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Found 1,366 Skills
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.
Dynamic path segments ($paramName), splat routes ($ / _splat), optional params ({-$paramName}), prefix/suffix patterns ({$param}.ext), useParams, params.parse/stringify, pathParamsAllowedCharacters, i18n locale patterns.
Reinforcement learning for trade execution optimization including order splitting, adaptive timing, and impact minimization
An Agent skill that converts long-form Markdown articles into a set of 3:4 Xiaohongshu images (1080×1440). It includes five design skins (E Elegant Editorial · Default / A Warm Apricot Latte / B Misty Pine Teal / C Charcoal Ink with Gold Accents / D Gilded Night Reading) plus three women-oriented themes (F Pink Kingdom / G Otome Night / H Ghibli) across multiple page layouts. The Agent reads the article, splits it into pages, and fills the HTML, while the Playwright renderer captures each page and exports PNGs. Use it when the user says “turn a long article into images,” “Xiaohongshu long images,” “turn this article into a Xiaohongshu post/image set/cards,” “article to images,” “lay it out with my design mockup,” “convert to a 3:4 image set,” asks for a feminine, girly, pink, otome, or Ghibli-style layout, or provides an article and mentions posting it to Xiaohongshu.
Break requirements into epics and user stories. Use when the user says "create the epics and stories list"
Create FilamentPHP v4 infolists with entries, sections, and layouts for view pages
Create outcome spec (overview, decisions, steps, edge cases). Use $ARGUMENTS as spec topic if provided.
Best practices and guidelines for Parcel, the zero-configuration web application bundler
Creates Jira epics for large features following Prowler's standard format. Trigger: When user asks to create an epic, large feature, or multi-task initiative.
Points to Michał Zalewski’s (lcamtuf) canonical American Fuzzy Lop (AFL) documentation at lcamtuf.coredump.cx/afl—coverage-guided fuzzing concepts, afl-fuzz usage, and historical technical notes for C/C++ targets. Use when the user cites AFL classic, lcamtuf’s AFL page, or needs the original upstream reference—not as a substitute for current AFL++ docs or authorized fuzzing policy.
React/Next.js 앱의 성능을 분석하고 최적화하는 스킬. 번들 사이즈, 렌더링 최적화, 코드 분할, 이미지 최적화, SSR/SSG 전략 등. "성능", "performance", "최적화", "느려", "번들 사이즈", "렌더링" 등의 요청 시 사용.
Render components on the server without sending their JavaScript to the client, dramatically reducing bundle sizes.