Skill UI

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.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.

A specialized module containing Yann LeCun's comprehensive arguments on the limitations of Large Language Models (LLMs). It critically examines the concept of 'emergent reasoning,' contrasts LLMs' statistical prediction with genuine world models, and addresses deep technical rivalries (e.g., LeCun vs. Hinton). Ideal for advanced discussions on AI causality, common sense, and AGI feasibility.

This skill covers using the SHAP library to explain and audit machine learning predictions. It guides users on selecting explainers, computing feature attributions, and validating results. It includes workflows for local and global visualizations, handling multi-output models, and ensuring model behavior descriptions are accurate without implying causality. Requires Python 3.12+ and specific SHAP versions.

Transforms a simple, prioritized list of product initiatives into a compelling, strategic narrative suitable for executive consumption. This tool helps structure the product roadmap by identifying core strategic themes, defining the causal progression across quarters, and drafting an executive summary that speaks to business outcomes rather than technical features. It also includes a critical 'What's Not on the Roadmap' section to demonstrate strategic discipline.

Query and manage agent knowledge through the controller bridge. Supports semantic routing, hierarchical recall, causal graphs, and context synthesis. Use MCP tools to store, retrieve, search patterns, and build knowledge graphs for AI agents.

This tool performs pathfinder traversal across a knowledge graph starting from a specified seed entity. It expands outward through causal edges, calculates path relevance scores, and prunes low-similarity branches. It is ideal for conducting impact analyses, tracing dependency chains, or understanding complex relationships within a structured knowledge base.

This advanced skill provides a rigorous framework for assessing whether an organizational or management research manuscript is suitable for highly selective, theory-driven journals like the Academy of Management Journal (AMJ). It guides authors on necessary theoretical framing, methodological rigor (e.g., endogeneity, causal inference), structural improvements, and common pitfalls to avoid, significantly improving submission quality and maximizing acceptance chances.

This guide provides comprehensive econometric methodologies for establishing causality in accounting and finance research. It covers advanced techniques such as Difference-in-Differences (DID), Propensity Score Matching (PSM), Heckman selection models, and Instrumental Variables (IV), emphasizing the correct handling of regulatory changes and external shocks to meet top-tier academic standards.

A comprehensive guide for structuring high-impact scientific papers in advanced materials. It teaches authors how to build a cohesive narrative around a single central scientific advance, emphasizing the crucial causal chain: Structure → Property → Function. It also provides rules for optimizing the opening paragraph to serve as a self-contained abstract.

This guide helps researchers determine if their applied microeconomics work meets the rigorous standards of the American Economic Journal: Applied Economics (AEJ: Applied). It serves as a pre-submission checklist, detailing the required depth of causal identification (e.g., RCTs, DiD, IV), methodological robustness, expected manuscript structure, and avoiding common desk-reject pitfalls. Essential for high-quality academic writing.

This skill guides authors through the rigorous requirements of American Economic Journal: Economic Policy. It helps evaluate whether an applied economics manuscript meets the journal's high bar, focusing specifically on the joint criteria of sound causal identification (DiD, IV, RDD) and clear, impactful policy relevance. Use it to re-frame arguments and check against best practices for policy evaluation.