advanced-math-tradingsignal-processing-features
Signal processing, filtering/denoising, and math-driven feature engineering.
Signal processing, filtering/denoising, and math-driven feature engineering.
Use when building AI-native products where user data can fine-tune performance, when static software fails to improve with usage, or when designing products that learn from interaction
Master training pipelines - orchestration, distributed training, hyperparameter tuning
Analyze SAE decoder weights - output influence, feature importance, and decoder similarity
Privacy attacks to extract training data and sensitive information from AI models
Data cleaning, preprocessing, and quality assurance techniques
Constitutional compliance validator. Enforces all 5 Constitutional Articles to ensure AI trading decisions comply with human-defined rules. Works as second line of defense alongside Risk Agent.
Empirical validation workflow for blockchain data collection pipelines before production implementation. Use when validating data sources, testing DuckDB integration, building POC collectors, or verifying complete fetch-to-storage pipelines for blockchain data.
Senior business consultant expertise for strategic analysis, frameworks, and executive communication. Use when structuring arguments, applying business frameworks (MECE, pyramid principle, Porter's Five Forces), crafting executive summaries, or developing strategic recommendations.
Generate quarterly sales reports with executive summary, regional breakdown, and trend analysis. Use for formal business reporting.
Expert CRO advisor that analyzes landing pages, product funnels, UI/UX friction, and provides data-driven A/B test ideas to maximize conversions, sign-ups, trials, and retention. Use when optimizing conversion rates, analyzing funnels, designing experiments, improving CTAs, reducing drop-offs, or when user mentions conversion rate, CRO, landing page optimization, A/B testing, or funnel analysis.
Analyzes algorithmic trading backtest results from Jupyter notebooks and generates summary reports. Use when the user wants to analyze or summarize backtest notebooks.
Analyzes customer behavior, needs, pain points, and sentiment through review mining, social listening, buyer persona development, and jobs-to-be-done framework. Use when the user requests customer analysis, voice of customer research, buyer personas, pain point analysis, or wants to understand customer needs and motivations.
Track and evaluate AI predictions over time to assess accuracy. Use when reviewing past predictions to determine if they came true, failed, or remain uncertain.
Analyze completed ECIR (Engineering Change Impact Report) Excel files to extract insights, identify trends, and detect patterns across single or multiple reports. Use when the user asks to analyze ECIR reports, find trends in ECIRs, compare multiple ECIRs, identify cost variance patterns, or generate insights from completed ECIR Excel files. This skill works with the output files from the ECIR Advanced Tool.
Analyzes financial statements, calculates key metrics, and generates investment reports. Use when reviewing earnings, building DCF models, or comparing company financials.
Comprehensive guidance for interpreting backtest results and detecting overfitting (project)
Use when developer is leaving or new hire onboarding, assessing team resilience, planning for developer departures, calculating bus/truck factor, identifying knowledge silos, or evaluating organizational risk - identifies knowledge gaps and transition risks
Categorize financial transactions by analyzing merchant names, amounts, and context. Assigns spending categories with confidence scores and suggests pattern rules. Use when you need to categorize transactions or determine spending patterns.
Performs sequential multi-round oracle analysis where each round builds on previous findings. Use when user specifies analysis count (e.g., "analyze 5 times"), asks to think repeatedly, or needs progressive refinement.
Expert in statistical analysis for blind A/B and ABX audio testing. Validates randomization, calculates statistical significance, and ensures proper experimental design. Use when implementing A/B test features or analyzing test results.
Data analysis workflows and patterns for exploring, transforming, and visualizing data. Use when working with data, creating reports, or when users mention "data analysis", "analyze data", "data exploration", or "reporting".
Complete market analysis toolkit using McKinsey consulting frameworks. Perform competitive research, market sizing, trend analysis, and strategic positioning. Generates actionable insights for product decisions.