Skill UI

This skill guides users in transforming chaotic debugging efforts into a structured, systematic process. It provides a methodology for tracking down elusive bugs, investigating complex performance degradation, and resolving critical production incidents. Users learn to capture necessary logs, formulate testable hypotheses, narrow the scope using techniques like binary search, and methodically verify fixes in distributed systems environments.

Guides developers through reproducing issues, isolating failure cases, testing hypotheses, and verifying fixes with regression safeguards, while referencing tools, logs, stack traces, and git bisect workflows to expose stubborn root causes.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.

Streamlines citation discovery via Google Scholar and PubMed, extracts complete metadata, validates references, and generates formatted BibTeX for accurate academic manuscripts or theses.

Conversational research ideation assistant that helps scientists generate novel hypotheses, explore interdisciplinary analogies, challenge assumptions, and outline experiment pathways during early-stage planning.

Guides engineers through a disciplined four-phase debugging workflow so they investigate root causes, analyze patterns, test hypotheses, and implement fixes based on evidence before touching code.

Guides product teams to convert major initiatives into structured hypotheses that spell out the action, beneficiary, outcome, and validation steps so uncertainties surface before roadmaps or delivery planning.

Helps product managers match hypotheses, risks, and resources to the right Proof of Life probe so they can pick the cheapest prototype that reveals the harsh truth before overbuilding.

Use the Recommendation Canvas to vet AI product concepts by mapping business and customer outcomes, hypotheses, positioning, risks, and success metrics so you can make defensible investment recommendations.

An expert guide for planning, designing, and implementing statistically rigorous A/B tests and product experiments. It covers core principles like formulating strong hypotheses, selecting primary/guardrail metrics, calculating sample sizes, and analyzing results to drive actionable business insights.

Structured workflow turns papers, articles, and reports into actionable briefs that capture theses, findings, methodology notes, and formatted citations for product teams and analysts needing quick research clarity.

Hunt for adversaries creating malicious email forwarding rules to persist in environments, using SIEM/EDR telemetry to validate hypotheses, analyze anomalies, and document response guidance.