mermaid-diagrams
Expert guidance on creating accurate, visually polished Mermaid diagrams for architecture documentation.
Expert guidance on creating accurate, visually polished Mermaid diagrams for architecture documentation.
Data analysis, visualization, statistical modeling, and reproducible research workflows.
Comprehensive data visualization skill covering visual execution and technical implementation. Includes perceptual foundations, chart selection, layout algorithms, and library guidance. Load on-demand when building charts, graphs, dashboards, or any visual data representation.
Marketing strategy and copywriting guidance for NestMind, a privacy-first iOS knowledge management app. Use when creating website copy, landing pages, App Store descriptions, marketing materials, social media content, or any promotional content for NestMind. Covers positioning, messaging hierarchy, differentiation, and conversion optimization.
Advanced recursive reasoning methodology for systematic problem-solving. Use when you need to explore multiple solution approaches in parallel, evaluate them rigorously, and recursively deepen the best path. Ideal for complex decisions with trade-offs, optimization problems, or strategic planning. Example: "Should we use microservices or monolith?" → Apply ToT to spawn 5+ architectural approaches, evaluate each systematically, recurse on the winner.
Generates professional markdown comparison report with tables, executive summary, and verdict by use case. Use when user asks to 'generate report', 'create comparison report', 'synthesize comparison', 'write comparison', or when orchestrator has completed all data collection. Creates structured report with specs tables, pros/cons, pricing analysis, and actionable recommendations.
Use when planning refactoring sprints, prioritizing technical debt backlog, justifying refactoring investment to executives, or creating data-driven roadmaps - calculates return on investment using effort-impact matrices and research-backed formulas
Expert guidance on machine learning and feature engineering for fantasy football player projection models. Use this skill when building predictive models, engineering features from player statistics, selecting appropriate ML algorithms, or addressing sports-specific ML challenges. Covers feature engineering patterns, model selection frameworks, validation strategies, and interpretability techniques for fantasy football analytics.
Automated lessons learned system for NinjaTrader trading. Use when implementing new features, fixing bugs, or reviewing code. Ensures past mistakes are never repeated by making historical lessons mandatory checkpoints.
Comprehensive AI ethics and responsible AI development specialist. Use PROACTIVELY for bias assessment, fairness evaluation, ethical AI implementation, community impact analysis, and regulatory compliance. Trigger keywords include bias, fairness, discrimination, disparate impact, ethical AI, responsible AI, AI safety, alignment, algorithmic justice, AI regulation, model audit, AI governance. Use for high-risk AI systems (employment, lending, healthcare, criminal justice, education), systems affecting vulnerable populations, large-scale deployments (more than 10,000 people), automated decision-making, facial recognition, biometric systems, and predictive analytics on people.
Generate McKinsey-style board presentation PPTs from weekly auto insurance data. Automatically calculates 16+ KPIs, creates executive-level slides with actionable insights, and supports week-over-week comparisons. Use when user uploads insurance cost data (Excel/CSV) and requests board report, weekly presentation, executive briefing, or mentions keywords like 董事会汇报, 周报PPT, 经营分析演示, McKinsey-style reports.
Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, A/B testing funnel performance, or optimizing user journeys.
Time-based cohort analysis with retention and behavior tracking. Use when analyzing user retention over time, comparing cohort performance, identifying lifecycle patterns, or measuring feature adoption by cohort.
This skill should be used when the user asks to "calculate TAM", "determine SAM", "estimate SOM", "size the market", "calculate market opportunity", "what's the total addressable market", or requests market sizing analysis for a startup or business opportunity.
Use AI to reshape strategic decisions, not just execution. Identify where AI creates competitive advantage, shifts constraints, and enables new business models.
Data scientist specializing in startup analytics, user behavior tracking, and metrics analysis for Lean Startup and Customer Development methodologies. Use when analyzing user data, setting up analytics, measuring validation metrics, cohort analysis, or when user asks about tracking, metrics, data analysis, or measuring startup hypotheses.
Analyzes and explains Actoris trust scores, their components, and how to optimize them. Use when you need to understand trust mechanics, interpret trust scores, or provide recommendations for improving agent trustworthiness.
Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.
Synthesize research, data, and insights to create a rich, actionable buyer persona with demographics, psychographics, jobs-to-be-done, pain points, motivations, buying journey, and channel preferences. Use when the user needs to understand their target customer deeply or create a customer persona for marketing strategy.
Comprehensive data visualization skill covering visual execution and technical implementation. Includes perceptual foundations, chart selection, layout algorithms, and library guidance. Load on-demand when building charts, graphs, dashboards, or any visual data representation.
Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.
Runs Exploratory Data Analysis (EDA) following the mandatory validation workflow. Use when performing data analysis, exploring datasets, validating data quality, or when the user mentions EDA, data exploration, sanity checks, or data validation. Always run before main analysis queries.
Build mathematically correct, visually prominent data visualizations for time-series charts. Use this skill when creating charts with mathematical overlays (trendlines, patterns, indicators), fixing visual artifacts (wavy lines, domain mismatches), or validating chart correctness. Focuses on technical correctness and progressive validation, not aesthetic design.