Skill UI

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 chaos engineering experiments by defining objectives, choosing tools such as Chaos Mesh, Gremlin, Toxiproxy, or AWS FIS, injecting failures, monitoring metrics, and recommending fixes to harden services and APIs.

Create and experiment with a minimal Evernote integration by scripting your first note, demonstrating ENML usage, note creation, listing notebooks, and retrieval flows for API learning or setup validation.

LibAFL is a modular fuzzing library in Rust that exposes advanced mutators, feedback, objectives, and schedulers so you can build bespoke fuzzers or use it as a drop-in replacement for libFuzzer when targeting non-standard architectures or research experiments.

Provides a minimal Mistral AI chat completion example in TypeScript and Python for bootstrapping a new integration, validating your setup, and experimenting with streaming responses.

Guides through creating a minimal Obsidian plugin that wires ribbon icons, command palette entries, editor insertion, settings tab, and modals so beginners can verify their setup and experiment with core API surface before extending to hot-reload workflows.

Guidance for implementing a production-grade PostHog setup, covering event taxonomy, client SDK wiring, feature flag architecture, experimentation tracking, and backend integration patterns so teams can align on best practices for analytics projects.

Run controlled chaos experiments to validate resilience, fault tolerance, and recovery of distributed services by injecting latency, outages, and resource exhaustion while monitoring metrics and rollback safeguards.

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.

Cirq is Google Quantum AI’s Python framework for building, simulating, and deploying noise-aware quantum circuits on both simulators and real hardware, complete with experiment templates, transformation utilities, and hardware-specific toolkits.

This skill provides complete, programmatic integration with the protocols.io API v3, enabling advanced management of scientific protocols. Use it for the entire protocol lifecycle, including discovery by DOI or keywords, creating new protocols, managing steps and materials, coordinating collaborative development, organizing workspaces, and tracking experiments. It covers authentication, file handling, and discussion management, making it ideal for automated lab workflows and scientific documentation systems.

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.