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Found 2,753 Skills
ETL pipeline and analytics application for Harvard Art Museums API with SQL storage and Streamlit visualization
Use when operating the vigolium CLI for web vulnerability scanning, security testing, traffic ingestion, server management, AI agent-driven scanning and code review, cloud-storage management, or writing custom JavaScript extensions. Invoke for scan commands, scan-url, scan-request, run, ingest, server, agent (query/autopilot/swarm/olium/piolium/audit/session), traffic browsing, database queries, storage uploads/downloads, module management, extension scripting, export, project management, and configuration tuning.
This skill should be used when the user wants to interact with their paper database — listing papers, searching content, showing paper details, adding papers, or exporting context. Matches queries like "search papers for X", "add this arXiv paper", "show equations from paper Y", "what papers do I have". Prefer CLI over MCP RAG tools for direct lookups.
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.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.
Use when asking about 'FileProtectionType', 'file encryption iOS', 'NSFileProtection', 'data protection', 'secure file storage', 'encrypt files at rest', 'complete protection', 'file security' - comprehensive reference for iOS file encryption and data protection APIs
Guide for implementing Grafana Mimir - a horizontally scalable, highly available, multi-tenant TSDB for long-term storage of Prometheus metrics. Use when configuring Mimir on Kubernetes, setting up Azure/S3/GCS storage backends, troubleshooting authentication issues, or optimizing performance.
Pre-ingestion verification for epistemic quality in RAG systems with 9-point verification and Two-Round HITL workflow
Yjs CRDT patterns, shared types, conflict resolution, and meta data structures. Use when building collaborative apps with Yjs, handling Y.Map/Y.Array/Y.Text, implementing drag-and-drop reordering, or optimizing document storage.
PyTiDB (pytidb) setup and usage for TiDB from Python. Covers connecting, table modeling (TableModel), CRUD, raw SQL, transactions, vector/full-text/hybrid search, auto-embedding, custom embedding functions, and reference templates/snippets (vector/hybrid/image) plus agent-oriented examples (RAG/memory/text2sql).
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.
Comprehensive testing specialization covering test strategy, automation, TDD methodology, test writing, and web app testing. Use when setting up test infrastructure, writing tests, implementing TDD workflows, analyzing coverage, integrating tests into CI/CD, or testing web applications with Playwright. Framework-agnostic approach with framework-specific guidance via reference files.