Skill UI

Provides red team workflows combining subdomain enumeration, live host discovery, technology fingerprinting, XSS hunting, API fuzzing, and automated recon scripting to support bug bounty hunters and security researchers.

Denario orchestrates a multi-agent AI to automate research workflows, covering idea generation, methodology development, computational experiments, and LaTeX publication output.

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

This skill runs the ScholarEval framework to score academic submissions across problem setting, methodology, data, analysis, writing, and references, providing structured strengths/weaknesses, numeric ratings, and guidance for visualizing the evaluation.

Assess research claims and experimental rigor by evaluating methodology, statistical validity, evidence quality, and biases using GRADE and Cochrane ROB frameworks, plus supporting schematics for complex concepts.

A rigorous, security-focused code review process designed for Pull Requests (PRs), commits, and diffs. It moves beyond general reviews by focusing intensely on high-risk areas such as authentication, cryptography, external calls, and value transfer. The methodology is evidence-based, requiring detailed attack scenario modeling and generating comprehensive, auditable markdown reports to identify overlooked vulnerabilities and regressions.

This guide provides a deep, investigation-driven methodology for reviewing Django and Django REST Framework (DRF) codebases. It helps identify critical authorization gaps, focusing on Insecure Direct Object References (IDOR) and object-level access failures. Learn how to trace data flows, analyze custom managers, and verify that users can only access resources they are explicitly authorized to view or modify.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.

This skill provides a systematic methodology for iteratively improving complex skill documentation. By utilizing a reviewer agent, it identifies and categorizes issues (Critical, Major, Minor) in a structured loop. The process ensures that the skill meets high standards of quality, reliability, and functional completeness before deployment, moving beyond simple manual edits.

This methodology provides a structured framework for stress-testing critical business assumptions before they fail in the market. Learn to systematically identify, challenge, and model the downside risk associated with market size, revenue projections, competitive moats, and customer behavior. It moves beyond optimism to achieve crucial business calibration.

This framework provides a systematic methodology for engineering virality, moving beyond luck to design shareable products, ideas, and content. It utilizes the STEPPS model (Social Currency, Triggers, Emotion, Public, Practical Value, Stories) to systematically increase the likelihood of organic sharing and word-of-mouth marketing. Use it when optimizing for shareability or predicting user adoption patterns.