Data Science

Intelligent skill router that analyzes user requests and automatically dispatches to the most appropriate skill(s) or zen-mcp tools. Routes to zen-chat for Q&A, zen-thinkdeep for deep problem investigation, codex-code-reviewer for code quality, simple-gemini for standard docs/tests, deep-gemini for deep analysis, or plan-down for planning. Use this skill proactively to interpret all user requests and determine the optimal execution path.

Deep technical documentation generation workflow using zen mcp's clink and docgen tools. First uses clink to launch gemini CLI in WSL for code analysis, then uses docgen for structured document generation with complexity analysis. Specializes in documents requiring deep understanding of code logic, model architecture, or performance bottleneck analysis. Use when user requests "use gemini for deep analysis", "generate architecture analysis document", "analyze performance bottlenecks", "deeply understand code logic", or similar deep analysis tasks. Default output is .md format.

Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis

Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

This skill analyzes code for design quality improvements across 8 dimensions: Naming, Object Calisthenics, Coupling & Cohesion, Immutability, Domain Integrity, Type System, Simplicity, and Performance. Ensures rigorous, evidence-based analysis by: (1) Understanding code flow first via implementation-analysis protocol, (2) Systematically evaluating each dimension with specific criteria, (3) Providing actionable findings with file:line references. Triggers when users request: code analysis, design review, refactoring opportunities, code quality assessment, architecture evaluation.

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.

Create, review, and refactor project documentation (README, AGENTS.md, architecture docs, runbooks, API docs) with deep codebase analysis, clear markdown structure, and diagrams/user flows. Use when asked to write or improve docs, audit existing documentation for accuracy or quality, generate diagrams/flows, or assess agent-first docs like AGENTS.md/PLANS.md for freshness and completeness.

This skill should be used when the user asks to "analyze my product", "research competitors", "find feature gaps", "create feature request", "prioritize backlog", "generate PRD", "plan roadmap", "what should we build next", "competitive analysis", "gap analysis", "sync issues", or mentions product management workflows. Provides AI-native PM capabilities for startups with signal-based feature tracking, the WINNING prioritization filter, and GitHub Issues integration with deduplication.

Evaluate and upgrade test suites with TDD/BDD rigor, coverage tracking,and quality assessment.Triggers: test audit, test coverage, test quality, TDD, BDD, test gaps,test improvement, coverage analysis, test remediationUse when: auditing test suites, analyzing coverage gaps, improving testquality, evaluating TDD/BDD complianceDO NOT use when: writing new tests - use parseltongue:python-testing.DO NOT use when: updating existing tests - use sanctum:test-updates.Use this skill for test suite evaluation and quality assessment.

Consolidates ephemeral LLM-generated markdown files into permanent documentation.Triggers: consolidate docs, untracked reports, ephemeral files, git cleanup,report consolidation, knowledge extraction, REPORT.md files, ANALYSIS.md filesUse when: you have untracked *_REPORT.md or *_ANALYSIS.md files, git statusshows markdown artifacts that shouldn't be committed, preparing PR and needto clean up working artifacts, preserving insights from code reviewsDO NOT use when: files are already in docs/ or skills/ locations.DO NOT use when: files are intentionally temporary scratch notes.DO NOT use when: source files have no extractable value.Merges valuable content into permanent documentation, then deletes source files.

Evaluate codebase architecture against ADRs, coupling rules, and team guardrails.Triggers: architecture review, ADR audit, coupling analysis, design review,principle checks, Law of Demeter, architecture assessmentUse when: reviewing architecture decisions, auditing ADR compliance, analyzingcoupling, validating design principlesDO NOT use when: selecting architecture paradigms - use archetypes skills.DO NOT use when: API surface review - use api-review.Use this skill for architecture assessment and compliance.

Expert-level Rust audits covering ownership, concurrency, unsafe blocks,traits, and Cargo dependencies.Triggers: Rust review, ownership analysis, borrowing, unsafe audit, concurrency,Cargo dependencies, lifetime annotations, trait boundsUse when: reviewing Rust code, auditing unsafe blocks, analyzing ownership patterns,scanning Cargo dependencies for securityDO NOT use when: general code review without Rust - use unified-review.DO NOT use when: performance profiling - use parseltongue:python-performance pattern.Use this skill for Rust-specific code audits.

Workflow for capturing evidence and citations to create reproducible analysesand audit trails.Triggers: evidence capture, citations, reproducible analysis, audit trail,documentation, evidence logging, findings documentationUse when: conducting any review that needs evidence trails, creating auditdocumentation, ensuring reproducibility of analysesDO NOT use when: quick informal checks without documentation needs.DO NOT use when: structured output is the focus - use structured-output.Use this skill as foundation for all evidence-based review workflows.

Evaluate and improve Claude skill quality through auditing.Triggers: skill audit, quality review, compliance check, improvement suggestions,token usage analysis, skill evaluation, skill assessment, skill optimization,skill standards, skill metrics, skill performanceUse when: reviewing skill quality, preparing skills for production, auditingexisting skills, generating improvement recommendations, checking compliancewith standards, analyzing token efficiency, benchmarking skill performanceDO NOT use when: creating new skills from scratch - use modular-skills instead.DO NOT use when: writing prose for humans - use writing-clearly-and-concisely.DO NOT use when: need architectural design patterns - use modular-skills.Use this skill BEFORE shipping any skill to production. Check even if unsure.

Analyze and enhance Makefiles for complete user functionality coverage.Triggers: Makefile analysis, Makefile gaps, missing targets, plugin release,Makefile coverage, build targets, make dogfood, plugin qualityUse when: analyzing Makefile completeness before releasing plugins, identifyinggaps during plugin maintenance, scoring Makefiles against best practices,verifying Makefiles support standard developer workflowsDO NOT use when: writing initial Makefiles from scratch.DO NOT use when: debugging specific build target failures.DO NOT use when: creating custom non-standard build systems.Use this skill BEFORE releasing any plugin to verify Makefile coverage.

Expert-level Home Assistant configuration management with efficient deployment workflows (git and rapid scp iteration), remote CLI access via SSH and hass-cli, automation verification protocols, log analysis, reload vs restart optimization, and comprehensive Lovelace dashboard management for tablet-optimized UIs. Includes template patterns, card types, debugging strategies, and real-world examples.

Apply systematic four-phase debugging methodology (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures thorough understanding before attempting solutions, preventing random fixes and reducing debugging time from hours to minutes. Use this skill when encountering any test failures in test suites (Jest, pytest, RSpec, JUnit, Go testing), when production bugs are reported or discovered, when code produces unexpected output or behavior different from requirements, when experiencing build failures or compilation errors, when integration tests fail due to component interaction issues, when performance problems or slowdowns are detected, when encountering race conditions, timing issues, or intermittent failures, when error messages or stack traces appear in logs or console output, when refactoring causes existing tests to fail, when you've already attempted one or more fixes that didn't resolve the issue, when deployment or CI/CD pipelines fail, when you're tempted to make a

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Generate safe, read-only PostgreSQL queries from natural language. Use when users need to query blog_small, ecommerce_medium, or saas_crm_large databases. Supports query generation, execution, and result analysis with confidence scoring.