Results for “recipe”

Wix business solution management recipes — REST API operations for configuring and managing Wix business solutions. Routes to: stores, bookings, get-paid, CMS, contacts, forms, media, app-installation, pricing-plans, restaurants, rich-content, sites, blog, calendar, domains, site-properties, ecommerce.

Configure ViewModel state emission with StateFlow, SharedFlow, and sealed UiState classes in Android. Use for state modeling and sharing policies; defer generic lifecycle collection, navigation-event plumbing, Hilt injection, test code, and configuration-change recipes to their specific skills.

Select a consistent visual design system for PPT slides using radius/spacing style recipes, including Sharp, Soft, Rounded, and Pill variants with component mappings and typography rules.

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.

Battle-tested Playwright patterns for writing and debugging reliable E2E, API, component, visual, accessibility, and security tests. Use when you need locator strategy, assertions, fixtures, network mocking, auth flows, trace debugging, or framework recipes for React, Next.js, Vue, and Angular. TypeScript and JavaScript.

Sweeps a codebase or UI for places that would genuinely benefit from motion, applying a strict gate to reject over-animation, and proposes precise animation recipes without implementing them.

Validates that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after deploying a disagg or multi-node recipe to confirm KV transport is correct, or use troubleshoot for already-failed pods.

Write idiomatic Kotlin 1.9+ with null safety, sealed classes, data classes, extension functions, delegates, collections, inline/reified generics, and expression syntax. Use for Kotlin language constructs, Java-to-Kotlin migration, lazy delegates, data classes, backing properties, or inline/reified generics; keep language constructs in scope even inside response models, and defer coroutine configuration, Android Context/framework, Retrofit/OkHttp client setup, and other library-specific recipes.

Diagnose your own behavior by reading the structured logs you leave behind — turns.jsonl, events.jsonl, chat_history.jsonl, scheduler.yaml. Use when something has gone wrong (a message didn't land, a scheduled job isn't firing, a communication pattern feels off), when you need to understand a pattern over many turns, or when cost / token usage needs auditing. Covers jq query recipes and points at the debugging-jobs / debugging-communication / debugging-drift companion guides for specific failure modes.

Default execution rhythm — inspect → act → verify → checkpoint — applied iteratively until the task is done or a clear blocker emerges. Use when working through a known plan, draining a queue (PRs, issues, dependency bumps), or executing a multi-step recipe where each step's output informs the next. Each iteration must produce a concrete artifact (commit, comment, decision) and a one-line state update. Stop on first unrecoverable error rather than retrying blindly. Pair with `ship` for merge-bound work and `handoff` if budget runs out mid-loop.

Route web design, motion, WebGL, and visual-styling requests into the narrowest matching sub-skill of the 79-skill MengTo/Skills web-design family — Style/Design-System Packs, Full-Site Quality & Direction, Motion & Scroll Systems, WebGL/3D Backgrounds & Shaders, Library Embeds & Integrations, Component & State Effects, and CSS Technique Primitives. Use when picking and fetching the right visual recipe for a marketing site, landing page, portfolio, or dashboard shell, then combining layers in direction → layout → background → motion → component-state → polish order. Triggers on: web design, landing page, awwwards, GSAP, Lenis, scroll animation, WebGL background, shader, glass UI, dark mode design system, editorial layout, cursor trail, progressive blur, gradient border, three.js background, vanta, globe.gl, unicorn studio, marketing site polish.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in...