matheus-rech

Detects code smells, anti-patterns, and readability issues. Use when implementing features, reviewing code, or refactoring.

Guides PRD, ADR, Design Doc, and Work Plan creation with templates and decision matrix.

Defines frontend environment variables, component design, and data flow patterns. Use when configuring React environment.

Applies React/TypeScript type safety, component design, and state management rules. Use when implementing React components.

Designs tests with React Testing Library and MSW. Applies component testing patterns.

Designs integration and E2E tests with mock boundaries and behavior verification rules. Use when writing E2E or integration tests.

Provides project-specific tech stack, architecture, and implementation rules. Use when checking project structure.

Coordinates subagent task distribution and collaboration. Controls scale determination and autonomous execution mode.

Teach Bayesian approaches to meta-analysis including prior specification, MCMC methods, and interpretation of posterior distributions. Use when users want to incorporate prior knowledge, need probabilistic interpretations, or are working with sparse data.

Extract and prepare study data for meta-analysis including effect size calculation, variance estimation, and handling missing data. Use when users need to convert reported statistics into analyzable format or calculate effect sizes from raw data.

Teach meta-analysis of diagnostic test accuracy studies including sensitivity, specificity, SROC curves, and bivariate models. Use when users need to synthesize diagnostic accuracy data, understand SROC curves, or assess quality with QUADAS-2.

Generate and interpret forest plots for meta-analysis visualization using R and the metafor package. Use when users need to create forest plots, understand visual representation of pooled effects, or interpret study weights and confidence intervals.

Apply the GRADE framework to assess certainty of evidence in systematic reviews. Use when users need to rate evidence quality, create Summary of Findings tables, or understand the factors that affect confidence in effect estimates.

Assess and interpret between-study heterogeneity in meta-analysis using I², Q statistic, tau², and prediction intervals. Use when users need to evaluate consistency across studies, understand sources of variation, or decide if pooling is appropriate.

Teach Individual Patient Data (IPD) meta-analysis methods for analyzing raw participant-level data from multiple studies. Use when users have access to original datasets, need to explore treatment-effect modifiers, or want to conduct time-to-event analyses.

Teach the foundational concepts of meta-analysis including effect sizes, statistical models, and evidence synthesis. Use when users ask about meta-analysis basics, want to understand pooled effects, or need guidance on fixed vs random effects models.

Use when extracting structured data from medical research PDFs, parsing study characteristics, patient demographics, outcomes, and results. Invoke for systematic review data collection from papers.

Use when writing systematic review manuscript sections following PRISMA 2020 guidelines. Covers abstract, introduction, methods, results, and discussion drafting for medical journals. Invoke for academic writing assistance.

Use when performing meta-analysis, pooling study data, generating forest plots, funnel plots, assessing heterogeneity, or conducting subgroup and sensitivity analyses. Invoke for any statistical synthesis of multiple studies.

Use when searching for neurosurgery literature, developing PubMed search strategies, identifying MeSH terms, or building systematic search queries. Invoke for literature searching in neurosurgery topics.

Use when assessing risk of bias or study quality. Covers RoB 2 for RCTs, Newcastle-Ottawa Scale for cohorts, ROBINS-I for non-randomized interventions, and QUADAS-2 for diagnostic studies. Invoke for quality assessment.

Extract structured data from neurosurgical studies

PRISMA-compliant manuscript drafting for systematic reviews

Complete meta-analysis generation for neurosurgical outcomes

Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships

Launch the interactive web dashboard to visualize a codebase's knowledge graph

Use when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks

Use when you need to ask questions about a codebase or understand code using a knowledge graph

Use when you need a deep-dive explanation of a specific file, function, or module in the codebase

Use when you need to generate an onboarding guide for new team members joining a project

Build Shiny web applications in R. Use when creating or editing Shiny apps, modules, reactive logic, bslib layouts, dashboards, or LLM-powered chat apps.