What problem does it solve? Brain modeling projects involve many error-prone stages—specification, literature verification, implementation, acceleration, experiment execution, and independent review—and losing track of progress or skipping validation steps produces unreliable scientific results. This Skill enforces a rigorous, checkpointed loop that keeps every stage auditable and resumable. ## Core Features & Use Cases - Checkpointed modeling loop: Maintains an append-only brainmodeling-memory.md so work can resume after interruptions without repeating completed experiments or reviews. - Specification and literature gate: Produces a locked NeuroSpecification.md and optionally triggers bio-neuro-lit research when mechanisms are unknown, unverified, or controversial. - Guided implementation and execution: Routes through BrainX package skills (braincell, brainpy-state, brainmass), acceleration parity checks, and immutable run snapshots via run-experiment and monitor-experiment references. - Independent Codex review: Sends each iteration's code and results to the Codex MCP server, preserving verbatim review reports and looping back on REFUSE verdicts. - Use Case: A researcher asks for a Hodgkin-Huxley parameter-fitting study; the Skill writes the specification, studies the relevant BrainX skills, implements and accelerates the model, runs the experiment, obtains a Codex review, and only visualizes results after the review passes. ## Quick Start Use brainx-modeling-loop to start a new BrainX modeling project from my research question, or resume the existing loop recorded in brainmodeling-memory.md.
