Skill UI

This comprehensive tool scans all project dependencies against databases of known CVEs (Common Vulnerabilities and Exposures) and security vulnerabilities. It is essential for conducting thorough security audits of third-party packages, ensuring code quality before major releases, or responding to specific security advisories. It provides actionable reports categorized by severity.

A comprehensive, multi-phase checklist for ensuring the quality, security, and deployment readiness of Django applications. It covers linting (ruff, mypy), unit testing (pytest), database migrations, security audits (bandit, pip-audit), and performance checks, making it an essential quality gate for CI/CD pipelines before merging or deploying to production.

This skill provides comprehensive, read-only market intelligence for prediction markets. It helps users research market events, compare different venues (like Polymarket, Kalshi), analyze liquidity, and understand underlying assets. It generates source-grounded briefings, emphasizing context and data quality, and strictly avoids providing investment or trading advice.

This skill provides a structured framework for researching prediction markets when they are used as data sources, forecasting inputs, or oracle signals for business intelligence. It allows users to perform source-grounded analysis of market-implied probabilities, signal quality (e.g., liquidity, spread, age), and compare these signals against traditional internal KPIs or news sources. Crucially, it strictly separates implied signals from investment advice and trade recommendations.

This skill reviews prediction market, basket, oracle, and trading-agent workflows to ensure compliance, safety, data quality, and privacy. It must be run before any workflow handles user portfolio data, venue authentication, API keys, or trade planning to mitigate execution and regulatory risks across multiple domains.

This tool synthesizes multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

A comprehensive tool for importing and validating leads into a CRM. It performs advanced data scrubbing, deduplication, and mandatory compliance checks (GDPR, CCPA). Leads are automatically scored and qualified (MQL/SAL), ensuring that only high-quality, clean data reaches the sales team, while low-scoring leads are automatically routed into nurture sequences. Supports CSV, JSON, and manual sources.

A comprehensive tool for publishing fully optimized blog content to major CMS platforms like WordPress or Webflow. It enforces multiple quality gates, including SEO optimization, brand voice alignment, content scoring, and compliance checks, ensuring drafts are ready for live deployment with scheduling and metadata.

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

This comprehensive guide outlines the critical steps authors must take after their manuscript has been accepted by a journal. It guides users through the transition from academic writing to scientific publishing quality control, covering copyright agreements, mandatory metadata filling (e.g., funding, affiliations), rigorous formatting checks (references, formulas, figures), and the multi-stage proofreading (proofsheet) process to ensure zero-defect publication.

This guide provides comprehensive best practices for preparing supplementary materials for major academic conferences like AISTATS. It details the proper structure and handling of appendices, mathematical proofs, code archives, and additional data tables, ensuring that the main paper remains fully understandable even if the supplementary material is ignored by reviewers. Essential for publishing high-quality, robust research.

Guides authors through the final pre-submission quality check for American Anthropologist manuscripts submitted via Research Exchange. It verifies crucial elements such as anonymity stripping, meeting specific word counts, adhering to Chicago author-date style, securing ethics approvals, and ensuring proper metadata (DOIs, ORCID, alt text) are included. Essential for avoiding common rejection reasons.