Databases

Query and explore SAP OData endpoints with secure logging and configurable output. Use this skill when the user needs to fetch data from SAP systems, explore SAP entity structures, or query SAP business objects like BusinessPartner, SalesOrder, Product, etc.

Performance tuning for AgentDB across indexing, storage, and query paths.

Comprehensive validation methodology for multi-component applications including backend, frontend, database, and infrastructure

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Use Codex CLI for sandboxed auditing, debugging, and autonomous prototyping

Comprehensive prompt iteration workflow for debugging and improving Langfuse prompts. Analyzes codebase usage, sets up reproducible tests, runs iterative refinement cycles with user feedback. Use when user says "prompt X isn't working right", "improve prompt", "iterate on prompt", "refine prompt", or "test prompt changes".

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.

Use Codex CLI Full Auto for unattended, sandboxed prototyping and refactors.

Vector search and semantic memory backbone for agents and RAG pipelines.

Notion API for managing pages, databases, and blocks. Use this skill to create pages, query databases, search content, and build integrations with Notion workspaces.

AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.

Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:

Quality gate before responding to user. Reviews proposed response against original query, checks skill usage, iterates 50% of the time for accuracy.

INVOKE BEFORE writing new code to find existing implementations (DRY). Also for codebase exploration and production data search. Run: docker exec arsenal-semantic-search-cli code-search find 'query'

Query Google NotebookLM for source-grounded, citation-backed answers from uploaded documents. Reduces hallucinations through Gemini's document-only responses. Browser automation with library management and persistent authentication.

Use Codex CLI sandbox mode to try risky changes safely. Isolated experimentation with network disabled and directory restrictions.

Qdrant vector database REST API via curl. Use this skill to store, search, and manage vector embeddings.

Retrieve all review comments from a pull request using the GitHub API. Use when you need to see what feedback has been provided on a PR.

Regel 07: CQRS - Command Query Responsibility Segregation. Use when implementing DDD patterns.

AgentDB Vector Search Optimization operates on 3 fundamental principles: