx workspace structure, project configuration, or available tasks.
nx-gradle-plugin-version-bump
Bump the dev.nx.gradle.project-graph plugin version. Use when updating the Gradle project graph plugin version across the codebase, creating the migration files, and updating migrations.json.
nx-generate
Generate code using nx generators. USE WHEN scaffolding code or transforming existing code - for example creating libraries or applications, or anything else that is boilerplate code or automates repetitive tasks. ALWAYS use this first when generating code with Nx instead of calling MCP tools or running nx generate immediately.
run-nx-generator
Run Nx generators with prioritization for workspace-plugin generators. Use this when generating code, scaffolding new features, or automating repetitive tasks in the monorepo.
nx-docs-style-check
Check modified Nx documentation pages against the astro-docs style guide. Auto-trigger after writing or editing docs content in the nx repo. Also trigger on "check style", "style guide", "docs review", "validate docs". Should run as a final step whenever docs files are modified. IMPORTANT: anytime astro-docs/**/*.mdoc files are modified, this should always run automatically without being asked.
gpt-researcher
GPT Researcher is an autonomous deep research agent that conducts web and local research, producing detailed reports with citations. Use this skill when helping developers understand, extend, debug, or integrate with GPT Researcher - including adding features, understanding the architecture, working with the API, customizing research workflows, adding new retrievers, integrating MCP data sources, or troubleshooting research pipelines.
workflow-automation
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Swarm Orchestration
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 distributed AI systems.
add-model-descriptions
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
V3 Swarm Coordination
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
V3 Security Overhaul
Complete security architecture overhaul for claude-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation.
Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
worker-benchmarks
Run comprehensive worker system benchmarks and performance analysis
worker-integration
Worker-Agent integration for intelligent task dispatch and performance tracking
V3 Performance Optimization
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
swarm-orchestration
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V3 CLI Modernization
CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.
V3 Core Implementation
Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
Skill Builder
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 templates, or understand the Claude Skills specification.
ReasoningBank Intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
Pair Programming
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. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
memory-management
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neural-training
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hive-mind
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Hooks Automation
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre$post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
V3 DDD Architecture
Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
github-workflow-automation
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
hive-mind-advanced
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
github-automation
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flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
flow-nexus-platform
Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges
flow-nexus-swarm
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
github-code-review
Comprehensive GitHub code review with AI-powered swarm coordination
claims
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github-multi-repo
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
embeddings
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github-project-management
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
V3 Deep Integration
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
V3 Memory Unification
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
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.
agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
github-release-management
Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management
sparc-methodology
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agent-specification
Agent skill for specification - invoke with $agent-specification
security-audit
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browser
Web browser automation with AI-optimized snapshots for claude-flow agents
ReasoningBank with AgentDB
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
agent-swarm-issue
Agent skill for swarm-issue - invoke with $agent-swarm-issue
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