Skill UI

This skill provides deep, decision-grade research dossiers on any specific entity (company, person, or organization). Unlike generic profiles, it mandates the user to state a clear hypothesis upfront. The resulting dossier rigorously tests this hypothesis, providing a conclusive verdict, detailed activity timelines, network signals, red flags, and a source-provenance audit log. Ideal for due diligence, investment analysis, or high-stakes meeting preparation.

This profile acts as a narrative and synthesis partner, focusing on articulating the 'why' behind decisions rather than just the 'what.' It specializes in creating clear, structured written artifacts, refining design notes, and synthesizing complex tradeoffs. Wiggins helps ensure that the intent aligns with the execution, adjusting the explanation level and framing to suit any mixed audience, from engineers needing mechanisms to leaders needing implications.

This powerful orchestrator guides the planning, funding, scoping, and synthesis of enterprise research across four critical domains: clinical R&D, finance, market analysis, and product usability. It acts as a central hub, routing complex inquiries to specialized sub-skills and generating structured digests with actionable insights and critical 'grill challenges' for operational rigor.

This meta-skill orchestrates a comprehensive workflow for analyzing PDF documents. It handles complex tasks such as summarizing, cross-document comparison, and targeted Q&A. It ensures findings are traceable by indexing evidence from various sources (full PDFs, pasted excerpts, etc.) and synthesizing structured insights across multiple files.

A generic entry point for delegating self-contained, multi-step tasks to specialized LLM agents (Codex, Claude Code, Pi). Utilizes background processing tools to manage the full lifecycle of AI-driven sub-tasks, such as policy review, complex report synthesis, document generation, or large-scale code refactoring. Ideal for meta-skill workflows requiring full agent agency.

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.

This skill facilitates structured, bidirectional reading and knowledge synthesis for academic texts, theories, and articles. It forces users to first capture the source material's original intent, before critically mapping its relationships, conflicts, and absorptions against the CrossFrame framework. It ensures structured output with clear source boundaries, making it ideal for deep learning and scholarly work.

A comprehensive framework for structuring user interviews, customer discovery, or Jobs-to-be-Done (JTBD) research. This guide provides structured steps for screener creation, deep behavioral exploration (focusing on past actions, not future predictions), and systematic synthesis/pattern detection, ensuring the gathering of genuine, actionable insights.

This skill synthesizes user signals gathered from diverse sources—such as interviews, support tickets, NPS verbatims, app reviews, and sales calls—into a single, weighted, and comprehensive insight brief. It moves beyond surface-level feature requests to surface the core underlying user needs, identifies contradictions (divergence), and assigns confidence ratings based on source diversity and weight. Ideal for product managers and UX researchers needing to reconcile complex, mixed data sets.

This skill synthesizes raw user interview transcripts into structured research findings. It identifies core themes, pain points, workflow insights, and actionable product implications. Use this when analyzing qualitative research notes to derive evidence-based product recommendations and next steps for development teams.

This skill analyzes raw user research data—including transcripts, survey results, and feedback—and synthesizes it into a structured, actionable report. It identifies key themes, quantifies prevalence, pinpoints critical pain points, prioritizes feature requests, and provides strategic, evidence-backed recommendations for product improvement and development.

Generates a comprehensive and actionable UX research plan for any product feature or question. This skill covers the entire research lifecycle, including defining objectives, selecting methodology (e.g., usability testing, interviews), creating participant screeners, writing discussion guides, and structuring a synthesis framework. Ideal for product managers and UX researchers needing a structured approach to validate assumptions and drive design decisions.