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Found 1,654 Skills
Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.
Connect to Postgres databases, run SQL and diagnostics, inspect schemas and migrations, review query performance, and use common PostGIS or pgvector patterns.
Maps multi-phase trajectories with dependencies into clear, sequenced roadmaps. Use when work has multiple phases that need sequencing, when decisions today affect future decisions, when stakeholders need to see the whole journey, or when external dependencies exist. Applicable regardless of total duration — a 4-week multi-phase initiative benefits as much as a quarterly roadmap.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Test if user signup is open and identify potential abuse vectors in the registration process.
Use when user needs Active Directory security analysis, privileged group design review, authentication policy assessment, or delegation and attack surface evaluation across enterprise domains.
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.
Amazon Bedrock Knowledge Bases for RAG (Retrieval-Augmented Generation). Create knowledge bases with vector stores, ingest data from S3/web/Confluence/SharePoint, configure chunking strategies, query with retrieve and generate APIs, manage sessions. Use when building RAG applications, implementing semantic search, creating document Q&A systems, integrating knowledge bases with agents, optimizing chunking for accuracy, or querying enterprise knowledge.
Persistent shared memory for AI agents backed by PostgreSQL (fts + pg_trgm, optional pgvector). Includes compaction logging and maintenance scripts.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
Semantic skill discovery and routing using GraphRAG, vector embeddings, and multi-tool search. Automatically matches user intent to the most relevant skills from 144+ available options using ck semantic search, LEANN RAG, and knowledge graph relationships. Triggers on /meta queries, complex multi-domain tasks, explicit skill requests, or when task complexity exceeds threshold (files>20, domains>2, complexity>=0.7).
Configure the OpenTelemetry Collector with Sentry Exporter for multi-project routing and automatic project creation. Use when setting up OTel with Sentry, configuring collector pipelines for traces and logs, or routing telemetry from multiple services to Sentry projects.