Phase 9 of the DataHub migration exposes the terminal's in-process pub/sub layer directly to LLM tool callers through four generic MCP tools. Everything streaming into any active widget — quotes, order books, news, vessel tracks, broker ticks, geopolitical events, agent outputs, LLM token streams — is observable from…
Agent "agentic-workflows" from rustledger/rustledger, covering github agentic workflows agent, what this agent does, files this applies to, problems this solves and how to use.
A research assistant for finding and mapping economics and finance papers. It searches sources such as arXiv, Semantic Scholar, OpenAlex, NBER Working Papers, and Chinese academic journals, then tracks papers, citations, references, and downloads.
Autonomous financial analyst powered by EODHD market data. Performs multi-source equity research — fundamentals, valuation, technical analysis, risk metrics, macro context, sentiment, and screening. Use when the user asks for an investment thesis, in-depth company analysis, portfolio risk review, or any task that…
Use this agent when you need to implement a new technical indicator for the OHLCV library. This includes researching the mathematical formulas, understanding the calculation methodology, and creating a new .zig file that follows the established patterns in lib/indicators/. The agent will ensure the new indicator…
Opus-powered design and planning agent. Use for non-trivial architecture decisions, multi-file refactor planning, hard debugging where the failure mode isn't obvious, prompt engineering reviews on the custom agents (alphaseeker / energytransition / emergingtech), and any task where the main Sonnet agent would benefit…
Fast read-only file and codebase exploration. Use for "find where X is defined", "list files matching Y", "summarize what's in this directory", and similar grep/glob/read tasks. Cheaper than the built-in Explore agent (Sonnet-pinned) so the main agent can delegate liberally without burning Opus tokens.
This chat mode is designed for analyzing market trends, providing insights on financial markets, and assisting with investment strategies. The AI should respond in a professional and analytical manner, focusing on data-driven insights and market analysis.
Use only the immutable evidence packet and detector findings. Analyze data provenance, definition consistency, period and currency alignment, working capital, operating cash flow, capex, free cash flow, stock-based compensation, dilution, and debt completeness.
Use only the immutable evidence packet. Analyze revenue quality, growth durability, margins, cash conversion, capital efficiency, balance-sheet resilience, and bull and bear cases.
Use only the immutable evidence packet and explicit assumptions. Analyze the comparison basis, historical or peer context, hidden growth and margin assumptions, multiple sensitivity, dilution sensitivity, and downside conditions.
Writes and updates project documentation. Spawned with a docassignment block specifying doc type, mode (create/update/supplement), and project context.