Skill UI

A comprehensive guide for academic finance researchers to assess if their quantitative manuscript is suitable for submission to the Journal of Financial and Quantitative Analysis (JFQA). It covers the journal's elite positioning, necessary methodological rigor (e.g., causal identification, standard errors), required structure, and common desk-reject pitfalls, helping authors refine their work for top-tier quantitative finance venues.

This skill generates validated local artifacts (PNG, JPEG, or PDF) from public HTTP(S) web URLs using the Latchshot service. It is ideal for creating visual reports, quality assurance documentation, or social media previews of public web content. Note that this tool only supports public pages and cannot access private, authenticated, or restricted content.

Cirq is a powerful, open-source Python framework developed by Google Quantum AI. It allows users to design, simulate, and execute complex quantum circuits. It supports advanced features including parameterized circuits, noise modeling, circuit transformation, and seamless integration with multiple quantum hardware backends (e.g., IonQ, Google Quantum, Pasqal).

This skill helps developers and QA engineers transform vague, intermittent, or poorly documented bug reports into a minimal, reproducible, and evidence-backed brief. It guides the user to isolate the exact failure conditions, separate expected outcomes from actual errors, and reduce the issue to the smallest safe failing fixture, ensuring diagnosis occurs before premature repair.

This skill replaces detected Protected Health Information (PHI) in clinical notes with realistic, type-matched synthetic surrogates. Unlike simple redaction, surrogates maintain the natural readability and structure of the text, ensuring the data remains parseable for downstream NLP tasks, QA, or human review. It supports consistent, reproducible fake identities and allows custom identifier generation for proprietary formats.

A comprehensive, multi-step framework for conducting systematic QA testing on web applications. This guide covers scope detection (diff-aware), tiered testing depth (Quick, Standard, Exhaustive), detailed issue taxonomy (Functional, Visual, UX, etc.), structured bug documentation, and the critical fix-and-verify loop, ensuring high code quality throughout the development cycle.

Creates or edits Power Apps Canvas Apps via Canvas Authoring MCP. Supports new app generation, targeted edits, multi-screen changes, responsive layout, per-screen self-QA, and compile-error convergence. Triggered by requests to create, modify, or edit Canvas Apps or .pa.yaml files.

Guides full contract lifecycle review from intake through playbook normalization, clause review, deviation scoring, negotiation planning, approvals, QA, and next actions. Covers legal, business, operational, compliance, privacy, security, technology, and AI risks across NDAs, SaaS agreements, DPAs, procurement and commercial contracts.

Runs a multi-persona dev team session where PM, Architect, Developer, and QA each respond to a topic from their own role. Use for feature design, proposal review, or initiative onboarding when you want multi-role perspectives without switching agents.

This skill generates comprehensive test data spreadsheets for compliance teams to validate their sanctions screening systems. It pulls new designations and deliberate variations from major global lists (OFAC, OFSI, and EU). The test verifies both the list's coverage (catching the newest designations) and the system's fuzzy matching tuning (catching realistic name variations, transpositions, and aliases). Ideal for screening QA, regression testing, and ensuring regulatory compliance.

Manages complex, iterative background jobs (Ralph loop) for continuous process evolution. It automatically runs multiple `evolve_step` generations until predefined stopping criteria are met (e.g., QA pass, convergence, or max generations). Ideal for complex AI generation pipelines requiring robust, non-stop processing.

Generate validated Seed specifications from interview results using MCP tools, user interaction, and code inspection capabilities. Supports both interview-based and interview-less workflows with audit trail maintenance and QA score tracking.