Use a branching decision-tree workflow builder to orchestrate complex automated bots that perform sequential checks and actions.

Embed multi-level decision making in AI systems by separating strategic, rolling analyses from near-real-time tactical event-driven tasks.

Leverage Jupyter Notebooks for iterative development and exploration of AI code rather than expecting one-click deployment from the outset.

Use an Agents MD file—a markdown-based specification—to declare your AI agent’s tools, workflows, and execution pointers in a human-readable yet machi...Show more

When building agents with the OpenAI API, define and register functions in a standardized JSON schema to drive uniform execution and response handling...Show more

Adopt a shared config-file approach (inspired by Claude skills) to enforce consistent LLM prompt structures and processing pipelines across your devel...Show more

Use LLM “skills” as modular scripts with guardrails to ensure consistent code generation across repeated queries by converting ad-hoc prompts into reu...Show more

Building a scoped tool like 'MCP Central' that exposes specific function calls with fixed one-shot prompts helps safely test and debug LLM-driven work...Show more

Downloading and storing API flat-file data like tickers CSV in a container allows pre-processing and improves query performance over streaming raw API...Show more

They use meta-prompting by instructing ChatGPT to generate a report-style prompt which then triggers an MCP tool call to fetch market data on demand.

Tom explains how to configure a remote server connector in ChatGPT by navigating to Settings→Connectors→Create and entering the server URL to integrat...Show more

He specifies including low/high IV charts, volatility surfaces, option greeks, and time-series modeling as standard analytics frameworks for any tradi...Show more

Cameron describes a set of modular UI chart components (low/high IV, standard option charts, strategy analysis) that can be picked up and integrated i...Show more

Build a 3D-style scenario modeling engine that visualizes option cost, payoff curves, break-even points, and volatility impact over time for different...Show more

Start with an MCP server embedded in a high-quality chat interface to ingest user data for evaluating positions or brainstorming options strategies be...Show more

Design an AI-enabled options trading copilot that generates new strategy ideas, evaluates existing positions, and autonomously manages portfolio actio...Show more

Use Claude’s MCP server integration to store your prompt engineering in reusable 'prompt' templates and inject variables at runtime via 'resource' def...Show more

Group related low-level MCP tools into a smaller set of high-level composite functions to drastically reduce initial token load and simplify call logi...Show more

Regularly compare AI models against domain-specific benchmarks like browser comp and coding tests to guide tool selection and validate improvements.

Use multi-agent architectures around smaller open-source GPT models to orchestrate systems-level workflows and compensate for individual model limitat...Show more