Skill UI

Applies empirically grounded cognitive science frameworks to help CS and AI researchers generate genuinely novel directions through bisociation, analogical reasoning, constraint shifts, and problem reformulation workflows.

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.

This comprehensive pipeline guides users from a vague research concept to a fully structured, testable academic proposal. It first refines the core methodology (research-refine) to establish a stable thesis, and then generates a detailed, claim-driven experiment roadmap (experiment-plan). This end-to-end workflow ensures that the final academic output is both elegant and empirically validated.

Drafts patent claims defining legal protection scope for inventions. Processes invention disclosures and prior art to generate jurisdiction-compliant independent and dependent claims. Ensures proper structure, antecedent basis, and avoids empirical content, serving as the core step in the patent pipeline.

A comprehensive guide for generating highly detailed and structured embodiment descriptions for patent specifications. It outlines the necessary workflow for describing how an invention is made and used (method, system, apparatus), ensuring the patent meets enablement requirements while strictly differentiating technical descriptions from empirical experimental results.

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

This tool guides authors to assess whether their management or organizational research manuscript is suitable for the Academy of Management Review (AMR). It focuses on the journal's pure-theory nature, providing detailed checks on conceptual novelty, logical rigor, structural requirements, and identifying potential desk-reject risks for non-empirical, theory-building papers.

A comprehensive guide for authors targeting top Human-Computer Interaction (HCI) conferences like CHI. It helps authors position their manuscripts, define required contribution types (e.g., system, dataset, empirical finding), and structure the paper to meet the rigorous standards of human-centered computing reviewers, focusing on user experience and design implication.

A comprehensive guide for authors assessing the fit of their computer science manuscript for the ACM CSCW conference. It provides expert advice on positioning, required evidence (empirical depth, design implications), scope vocabulary (collaboration systems, social platforms), and structuring the paper to maximize acceptance chances in social computing venues.

A comprehensive guide for authors targeting the ACM International Conference on the Foundations of Software Engineering (FSE). It helps assess manuscript fit, refine the scientific narrative, strengthen empirical evidence, and understand venue-specific requirements for SE foundations, tools, and AI applications. Use this to strategically position your research for top-tier SE conferences.