machine-learning
32.1K
multi-agent-brainstorming
Simulate a structured peer-review process using multiple specialized agents to validate designs, surface hidden assumptions, and identify failure modes before implementation.
sickn33
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
32.1K
scikit-learn
Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
sickn33
data-ai
machine-learning
32.1K
statsmodels
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.
sickn33
data-ai
machine-learning
32.1K
yann-lecun-tecnico
Sub-skill técnica de Yann LeCun. Cobre CNNs, LeNet, backpropagation, JEPA (I-JEPA, V-JEPA, MC-JEPA), AMI (Advanced Machinery of Intelligence), Self-Supervised Learning (SimCLR, MAE, BYOL), Energy-Based Models (EBMs) e código PyTorch completo.
sickn33
data-ai
data-engineering
32K
system-table-change
Use when adding, removing, or modifying columns/indexes on system tables. Provides a checklist covering schema definitions, migrations, version gates, golden files, and test hashes.
cockroachdb
data-ai
data-engineering
31.2K
agentdb-advanced-features
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.
ruvnet
data-ai
data-engineering
31.2K
agentdb-performance-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.
ruvnet
data-ai
data-engineering
31.2K
memory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
ruvnet
data-ai
data-engineering
31.2K
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
ruvnet
data-ai
data-engineering
31.2K
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
ruvnet
data-ai
data-engineering
31.2K
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.
ruvnet
data-ai
data-engineering
31.2K
agentdb-advanced-features
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.
ruvnet
data-ai
data-engineering
31.2K
agentdb-performance-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.
ruvnet
data-ai
data-engineering
31.2K
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
ruvnet
data-ai
data-engineering
31.2K
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
ruvnet
data-ai
data-engineering
31.2K
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.
ruvnet
data-ai
data-engineering
31.2K
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).
ruvnet
data-ai
llm-ai
31.2K
agent-adaptive-coordinator
Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator
ruvnet
data-ai
llm-ai
31.2K
agent-agentic-payments
Agent skill for agentic-payments - invoke with $agent-agentic-payments
ruvnet
data-ai
llm-ai
31.2K
agent-analyze-code-quality
Agent skill for analyze-code-quality - invoke with $agent-analyze-code-quality
ruvnet
data-ai