Comprehensive pixi package manager skill for all pixi operations from beginner to advanced. Use for initializing projects, managing dependencies, configuring environments, multi-environment setups, workspace composition, system requirements (CUDA/glibc), task workflows, CI/CD integration, or any pixi.toml/pyproject.toml configuration—e.g., "pixi init", "pixi add numpy", "setup multi-environment project", "configure CUDA", "monorepo workspace", "pixi.lock issues", "Docker with pixi", "GitHub Actions pixi".

Byte size parsing and formatting with pybytesize. Use when converting bytes to human-readable sizes, parsing size strings, or doing block-aligned calculations—e.g., "format 1GB as MiB", "parse '500MB'", "human readable file size".

Pytest-first Python testing with emphasis on fakes over mocks. Covers unit, integration, and async tests; fixture design; coverage setup; and debugging test failures. Use when writing tests, reviewing test quality, designing fixtures, setting up pytest, or debugging failures—e.g., "write unit tests for new feature", "fixture design patterns", "fakes vs mocks comparison", "fix failing tests".

Azure DevOps Repos-first template architecture for reusable CI pipelines. Use when designing or debugging Azure Pipelines YAML templates, splitting PR validation from post-merge main workflows while reusing one core CI template, enforcing strict compile-time/runtime variable rules, building typed template APIs (`stepList`, `jobList`, `stageList`, `templateContext`), or creating self-contained dependency-management templates for TypeScript/Python/R using bun, npm, and pixi—e.g., "one CI template for PR and main", "fix expression timing bug", "design jobList template contract".

rlang metaprogramming for tidy evaluation and non-standard evaluation (NSE) in R. Use when building data-masking APIs, wrapping dplyr/ggplot2/tidyr functions with {{ !! !!! operators, implementing quosures and dynamic dots, or designing tidyverse-style DSLs—e.g., "tidy eval wrapper function", "embrace operator usage", "NSE programming patterns", "custom select helpers".

Write production-ready one-off scripts and automation utilities with proper error handling and safety patterns. Use when developing bash automation, Python CLI tools, shell scripts, system administration scripts, or command-line batch processing—e.g., "write a script to process files", "python one-liner for data conversion", "bash automation for backups", "shell script with error handling".

Core R programming skill for all R code, package development, and data science workflows. Use when writing R functions, building packages, using tidyverse (dplyr, ggplot2, purrr), creating Shiny apps, working with R Markdown/Quarto, or doing data analysis—e.g., "write an R function", "refactor this R code", "create a Shiny dashboard", "set up package tests", "debug R errors".

R benchmarking, profiling, and performance analysis with reproducibility and measurement rigor. Use when timing R code execution, profiling with Rprof or profvis, measuring memory allocations, comparing function performance, or optimizing bottlenecks—e.g., "benchmark R function", "profvis profiling", "microbenchmark comparison", "performance analysis", "memory profiling".

Python code quality with Ruff linting/formatting and ty type checking. Use when configuring Ruff rules, setting up ty type checking, writing pyproject.toml quality config, creating pixi quality tasks, enforcing type annotations, or fixing lint errors—e.g., "set up ruff and ty", "configure Python linting", "add type checking to project", "fix ruff violations".

Guides creation, validation, and packaging of AI agent skills with token-efficient design, progressive disclosure patterns, and YAML frontmatter best practices. Use when building new skills, updating existing skills, validating skill structure against standards, or packaging for distribution—e.g., "create skill", "validate SKILL.md", "package skill for sharing", "check description format".

Azure DevOps Pipelines logging-command guidance for reliable script-to-agent signaling, variable passing, and log UX. Use when writing or debugging `##vso[...]` and `##[...]` commands in YAML/Bash/PowerShell pipelines, troubleshooting output variable scope, handling secrets and masking behavior, or publishing summaries/artifacts from scripts. Pair with `azuredevops-pipelines-template` when template architecture and logging semantics are both in scope.

R package testing with testthat 3rd edition. Use when writing R tests, fixing failing tests, debugging errors, or reviewing coverage—e.g., "write testthat tests", "fix failing R tests", "snapshot testing", "test coverage".

Base R error handling with tryCatch, withCallingHandlers, and custom condition classes. Use when implementing error recovery, debugging conditions, or working with stop/warning/message—e.g., "tryCatch in R", "custom condition class", "handle warnings and errors", "error recovery patterns".

Expert in building browser extensions that solve real problems - Chrome, Firefox, and cross-browser extensions. Covers extension architecture, manifest v3, content scripts, popup UIs, monetization ...

Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, an...

This skill should be used when the user asks to "test for broken authentication vulnerabilities", "assess session management security", "perform credential stuffing tests", "evaluate ...

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatt...

Use before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.

Build production-ready Web3 applications, smart contracts, and decentralized systems. Implements DeFi protocols, NFT platforms, DAOs, and enterprise blockchain integrations.