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Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affect

PC and console game development principles. Engine selection, platform features, optimization strategies.

Bulk RNA-seq DE with R/Bioconductor DESeq2. Negative binomial GLM, empirical Bayes shrinkage, Wald/LRT tests, multi-factor designs, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap viz. R gold standard. Use pydeseq2-differential-expression for Python; use edgeR for TMM normalization.

Use this skill when working on the Repomix documentation website in `website/` directory, including VitePress configuration, multi-language content, or translation workflows.

Gere vídeos de IA com Google Veo, Seedance, Wan, Grok e mais de 40 outros modelos via CLI inference.sh, oferecendo recursos como texto para vídeo, imagem para vídeo, sincronização labial e animação de avatares para mídias sociais, marketing e demonstrações de produtos.

Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.

Disciplined plan execution for implementation tasks. Use when executing a saved implementation plan, following step-by-step instructions from a plan document.

Set up, migrate, and maintain a project's documentation for maximum AI context efficiency using Claude Code's native mechanisms. Builds a 2-layer system (`.claude/rules/*.md` for the AI + `docs/*.md` for humans) that cuts startup tokens 20-40% vs custom summary patterns. Triggers — "set up docs structure", "optimize docs for AI", "install .claude/rules", "migrate _lite to rules", "audit docs", "sy

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.

Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

Systematic debugging with root cause investigation. Four phases: investigate, analyze, hypothesize, implement. Iron Law: no fixes without root cause. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively invoke this skill (do NOT debug directly) when the user reports errors, 500 errors, stack traces, unexpected behavio

Orchestrate multi-simulation campaigns including parameter sweeps, batch jobs, and result aggregation. Use for running parameter studies, managing simulation batches, tracking job status, combining results from multiple runs, or automating simulation workflows.