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Found 1,683 Skills
TypeGPU is type-safe WebGPU in TypeScript. Use whenever the user writes, debugs, or designs TypeGPU code: 'use gpu' shader functions, tgpu.fn, buffers, textures, bind groups, compute and render pipelines, vertex layouts, slots, accessors, and any TypeGPU API. Shader logic and CPU-side resources are tightly coupled - handle both sides here even if the user only mentions one (e.g. "how do I write a shader", "how do I create a buffer"). Trigger on any mention of typegpu, tgpu, "use gpu", TypedGPU, or WebGPU code written using TypeGPU's schema API (d.*, tgpu.*, std.*). Do NOT trigger for raw WebGPU (using GPUDevice/GPURenderPipeline directly without tgpu), WGSL-only questions, Three.js, Babylon.js, or WebGL.
Search and interpret bitdrift documentation for product behavior, SDK setup, API and service docs, and best practices. Use whenever the user asks how bitdrift works, how to set up the SDK, how to configure a feature, what an API or service does, or for conceptual guidance about bitdrift — even if they do not explicitly mention documentation. Also trigger when the user mentions /bd-docs or asks about bitdrift concepts, architecture, or integration guides.
Automatically generate standardized comments for Vue 2 Single-File Components (.vue). Parse the three blocks of template, script, and style, add structured comments according to the agreed format, without modifying any code logic. Trigger scenarios: Users request to add comments, supplement document comments for components, and interpret Vue 2 component structure.
Guides benchmarking and comparing explicit multi-statement transactions versus single-statement CTE transactions in CockroachDB, with fair test methodology, contention analysis, and performance interpretation. Use when comparing transaction formulations, benchmarking CockroachDB workloads under contention, investigating retry pressure, or deciding whether to rewrite multi-step application flows into single SQL statements.
Scaffold a new DataHub Micro Frontend (MFE) app with all boilerplate files. Use when the user wants to create a new micro frontend, MFE, remote app, or Module Federation app for DataHub.
Generative Engine Optimization (GEO) — make content rank in AI search answers from ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Audits existing content, rewrites for AI citation, and produces per-engine strategy. Use when asked to "optimize for AI search", "rank in ChatGPT", "GEO audit", "improve AI citations", "rank in Perplexity", "AI Overview optimization", "AI Overview ranking", "LLM SEO", "answer engine optimization", "AEO", "get cited by AI", "GEO", "generative engine optimization", "show up in ChatGPT", "appear in AI answers", "be cited by Perplexity", "SGE optimization", "Search Generative Experience", or "make my content show up in AI answers". Distinct from regular SEO — this targets generative engines, not traditional Google rankings.
Presales expert for China's government digital transformation market (ToG), proficient in policy interpretation, solution design, bid document preparation, POC validation, compliance requirements (classified protection/cryptographic assessment/Xinchuang domestic IT), and stakeholder management — helping technical teams efficiently win government IT projects.
Expert in AI recommendation engine optimization (AEO/GEO) — audits brand visibility across ChatGPT, Claude, Gemini, and Perplexity, identifies why competitors get cited instead, and delivers content fixes that improve AI citations
Write structured experiment report documents from ML/research experiment notes, configs, logs, metrics, tables, and figures. Use this skill whenever the user asks to write an experiment report, research update, mentor update, weekly experiment summary, result analysis document, or presentation-ready experiment writeup, especially when the output should explain motivation, setup, algorithms, metrics, results, figures, interpretation, conclusions, limitations, and next steps.
Run, rerun, debug, or interpret OpenClaw Parallels install, onboarding, gateway smoke, and upgrade checks.
SEO intelligence toolkit covering the full lifecycle via live web data: keyword research, rank tracking, site audits, content gap analysis, competitor keyword reverse-engineering, AI visibility across five platforms (ChatGPT, Perplexity, Google AI, Gemini, Grok), and GitHub repo SEO. Crawls real sites and SERPs via Nimble CLI — no fabricated metrics. Triggers: "SEO", "keywords", "rank tracker", "site audit", "content gap", "competitor keywords", "AI visibility", "GitHub SEO", "SERP analysis", "keyword research", "technical SEO", "keyword difficulty", "topic clusters", "ranking delta", "on-page SEO", "AI citation audit". Do NOT use for competitor business signals — use `competitor-intel` instead. Do NOT use for competitor messaging — use `competitor-positioning` instead. Do NOT use for general web scraping — use `nimble-web-expert` instead.
Plan, draft, and revise ML/AI limitations, scope, failure cases, ethics, broader impact, and conclusion caveats so they control claim boundaries without undermining the paper. Use when the user wants limitation wording, scope statements, failure-case interpretation, ethics/broader-impact text, or overclaim reduction.