Results for “lab-automation”

Write Opentrons Protocol API v2 protocols for Flex and OT-2 robots to automate liquid handling, control hardware modules, and manage labware configurations.

Laboratory automation toolkit for controlling liquid handlers, plate readers, pumps, heater shakers, incubators, centrifuges, and analytical equipment. Use this skill when automating laboratory workflows, programming liquid handling robots (Hamilton STAR, Opentrons OT-2, Tecan EVO), integrating lab equipment, managing deck layouts and resources (plates, tips, containers), reading plates, or creating reproducible laboratory protocols. Applicable for both simulated protocols and physical hardware control.

Master AI-powered test automation with modern frameworks, self-healing tests, and comprehensive quality engineering. Build scalable testing strategies with advanced CI/CD integration. Use PROACTIVELY for testing automation or quality assurance.

Queries and displays Labstep electronic lab notebook data — experiments, protocols, resources, and inventory — via labstepPy, with an offline demo mode using synthetic biology data.

Orchestrate parallel scientist agents for comprehensive analysis with AUTO mode

Analyze a screen recording of a manual process and produce targeted, working automation scripts. Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation at multiple complexity levels using tools already installed on the user machine.

Use when designing or auditing the evaluation of an ASE (IEEE/ACM Automated Software Engineering) paper, covering real subject systems, fair runnable tool baselines, task-matched effectiveness metrics, ablations that isolate a learned component, oracle and correctness validation, contamination-aware LLM handling, and provenance for mining.

Access and manage LabArchives electronic lab notebooks programmatically via REST API. Create entries, upload attachments, backup notebooks, generate reports, and integrate with Protocols.io, Jupyter, REDCap, and other scientific tools.

Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".

Full autonomous research workflow — brainstorm, plan, implement, review, and document

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Query experiments, protocols, resources, and inventory in the Labstep electronic lab notebook using the labstepPy API.

Automates CI/CD pipeline setup, quality gates, test runners, and deployment strategies for GitHub Actions.

Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.

Produces an evidence-backed inventory of live automations (jobs, hooks, connectors, MCP servers, wrappers) and recommends keep/merge/cut/fix-next actions before rewriting anything.

Use this skill for GitHub Actions, checks, release gates, deployment automation, reproducible pipelines. Trigger when the task involves devops work related to CI CD Automation, production implementation, audits, debugging, strategy, or validation.

Manages A/B tests by running a Python script that processes input and produces JSON results.

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

CICD Fleet

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

Design and validate digital filters in MATLAB. Use when cleaning up noisy signals, removing interference, filtering signals, designing FIR/IIR filters (lowpass/highpass/bandpass/bandstop/notch), or comparing filters in Filter Analyzer.

Automate ElevenLabs text-to-speech workflows: generate speech from text, browse and inspect voices, check subscription limits, list models, stream audio, and retrieve history via the Composio MCP integration.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

Automates hypothesis generation and testing on tabular datasets using LLMs, combining data-driven discovery with literature integration for scientific research.

Wilkinson, branchline, ratrace, directional couplers, corporate dividers, Rotman lenses for power splitting and beam-forming. TRIGGER: user asks to design, create, or analyze any coupler, splitter, power divider, combiner, or Rotman lens. Invoke BEFORE writing code — class names and design() availability vary per coupler type. SKIP: EM simulation/S-parameter extraction of an existing component (use matlab-analyze-em), building custom non-catalog geometry (use matlab-assemble-pcb-layout), material/stackup setup only (use matlab-manage-pcb-material), cascading multiple components (use matlab-integrate-pcb-circuit).

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

实验判决门：Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.