「shipping」の検索結果

Build complex claude.ai HTML artifacts with React and Tailwind. Anthropic's reference workflow for shipping rich, embeddable artifacts.

日本語の概要は準備中です。原文の説明を表示しています。

300+ design rules from Apple HIG, Material Design 3, and WCAG 2.2 for cross-platform apps. Useful when shipping a single design across iOS, Android, and the web.

日本語の概要は準備中です。原文の説明を表示しています。

Use the RuView `wifi-densepose` CLI binary (incl. MAT scan/status/zones/survivors/alerts/export subcommands), the REST API (`wifi-densepose-api`, Axum), and the browser/WASM build (`wifi-densepose-wasm`, `wifi-densepose-wasm-edge`). Use when integrating RuView into another program, scripting it from the shell, exposing it over HTTP, or shipping it to the browser / ESP32-WASM3.

日本語の概要は準備中です。原文の説明を表示しています。

Train RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.

日本語の概要は準備中です。原文の説明を表示しています。

Retrieve live disruption status for the 13 monitored maritime chokepoints (Suez, Hormuz, Malacca, Bab el-Mandeb, Panama, and more). Use when the user asks whether a strait or canal is disrupted, congested, or safe for shipping right now.

日本語の概要は準備中です。原文の説明を表示しています。

Stop coding agents from shipping generic UI. Extend the product's design system, use UIZZE evidence only when useful, cover required states, and inspect the rendered result.

日本語の概要は準備中です。原文の説明を表示しています。

Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

日本語の概要は準備中です。原文の説明を表示しています。

Implement saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservices where 2PC is unavailable, designing compensating actions for failed order workflows that span inventory, payment, and shipping services, building event-driven saga coordinators for travel booking systems that must roll back hotel, flight, and car rental reservations atomically, or debugging stuck saga states in production where compensation steps never complete.

日本語の概要は準備中です。原文の説明を表示しています。

Verify an MCP server before release by exercising a real protocol session, comparing runtime capabilities with source and documentation, testing failure paths, and recording reproducible evidence. Use when shipping or reviewing an MCP server, tool, resource, prompt, catalog, or install path.

日本語の概要は準備中です。原文の説明を表示しています。

Stop coding agents from shipping generic UI. Use UIZZE's 800,000+ real web and iOS screens to build product-specific interfaces, define a design contract, cover required states, and run a hard finish gate. Use when designing, implementing, redesigning, critiquing, or pre-ship reviewing a web or iOS interface in Codex, Claude Code, Cursor, Copilot, or another coding agent. Trigger with "anti-ui-slop", "stop UI slop", "ground this UI in real screens", or "run the UI finish gate".

日本語の概要は準備中です。原文の説明を表示しています。

The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Idempotent: running on an existing skill improves it (bug fix, new input, quality pass) instead of starting from scratch. Every skill declares an EVAL CONTRACT (its goal + skill-specific dimensions + hard-fails) so the cross-modal eval judges THIS skill's real purpose, not generic slop. Cross-modal eval runs BEFORE tests: 3 frontier models from different providers critique the output against the contract, you iterate to quality, THEN write/update tests that lock in the proven-good behavior. NO-REGRESSION LAW: any edit to a skill must score >= the previous iteration's eval — forward only, never back. For skills that back a scheduled job, an edit MUST re-run a representative task and eval it before shipping.

日本語の概要は準備中です。原文の説明を表示しています。

Use when breaking a large, complex, messy, or hard-to-review pull request into multiple smaller PRs; planning stacked PRs; extracting independent changes from a branch; splitting mixed refactor and behavior changes; managing drift after review feedback; rebasing follow-up PRs as earlier PRs change; or preserving original branch intent while shipping incrementally.

日本語の概要は準備中です。原文の説明を表示しています。

/cs:caio-review <plan> — Eval-demanding Chief AI Officer interrogation of any plan that involves AI: model selection, risk classification, cost economics, or AI hiring. Use when shipping an AI feature without an eval set, choosing between API, fine-tune, and self-hosted, or classifying a use case under the EU AI Act.

