Skill UI

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

Real-time research lookup that auto-selects between Parallel Chat API and Perplexity sonar-pro-search to gather papers, facts, market data, and cited findings.

Guides research teams through a two-stage manuscript workflow: outline sections with research-lookup, enforce IMRAD structure, citation styles, figures, and reporting guidelines, then convert into polished prose for journals.

Provides access to USPTO PatentSearch, PEDS, TSDR, and related APIs for patent and trademark searches, examination history, assignments, citations, and litigation data, with helper scripts for Python-based IP research and portfolio insights.

A robust Python client wrapper for interacting with the Zotero Web API v3. This skill allows users to programmatically manage academic references, including retrieving, creating, updating, and deleting items, collections, tags, and attachments. Ideal for academic researchers building automated workflows, bulk exporting citations (e.g., BibTeX), or integrating Zotero data into larger research applications.

Parallel Web Research skill orchestrates Parallel Chat and Extract APIs to search the internet, perform deep intelligence work, and craft cited summaries, with model controls, JSON outputs, and citation verification for market, news, technical, and general queries.

This command guides the entire lifecycle of major feature development, providing a structured, multi-phase workflow. It helps users move a feature from initial concept (research) through planning, implementation, and progress tracking. Use it when you need to coordinate status, next steps, and phased delivery for complex features.

A systematic skill for identifying similar vulnerabilities and bugs across large, complex codebases. It guides the user through generalizing initial bug patterns using techniques like CodeQL and Semgrep, ensuring comprehensive security audits beyond the initially discovered flaw. Ideal for advanced security research and robust code auditing.

A comprehensive technical module covering foundational deep learning principles and cutting-edge computer vision architectures, drawing from Yann LeCun's research. Topics include CNN fundamentals, backpropagation theory, and advanced self-supervised learning (SSL) models such as SimCLR, MAE, and the modern Joint Embedding Predictive Architecture (JEPA). Provides detailed theoretical explanations and complete PyTorch code for implementation.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

Allows users to resume an interrupted or paused autoresearch experiment. It automatically loads the full context, including configuration, strategy files, complete results history, and recent code changes. The system then summarizes the current best metrics and patterns, prompting the user to continue with a single iteration, set up an automated loop, or manually review the results.

This skill initializes a new autoresearch experiment. It guides the user through defining all necessary parameters, including the domain, target files, evaluation commands, metrics, and optimization direction. It is the essential starting point for automated optimization and performance benchmarking cycles.