Skill UI

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.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

An advanced workflow management skill designed for executing large-scale, multi-seed, or multi-stage Machine Learning experiments across remote GPU clusters via SSH. It manages complex dependencies, handles Out-of-Memory (OOM) retries, prevents race conditions between experimental 'waves,' and ensures robust, reproducible deep learning pipeline execution.

This tool functions as a post-task epilogue, designed to audit and record the entire research journey. It scans the conversation history at the end of a coding or research session to extract critical research artifacts, including decisions, experiments, dead ends, claims, heuristics, and major pivots. It systematically writes this provenance data into the structured `ara/` directory, ensuring a faithful and auditable trace of how the project truly evolved.

This pipeline orchestrates the process of adapting a highly polished, existing academic paper for resubmission to a new conference or journal. It specializes in text-only microedits, integrating prior reviewer feedback (review corpus) while strictly forbidding new experiments, new citations, or major structural changes. It is designed for high-stakes, time-constrained resubmissions.

This skill guides the entire chaotic engineering lifecycle, enabling teams to design safe, controlled experiments that surface weaknesses in production systems without causing actual outages. It provides tools for defining hypotheses, calculating blast radius (risk assessment), and generating structured post-mortem reports. Essential for SRE teams, Game Days, and validating system resilience.

This guide provides a comprehensive framework for designing statistically rigorous A/B tests, split tests, and multivariate experiments. It covers everything from formulating strong hypotheses and calculating sample sizes to selecting appropriate primary, secondary, and guardrail metrics for various business contexts.

This expert framework guides users through the systematic process of conducting rigorous A/B tests and growth experiments. It covers all stages: formulating strong hypotheses, selecting appropriate metrics (primary, secondary, guardrail), calculating necessary sample sizes, and choosing the best implementation method (client-side vs. server-side). Use this when planning to compare two versions or build a systematic growth experimentation program.