Sun-Lab-NBB

Complete reference for ExtractionConfig parameters, generation from manifest, validation, and lifecycle. Covers the full extraction configuration data model, MCP tools for reading, writing, and validating configs, event code semantics, and config lifecycle workflow. Use when creating, reading, writing, or validating extraction configurations for the log processing pipeline.

Guides creation and configuration of MicroControllerInterface, ModuleInterface, and MQTTCommunication instances for microcontroller communication. Covers interface initialization and lifecycle, ModuleInterface subclassing with command and parameter sending, MQTTCommunication setup, controller ID allocation, and DataLogger integration. Use when writing code that creates MicroControllerInterface or MQTTCommunication instances or needs the ataraxis-communication-interface API.

Guides creation of custom hardware Module subclasses in the ataraxis-micro-controller C++ firmware library. Covers SetupModule, SetCustomParameters, and RunActiveCommand implementation, stage-based command execution, PACKED_STRUCT parameter structures, event codes, SendData patterns, and main.cpp integration with Kernel and Communication. Use when writing or modifying firmware Module subclasses, or when implementing the microcontroller side of an ataraxis-communication-interface module.

Guides creation and configuration of VideoSystem instances for camera acquisition. Covers constructor parameters, lifecycle methods, system ID allocation, and encoding selection. Use when writing code that creates VideoSystem instances or needs to understand the VideoSystem API.

End-to-end orchestration guide for the ataraxis-video-system recording and analysis pipeline. Covers canonical phase ordering with handoff conditions, multi-camera planning with system ID allocation and DataLogger topology, and decision trees for interface, encoding, and processing configuration. Use when planning a full recording workflow, setting up multi-camera rigs, or deciding between MCP and code.

Guides post-recording verification and handoff: log archive assembly, video file validation, output completeness checks, and readiness assessment for downstream log processing. Use after stopping a video session to verify outputs before processing frame timestamps.

Performs in-depth codebase exploration at the start of a coding session. Builds comprehensive understanding of project structure, architecture, key components, and patterns. Use when starting a new session, when asked to understand or explore the codebase, when asked "what does this project do", when exploring unfamiliar code, or when the user asks about project structure or architecture.

Explores installed ataraxis dependency source code to build a live API snapshot of the public classes, functions, and constants/enums each dependency exports, flagging where project code reimplements existing library functionality. Use when starting a session on a project with ataraxis dependencies, before writing code that uses ataraxis library features, or when the user asks about available library APIs.

Guides agents through preparing raw imaging data for cindra processing. Covers creating and validating cindra_parameters.json acquisition parameter files, gathering acquisition metadata from users, converting data from common microscope formats into cindra-compatible TIFFs, and organizing data directories. Use when the user needs to prepare data for processing, create acquisition parameter files, or convert existing data into cindra-compatible format.

Diagnoses and resolves cindra and cindra-gui MCP server connectivity issues. Covers environment verification, command availability, Python version checks, dependency validation, and conda/pip/uv environment configuration. Use when the cindra or cindra-gui MCP tools are unavailable, when either MCP server fails to start, when the user reports cindra connection issues, or when starting a session that requires the cindra MCP tools.

End-to-end orchestration guide for the cindra neural imaging pipeline and the entry point for cindra work. Covers canonical phase ordering with handoff conditions for the single-recording and multi-recording pipelines, the single-vs-multi-recording decision, dataset planning, and the MCP-first execution model. Use when planning a full processing workflow, deciding which pipeline to run, or orienting at the start of a cindra session.

Complete reference for multi-recording pipeline configuration parameters, prerequisites, and MCP configuration tools. Documents all 7 configuration sections, parameter meanings, default values, prerequisites from single-recording processing, and available MCP tools for generating configurations and discovering candidates. Use when configuring multi-recording processing or when the user asks about multi-recording configuration parameters.

Orchestrates multi-recording neural imaging batch processing via the cindra MCP server, dispatching to configuration and results skills as needed. Use when the user asks to run multi-recording (cross-day ROI tracking) processing, process multiple recordings as a tracked dataset, monitor multi-recording batch jobs, re-run a multi-recording processing phase, or when invoking /multi-recording-processing.

Complete reference for multi-recording pipeline output data formats. Documents every file, directory, array shape, dtype, and NPZ key produced by the pipeline, plus verification checklists for output completeness. Use when evaluating multi-recording processing results or when the user asks about multi-recording output data.

Complete reference for single-recording pipeline configuration parameters and MCP configuration tools. Documents all 9 configuration sections, parameter meanings, default values, and available MCP tools for generating configurations and discovering recordings. Use when configuring single-recording processing or when the user asks about single-recording configuration parameters.

Orchestrates single-recording neural imaging batch processing via the cindra MCP server, dispatching to acquisition-preparation, configuration, and results skills as needed. Use when the user asks to process single-recording imaging data, run the single-recording batch pipeline, monitor single-recording batch jobs, re-run a single-recording processing phase, or when invoking /single-recording-processing.