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Generate, validate, and review k6 test scripts — load, stress, spike, soak, smoke, breakpoint, functional, and protocol. Covers HTTP, WebSocket, gRPC, browser, all executors, thresholds, checks, custom metrics, the k6-testing library, k6 Cloud execution, and the xk6 extension ecosystem; uses the xk6-docs CLI (with grafana.com web fallback) for docs lookup and validates every script by running it. Use when writing, generating, validating, or debugging any k6 or load-test script (including plain-language asks like "load test this API" or "stress test my service"), choosing executors/scenarios, or setting thresholds. For end-to-end website performance suites use k6-perf-test-website; for documenting k6 itself use k6-docs.

Load automatically when user asks to learn Medusa development (e.g., "teach me how to build with medusa", "guide me through medusa", "I want to learn medusa"). Interactive guided tutorial where Claude acts as a coding bootcamp instructor, teaching step-by-step with checkpoints and verification.

Generate opening hooks and post titles for long-form articles in EN or FR — blog posts, Substack/Medium/dev.to, LinkedIn long-form, paid newsletters, opinion essays, reported features, technical deep-dives. Trigger whenever the user asks for a hook, opening, lede, intro, first sentence/paragraph, opener, accroche, attaque, phrase d'accroche, or première phrase — including making a flat intro punchier or rewriting a draft opening. Also trigger when user asks for a post title, titre d'article, headline, or when ghostwriting skills reach the opening or titling step. Proposes 3-4 hooks pulling distinct psychological levers (curiosity gap, contrarian, scene, promise, authority), 2 candidates each, waits for the user to pick. Do NOT trigger for social posts (LinkedIn feed, Twitter/X, TikTok, Bluesky, Threads), READMEs or doc first lines, taglines, email subjects or openers, ad copy (Google/Meta Ads), landing-page headlines, press releases, SEO meta, fiction openings, talk/podcast/video scrip

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.

Use when learning Rust concepts. Keywords: mental model, how to think about ownership, understanding borrow checker, visualizing memory layout, analogy, misconception, explaining ownership, why does Rust, help me understand, confused about, learning Rust, explain like I'm, ELI5, intuition for, coming from Java, coming from Python, 心智模型, 如何理解所有权, 学习 Rust, Rust 入门, 为什么 Rust

This skill orchestrates autonomous discovery of brand materials across enterprise platforms (Notion, Confluence, Google Drive, Box, SharePoint, Figma, Gong, Granola, Slack). It should be used when the user asks to "discover brand materials", "find brand documents", "search for brand guidelines", "audit brand content", "what brand materials do we have", "find our style guide", "where are our brand docs", "do we have a style guide", "discover brand voice", "brand content audit", or "find brand assets".