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.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

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 comprehensive workflow for managing feature flags, running A/B/multivariate experiments, and analyzing user cohorts using PostHog. The guide covers implementing flag evaluation both client-side (browser) and server-side (Node.js), and demonstrates how to programmatically set up, track, and query experiment results via API calls (cURL/Bash).

Execute controlled chaos experiments to proactively test system resilience, fault tolerance, and recovery capabilities. This toolkit simulates real-world failures, such as network latency, service crashes, resource exhaustion, and dependency outages, ensuring that microservices degrade gracefully and recover automatically. It is essential for modern distributed systems validation.

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.

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.

Provides guidance for organizing and navigating Jupyter Notebook workflows, focusing on structuring analysis, experiments, and reports within interactive Python sessions.

This skill transforms a refined research proposal or method idea into a detailed, structured, and claim-driven experiment roadmap. It helps users determine exactly which experiments to run, in what order, and what evidence is needed to support the core claims of a research paper, ensuring focus on novelty, simplicity, and genuine contribution.

This tool systematically generates, validates, and ranks concrete, publishable research ideas based on a broad research direction provided by the user. It guides the user through the entire idea discovery pipeline, from initial landscape survey (Phase 1) to validation (novelty checks, experiments) and review, ensuring the generated ideas are novel and relevant for academic publication. It manages complex workflow orchestration and utilizes multiple external tools.