Skill UI

This is the Azure Resource Manager SDK for .NET, designed to streamline the deployment and management of Weights & Biases (W&B) ML experiment tracking instances directly through Azure Marketplace. It allows developers to programmatically create, configure, and manage W&B environments, including setting up marketplace details, managing admin users, configuring Single Sign-On (SSO), and ensuring robust ML observability within an Azure ecosystem. Ideal for MLOps pipelines.

This is the Azure Resource Manager SDK for .NET, designed to streamline the deployment and management of Weights & Biases (W&B) ML experiment tracking instances directly through Azure Marketplace. It allows developers to programmatically create, configure, and manage W&B environments, including setting up marketplace details, managing admin users, configuring Single Sign-On (SSO), and ensuring robust ML observability within an Azure ecosystem. Ideal for MLOps pipelines.

Designs and executes chaos experiments, failure-injection frameworks, and game days for distributed systems, providing runbooks, manifests, rollback plans, and continuous resilience automation in CI/CD.

Guides use of the Adaptyv Bio Foundry API and Python SDK to design protein experiments, submit sequences, automate workflows, and fetch BLI/SPR or thermostability assay results via authenticated requests for lab-ready studies.

PyLabRobot is a vendor-agnostic, pure Python SDK designed for autonomous and automated laboratory workflows. It provides a unified interface to control a wide array of laboratory equipment, including liquid handling robots (Hamilton, Opentrons, Tecan), plate readers, pumps, shakers, and incubators. Ideal for developing complex, multi-device, reproducible protocols and simulating experiments before physical execution.

A comprehensive guide and SDK wrapper for utilizing the Adaptyv Bio Foundry API. This skill enables users to design, submit, monitor, and retrieve results for various protein characterization assays, including binding (BLI/SPR), thermostability, and expression. Users can programmatically manage the entire experimental lifecycle using Python.

Cirq is an open-source Python framework from Google Quantum AI for designing, simulating, and executing quantum circuits. It supports multiple hardware backends (IonQ, Pasqal, etc.) and includes comprehensive tools for noise modeling, circuit optimization, and parameterized experimentation, making it ideal for NISQ device development and advanced quantum computing research.

This skill orchestrates the constrained resubmission of a highly polished academic paper from one conference or workshop venue to another. It is designed for scenarios where the paper is finalized, and the goal is to absorb specific reviewer critiques (e.g., clarifying statements or minor edits) without introducing new experiments, changing the core bibliography, or restructuring the framework. It ensures the new submission is isolated in a sibling directory, preserving the integrity of prior submissions.

This skill guides the safe execution of chaos engineering experiments to surface weaknesses in production systems without causing actual outages. It provides tools to design robust experiments by defining hypotheses, calculating the potential 'blast radius' before failure injection, setting strict abort criteria, and generating structured postmortems. Ideal for SRE teams running game days and resilience tests.

A comprehensive guide for designing and presenting rigorous experiments and simulations for top-tier journals in Control Theory, Robotics, and Smart Systems. Covers RQ-driven design, establishing fair baselines, providing theoretical proofs of stability/convergence, performing ablation studies, and ensuring statistical significance, helping authors build an undeniable evidence chain for double-blind external review.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.

This guide outlines best practices for designing and executing Computer Aided Verification (CAV) empirical evaluations. It emphasizes ensuring evidence matches claims by using standard, pinned benchmark sets, selecting fair baselines, respecting equal resource limits, and employing rigorous reporting methods like cactus and scatter plots to establish technical soundness and performance.