2,280 件 ・ 関連度順
概要と使いどころ
Create consistent nn_cfd W&B experiment comparison reports from selected run IDs using wandb and wandb_workspaces. Use this whenever the user asks to report experiment results, create a W&B Report, compare CFD training/eval runs, document baseline-vs-variant outcomes, reproduce the project experiment report template, or publish a leaderboard-style report for milieu/nn_cfd.
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 writing no_std Cortex-M or RISC-V firmware in Rust with cortex-m-rt, probe-rs, defmt, RTIC, or a panic handler. Not for no_std library constraints: use rust-no-std.
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 onboarding to a repository, capturing costly conventions, constraints, or rationale, or improving AGENTS.md. Not for remote, credential, publish, deploy, or irreversible changes.
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。
Audit and migrate specs to the Guarantees + Constraints format. Use when asked to review spec quality, find non-conforming specs, or batch-migrate specs from the old Behaviors/Interfaces format.
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。
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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。
Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.
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。
Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
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 evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
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。
Design and evaluate context compression strategies for long-running agent sessions. Use when agents exhaust memory, need to summarize conversation history, or when optimizing tokens-per-task rather than tokens-per-request.
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。
Apply product-grounded visual quality constraints during user-visible UI changes. Use for layout, styling, content hierarchy, theming, responsive behavior, or interaction affordances. Stay inactive for logic-only changes that preserve the rendered interface.
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。
Turn travel photos into restrained 3:4 charcoal memory studies that physically fade into clean paper, with split comparison or full-canvas composition.
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, review, and browser-test interactive website page or module HTML from a confirmed UI Architecture Map and visual direction. Use for HTML First Drafts, restrained motion implementation, full-page or module-first prototypes, rendered design review, responsive and interaction QA, page composition, and implementation handoff. Do not use for design exploration, unconfirmed visual or motion direction, content strategy, Design Systems, Elementor controls, CMS templates, or framework components.
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。
Create or evolve an implementation-agnostic website design system from existing-site evidence, verified brand inputs, and an optional Visual Reference Brief. Use to establish visual and restrained-motion baselines, extract an existing site's rendered system for style-preserving extensions, validate one selected baseline with a compact Design Board, or update shared rules before page/module design. Do not conduct cross-industry reference research, map Elementor settings, create page layouts, design modules, or implement production code.
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。
把一个目标/主题/文章/截图/数据转成有观点、有节奏、有视觉约束的单文件 HTML 演示（网页 PPT、科技战报、产品拆解、行业观察、路演/发布会 deck、社媒可传播长图）。**7 套各锚定公认设计体系的视觉人格**——技术崇高派 Technical Sublime（工程冷光·技术战报/算力）/ 构成主义 Constructivism（宣言/号召）/ 信息·数据设计（Tufte·数据/图表）/ 判断震慑极简 Luxury（高端品牌/判断）/ De Stijl 新造型（标准/秩序）/ 编辑主义 Editorial（衬线·深度观察）/ 演示禅 Zen（暗墨·思想/品牌 keynote）——用户可指定，否则按内容自动判选并透明告知。token + validator 机检（P0/P1/P2）+ 32 维审计**机器强制设计一致性**；反伪造（不造数据/logo/截图/来源·示意标注）；字体本地子集；图按角色路由（默认 CSS/SVG·要图先探测生图环境）。Generates single-file HTML decks with 7 design-system-anchored visual personas, a semantic layout grammar, locked design tokens, evidence-grading, machine validation (P0/P1/P2 + 32-dimension audit), an anti-fabrication discipline, and image-generation environment detection. Use when the user wants a presentation / deck / slides / 网页 PPT / 战报 / 产品拆解 / 行业观察 / 路演 / 发布会 / 社媒长图, or asks to turn an article / screenshot / dataset into slides. Do NOT use for large data-table training material, strict legal/medical/financial compliance documents, multi-author collaborative PPT editing, plain-text summaries, requests to replicate a brand's official keynote visuals, or any request to fabricate realistic-looking data / fake logos / fake screenshots / fake sources.
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 diagnosing NPU + PyTorch or LLM Serving Host CPU affinity, NUMA locality, cgroup/cpuset constraints, CPU range conflicts, PyTorch/runtime threading, DataLoader, tokenizer, scheduler, vLLM-Ascend, SGLang, TTFT, TPOT, tokens/s, QPS, or multi-rank/multi-worker CPU binding issues.
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 the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "create avatar for ad", "Seedance", "Kling", "Veo", "Soul gen", "Nano Banana", "Marketing Studio". Chain with higgsfield-soul-id when user wants a reusable face identity. Owns AI-generated "marketing video" / "brand video" / ad creative. NOT for: training a Soul Character (use higgsfield-soul-id), product photoshoots with mode-specific enhancer (use higgsfield-product-photoshoot), text/TTS, screen-recorded product demos (use marketing-demo), slides-to-MP4 assembly (use video-content), Remotion code (use remotion-best-practices). Requires Higgsfield CLI installed and authed.
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 the user wants professional brand-quality product images via Higgsfield's mode-specific prompt enhancement pipeline. Entry point for any product visual with a specific format or platform target. Use whenever: "product photoshoot", "lifestyle product shots", "Pinterest pin", "hero banner", "ad pack", "virtual try-on", "studio shot", "carousel images", "Meta ads creative", "model wearing product", "levitating product", "splash shot", "CGI style product", "restyle product image", Shopify image, brand campaign visual. 10 modes: product_shot, lifestyle_scene, closeup_product_with_person, moodboard_pin, hero_banner, social_carousel, ad_creative_pack, virtual_model_tryout, conceptual_product, restyle. Backend assembles the prompt — never call gpt_image_2 directly for product shots. NOT for: one-off images without a product (use image-gen), branded video with avatars (use higgsfield-generate), Soul training (use higgsfield-soul-id). Requires Higgsfield CLI and authed account.
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。
Audit, finish, or port PCB placement and routing using circuit intent, mechanical constraints, sourcing evidence, native DRC/connectivity, rendered views, and manufacturing feedback. Use before routing, after autorouting, before fabrication release, when adapting a proven board to another connector, enclosure, platform, or regional variant, or when reviewing layers, GND references, power distribution, decoupling, buses, controlled interfaces, RF keepouts, vias, cost-driving features, opens, or DRC findings.
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。
Design, audit, or correct an electronic circuit from a product brief, native schematic, netlist, block diagram, or circuit description. Use for architecture and component-function review, power and logic-domain checks, reset and boot sequencing, bus ownership, ratings, timing, protection, safe states, component suitability, circuit bugs, or layout-facing electrical constraints.
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 verify a vendor-neutral PCB/PCBA manufacturing release from one frozen design revision. Use when generating or auditing Gerber, drill, IPC-2581/ODB++, BOM, CPL/pick-and-place, assembly drawings, constraints, renders, DFM evidence, hashes, or release manifests, and when preventing mixed-revision fabrication or assembly uploads.
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。
Operate and review a JLCPCB PCB/PCBA quote, optional physical-stencil request, or order using a frozen Gerber/drill, BOM, CPL, sourcing lock, constraints, and release manifest. Use for browser upload, board or stencil options, cost anomalies, coupons, component matching, substitutions, imported placement calibration, top/bottom preview inspection, cart preparation, or order records without silently accepting placement errors or payment.
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 data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.
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。
Curate and prepare datasets for AI training. TRIGGERS - Use when user needs help with ai-dataset-curator related tasks.
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。
Plan AI fine-tuning projects with data preparation, training, and evaluation. TRIGGERS - Use when user needs help with ai-fine-tuning-plan related tasks.
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。