Skill UI

This skill implements a sophisticated risk-based prioritization engine by combining three critical data sources: the CISA Known Exploited Vulnerabilities (KEV) catalog, the Exploit Prediction Scoring System (EPSS), and traditional CVSS scores. It allows security teams to move beyond simple severity ratings, focusing remediation efforts on vulnerabilities confirmed to be actively exploited in the wild or predicted to be highly exploitable. The workflow integrates fetching, enriching, and scoring vulnerabilities to generate an actionable remediation plan with defined SLAs.

This tool conducts comprehensive, sector-specific threat landscape assessments by analyzing designated threat actor patterns (e.g., APT groups, cybercriminals), common attack vectors, and industry-specific vulnerabilities. It is designed to provide actionable intelligence for risk management, security investment prioritization, and executive reporting across critical sectors like finance, healthcare, and energy.

This skill provides a comprehensive guide to the Common Vulnerability Scoring System (CVSS v4.0), the industry standard for assessing vulnerability severity. It teaches users how to calculate CVSS scores using Base, Threat, and Environmental metrics, and crucially, how to integrate real-world context like EPSS scores and CISA KEV listings for advanced, actionable risk prioritization in security operations.

This tool implements the Stakeholder-Specific Vulnerability Categorization (SSVC) framework, providing a structured decision-tree methodology for advanced vulnerability prioritization. It moves beyond traditional CVSS scoring by incorporating critical factors such as active exploitation status (CISA KEV), technical impact, automatability, and mission prevalence. It generates actionable remediation priorities (Track, Attend, Act) to guide security teams in managing risk and improving security program maturity.

Summarizes the Test-Driven Development workflow with RED/GREEN/REFACTOR phases, the AAA pattern, prioritization cues, anti-patterns, and AI-augmented agent roles to guide implementation.

A comprehensive workflow designed for Product Managers and technical leads to systematically identify, prioritize, and document missing features in a software project. It guides users through project understanding, gap analysis, applying a scoring matrix for prioritization, developing detailed Minimum Viable Product (MVP) specifications, and structuring the output as actionable development issues (e.g., GitHub Issues) with dependency mapping and workload optimization.

Medchem is a powerful Python library designed for molecular filtering and prioritization in drug discovery. It allows users to apply rigorous, literature-derived drug-likeness rules (e.g., Lipinski, Veber, CNS), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and combine multiple criteria using a custom query language. It is essential for compound library triage and hit-to-lead optimization.

This framework shifts the focus from product features to the fundamental 'job' customers need to get done. It helps teams uncover deep customer needs by analyzing functional, emotional, and social dimensions. Use it for product positioning, feature prioritization, and understanding market-product fit by defining the progress customers seek to make in specific life circumstances.

This domain-agnostic skill ranks impediments, risks, and gaps by using a specialized value-stream scoring model. It evaluates countermeasures based on four criteria: Return on Investment (ROI), Cost to Implement, Ease of Deployment, and Risk Factor. A proprietary formula calculates a final Priority score (1-100), allowing users to quickly determine the optimal sequence for remediation, improvement backlogs, or triage items.

This skill provides a scaffold for building complex, composable recommendation, ranking, and feed pipelines. It implements the robust six-stage pattern (Source→Hydrator→Filter→Scorer→Selector→SideEffect), which is the industry standard for determining "top K items." Use it for social feeds, content CMS ranking, RAG reranking, or any system needing complex candidate prioritization.

This comprehensive skill applies industry-standard prioritization frameworks, including RICE, MoSCoW, Kano, and ICE, to rank product features and backlog items. It generates a scored, defensible list that includes a recommended build order, explicitly deprioritized items, and documented assumptions. Essential for Product Managers and teams needing to make data-driven, strategic decisions about the product roadmap and manage scope trade-offs.

This skill generates a sophisticated prioritization matrix by combining quantitative RICE (Reach, Impact, Confidence, Effort) scoring with qualitative Strategic Alignment ratings. Use this when multiple features compete for resources, requiring a balanced decision between sheer user impact and adherence to core business OKRs. The output provides a scored priority matrix, quadrant placement (Now, Next, Later, Drop), and explicit sequencing recommendations.