By Sun-Lab-NBB
Provides microcontroller discovery, extraction configuration, log processing, and pipeline orchestration skills for ataraxis-communication-interface. Includes MCP bindings for hardware discovery, manifest management, and batch data processing.
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.
Documents the input data format required by the log processing pipeline: NPZ log archives produced by DataLogger, source ID semantics, microcontroller manifest system, archive internal message layout, and communication protocol. Use when the user asks about log archive format, source IDs, DataLogger output, or why processing fails due to missing or malformed archives.
Complete reference for log processing output data formats, feather file discovery, output verification, event distribution analysis, and interpretation guidance. Use when evaluating log processing results, when the user asks about extracted event data, timing statistics, or microcontroller data quality.
Orchestrates batch log processing via the ataraxis-communication-interface MCP server: archive discovery, batch preparation, job execution, progress monitoring, cancellation, and error recovery. Use when processing microcontroller log archives, extracting hardware module and kernel data, or managing batch processing jobs.
Diagnoses and resolves ataraxis-communication-interface MCP server connectivity issues. Covers environment verification, command availability, Python version checks, dependency validation, and conda/pip/uv environment configuration. Use when MCP tools are unavailable, when the server fails to start, when the user reports connection issues, or when starting a session that requires MCP tools.
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Bridging AI Coding Assistants and Scientific Hardware
Ataraxis is an open-source framework that enables AI coding assistants to interact with laboratory hardware. It provides optimized hardware interface libraries, Model Context Protocol (MCP) servers for structured device discovery, and domain-specific skills that encode expert workflows. AI agents use these components to generate efficient data acquisition pipelines, configure systems, and troubleshoot hardware issues.
Core Insight: AI assistance operates at configuration time while runtime data acquisition remains deterministic and AI-independent. This separation ensures that network latency, API rate limits, or model errors never disrupt a running experiment.
Authored by Ivan Kondratyev. Copyright: 2026, NeuroAI Lab, Cornell University.
┌─────────────────────────────────────────────────────────────────────────────┐
│ Ataraxis Architecture │
├─────────────────────────────────┬───────────────────────────────────────────┤
│ Configuration Time │ Runtime (No AI) │
├─────────────────────────────────┼───────────────────────────────────────────┤
│ │ │
│ ┌─────────────────────┐ │ ┌─────────────────────────┐ │
│ │ AI Agent (Claude) │ │ │ Static Acquisition │ │
│ └──────────┬──────────┘ │ │ Pipelines │ │
│ │ │ └────────────┬────────────┘ │
│ ▼ │ │ │
│ ┌─────────────────────┐ │ ▼ │
│ │ Skills & MCP │ │ ┌─────────────────────────┐ │
│ │ Discovery Tools │ ─────┼────▶│ Ataraxis Libraries │ │
│ └──────────┬──────────┘ │ └────────────┬────────────┘ │
│ │ │ │ │
│ ▼ │ ▼ │
│ ┌─────────────────────┐ │ ┌─────────────────────────┐ │
│ │ Config Files & │ │ │ Physical Hardware │ │
│ │ Pipeline Code │ │ └────────────┬────────────┘ │
│ └─────────────────────┘ │ │ │
│ │ ▼ │
│ │ ┌─────────────────────────┐ │
│ │ │ Session Data & Logs │ │
│ │ └─────────────────────────┘ │
└─────────────────────────────────┴───────────────────────────────────────────┘
npx claudepluginhub sun-lab-nbb/ataraxis --plugin communicationProvides the shared software development skills that enforce conventions and code style in Ataraxis and derived projects.
Provides firmware module implementation skills for ataraxis-micro-controller.
Provides camera acquisition, video recording, log processing, and pipeline orchestration skills for ataraxis-video-system. Includes MCP bindings for interactive camera discovery, testing, and session management.
Provides neural imaging pipeline configuration, batch processing orchestration, results analysis, data preparation, visualization, and MCP environment setup skills for cindra. Includes MCP bindings for data processing and GUI viewer management.
Node Hardware MCP - Comprehensive Hardware Monitoring and System Analysis for LLMs with real-time performance metrics
Embedded & IoT engineer — firmware, microcontrollers, edge computing, device protocols
AI-powered hardware development platform — design, verify, synthesize, and deploy working RTL with natural language. 18 agents, 25 skills, 8 IP blocks.
Enterprise IoT Solutions Architect for device management, edge computing, and industrial IoT
Build FastMCP 3.x Python MCP servers — covers provider/transform architecture (including CodeMode, Tool Search, and server-level transforms), component versioning, session state, authorization (MultiAuth, PropelAuth, connection-pooled token verifiers), evaluation creation, Pydantic validation, async patterns, STDIO and HTTP transports, nginx reverse proxy deployment, background tasks, Prefab Apps UI, security patterns, client SDK usage, testing, deployment, and migration from FastMCP v2. TypeScript is a legacy reference only and is not updated for v3.
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