日本語の概要は準備中です。原文の説明を表示しています。

Execute a plan or concrete work prompt end-to-end. Use when implementing from a plan document, a spec path, or a clear build request; use ce-debug for open-ended bugs. Use when an outer orchestrator needs implementation and local verification only, without the shipping tail.

日本語の概要は準備中です。原文の説明を表示しています。

Review a named diff or PR for bugs, regressions, tests, and standards. Use when asked to review code or when a shipping skill needs a review receipt. Use when asked to apply this review's findings locally. Use ce-resolve-pr-feedback for feedback already left on a PR.

日本語の概要は準備中です。原文の説明を表示しています。

Polish a working feature through user-directed live browser feedback. Use when a functional feature needs focused UX refinement before shipping.

日本語の概要は準備中です。原文の説明を表示しています。

Improve LifeOS from what the best practitioners are shipping around AI harnesses — Anthropic first (changelogs, docs, releases), then trusted creators, trending repos, and the system's own reflections — extracting concrete techniques and filtering them against verified current state so nothing already-done or rejected is re-recommended. USE WHEN upgrade, system upgrade, check Anthropic, new Claude features, algorithm upgrade, LifeOS upgrade, mine reflections.

日本語の概要は準備中です。原文の説明を表示しています。

Generates production-ready TypeScript CLIs via a 3-tier template system (manual arg parsing, Commander.js, oclif), each shipping full implementation, docs, package.json, strict config, JSON output, and exit-code compliance. USE WHEN create CLI, build CLI, command-line tool, wrap API, add command, upgrade tier, TypeScript CLI. NOT FOR LifeOS skill scaffolding (use CreateSkill).

日本語の概要は準備中です。原文の説明を表示しています。

Starts a CARLA UE 5.8 server from a ue58-dev tree — the editor binary in -game mode (the working headless server), the packaged shipping build, or the full editor — with offscreen/nullrhi rendering, custom RPC and streaming ports, and native ROS 2 via the single-dash -ros2 flag. Probes a running server, and stops one cleanly despite the 15-character process-name truncation. Use when the user asks to "run/start CARLA UE5", "launch a ue58 server", "run headless", "publish ROS 2 topics from UE5", or a client cannot reach the simulator.

日本語の概要は準備中です。原文の説明を表示しています。

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

日本語の概要は準備中です。原文の説明を表示しています。

Convex code reviewer — security, auth, validators, performance, and pattern checks for code in a convex/ directory. Use to review or audit Convex functions before shipping.

日本語の概要は準備中です。原文の説明を表示しています。

CCPM - spec-driven project management: PRD → Epic → GitHub Issues → parallel agents → shipped code. Use this skill for anything in the software delivery lifecycle: writing a PRD ('write a PRD for X', 'let's plan X', 'scope this out'), parsing a PRD into an epic, decomposing an epic into tasks, syncing to GitHub ('sync the X epic', 'push tasks to github'), starting work on an issue ('start working on issue N', 'let's work on issue N'), analyzing parallel work streams, running standups ('standup', 'run the standup'), checking status ('what's next', 'what's blocked', 'what are we working on'), closing issues, or merging an epic. Use ccpm any time the user is talking about shipping a feature, managing work, or tracking progress — even if they don't say 'ccpm' or 'PRD'. Do NOT use for: debugging code, writing tests, reviewing PRs, or raw GitHub issue/PR operations with no delivery context.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare and optimize browser-game 3D assets. Use when the user asks for GLB or glTF shipping work, including Blender cleanup and export, collision or LOD setup, compression, texture packaging, and runtime validation.

日本語の概要は準備中です。原文の説明を表示しています。

Use when building or debugging interactive 3D scenes on the web with Three.js (scene/camera/renderer, lights/materials, GLTF loading, controls, performance). Helpful for designers shipping 3D UI moments.

日本語の概要は準備中です。原文の説明を表示しています。