Out-of-band codebase exploration using a cheap/fast model. Keeps your context clean while a cheap model runs 30-80 commands in parallel. Tunable precision and output size - works for broad...
Use when reviewing contracts, extracting key terms, identifying risks, or building contract analysis tools - covers NLP approaches, clause identification, and risk scoringUse when ", " mentioned.
Analyzes TypeScript applications to discover patterns and propose coding conventions for shirokuma-docs lint rules. Use when "γ«γΌγ«ηΊθ¦", "rule discovery", "θ¦η΄ζζ‘", "convention proposal", "γγΏγΌγ³εζ", or...
Analyze gaps between implementation plans and actual codebase implementation for the Rust self-learning memory project
This skill guides adding new MCP tools to the codebase-analyzer server. Use when extending the analyzer with new capabilities like new analysis types, query tools, or integrations.
Comprehensive statistical analysis for research, experiments, and data science. Covers hypothesis testing, effect sizes, confidence intervals, Bayesian methods, regression, and advanced...
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Keeping codebases healthy, performant, and maintainable - refactoring, performance optimization, and technical debt managementUse when "refactor, optimize, performance, technical debt, cleanup,...
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti
You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti
Master data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib
Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Implement analytics, data analysis, and visualization best practices using Python, Jupyter, and modern data tools.
O(log n) sublinear coverage gap detection with risk-weighted analysis and intelligent test prioritization.
Discover patterns, distributions, and relationships in data through visualization, summary statistics, and hypothesis generation for exploratory data analysis, data profiling, and initial insights
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Use when starting technical work requiring structured approach - writing tests before code (TDD), planning data exploration (EDA), designing statistical analysis, clarifying modeling objectives...
Conduct comprehensive sentiment analysis of Reddit discussions for any product, brand, company, or topic. Analyzes what people like, dislike, and wish were different with structured output summaries.
Conduct systematic root cause analysis to identify underlying problems. Use structured methodologies to prevent recurring issues and drive improvements.