Skill UI

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 skill guides the implementation of security chaos engineering experiments. It involves deliberately degrading or disabling security controls (like WAF, firewalls, or log pipelines) in a controlled environment to rigorously verify the detection, alerting, and response capabilities of the Security Operations Center (SOC). Users learn to use tools like boto3 and Python to simulate failure scenarios and assess system resilience and compliance posture.

This skill guides the process of evaluating research results against intended scientific claims. After running experiments, it systematically collects data from various sources (W&B, logs, etc.), performs a deterministic evidence pre-check to detect hallucinated evidence, and finally uses a Codex judgment to determine if the results support the claims, requiring next actions like pivoting or supplementing the findings.

This skill provides comprehensive guidance on utilizing the Adaptyv Bio Foundry API and Python SDK. It enables users to programmatically design, submit, and track protein characterization experiments. Supports various assays, including binding kinetics (BLI/SPR), thermostability, and expression analysis, allowing researchers to convert amino acid sequences into actionable, automated experimental data.

PyLabRobot is a pure Python, vendor-agnostic SDK designed for controlling complex, autonomous laboratory workflows. It provides a unified interface to manage diverse equipment, including liquid handlers (Hamilton, Opentrons, Tecan), plate readers, pumps, incubators, and centrifuges. Ideal for creating repeatable protocols, simulating experiments, and managing laboratory resources across multi-vendor setups.

scVelo is a leading Python package for analyzing RNA velocity in single-cell RNA-seq data. It infers cell state transitions and developmental trajectories by modeling the kinetics of mRNA splicing (unspliced/spliced ratios). This allows researchers to determine cell fate decisions, predict differentiation paths, and identify driver genes without requiring time-course experiments. It complements tools like Scanpy for comprehensive analysis.

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.