rag-assistant
Knowledge base assistant that retrieves and cites documents from a curated index. Use when answering questions that must be grounded in specific source material with citations.
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Knowledge base assistant that retrieves and cites documents from a curated index. Use when answering questions that must be grounded in specific source material with citations.
Market research specialist that searches the web for market data, competitor intelligence, and industry reports, with curated fallback data when offline. Use when gathering external evidence for strategic decisions.
REQUIRED whenever the user asks what Tracecat is, what it can do, how to do something in the product, or where to find a feature in the UI — e.g. "how do I add a secret", "where are my integrations", "what's the difference between a case and a workflow", "can Tracecat send Slack messages", "how do I schedule a workflow", "set up an OAuth integration". Read this SKILL.md FIRST when orienting a user, explaining a concept, or directing them to a page. It covers the platform mental model, what the product offers, where each feature lives in the UI, and the core concepts (secrets vs variables vs expressions). For building/editing workflows, use tracecat-manage-workflows instead.
Queries U.S. Bureau of Labor Statistics data using the BLS MCP server. Use when user asks about CPI, inflation, unemployment, employment, wages, jobs, labor statistics, producer prices, or any economic indicator tracked by BLS. Maps natural language to correct series IDs and tools. Copied from https://github.com/larasrinath/bls_mcp
How to conduct ad-hoc analyses: gather data from multiple sources, synthesize findings, save reusable analysis artifacts that anyone can re-run for fresh results.
How to look up version-matched Agent Native framework docs and source in node_modules. Use before coding against @agent-native/core APIs or advanced features.
Use when a Brain question asks across company memory plus live/app-owned data such as metrics, email threads, workspace grants, or secrets.
Explain and reason about Dynamic Consistency Boundaries (DCB) and how Axon Framework 5 and Axon Server implement them. Use when the user asks about "DCB", "dynamic consistency boundary", "consistency boundary", "killing the aggregate", modeling consistency across multiple entities, "EventCriteria", "@EventCriteriaBuilder", "@EventTag", "@EventSourcedEntity", "Tag", tags vs streams, "AppendCondition", "SourcingCondition", "ConsistencyMarker", optimistic concurrency in the event store, how to choose a consistency boundary, why an aggregate is too big or too small, or how Axon Server's DCB event store (dcb.proto) sources and appends events with criteria.
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.