「compute」の検索結果

Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields

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Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.

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。

Automated cell behavior analysis from microscopy or XR lab recordings. Classifies cell motion phenotypes (migration, proliferation, apoptosis, division, quiescence), computes population-level quantitative metrics (growth rate, migration velocity, directionality index), and emits structured JSON for downstream reporting, plotting, or ELN integration.

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。

Computes albumin‑adjusted (corrected) serum calcium using total calcium and albumin to adjust for hypo‑ or hyperalbuminemia. Triggered when a clinician notes hypoalbuminemia and requests corrected calcium to assess true hypercalcemia of malignancy severity.

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。

This skill recommends computed tomography (CT) as the initial imaging modality for primary aldosteronism (PA) evaluation in Japan, citing its accessibility and comparable performance to MRI. It is triggered when a clinician orders imaging for suspected PA and asks 'What imaging should I start with?' or seeks a cost-effective initial evaluation.

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Propose the next dependency-ready sprint from the ALREADY-DECOMPOSED roadmap — standup's active sibling. Reads docs/roadmap-execution-plan.md + the memory backlog, computes per-task readiness from the roadmap's machine-readable status, orders with roadmap-planner's graph engine, and emits a proposed task-set (sprint-plan manifest shape) with per-feature done-criteria, excluding unready tasks and naming their unmet prerequisite. PROPOSES ONLY — the human confirms/edits before /triage routes it (typically to the `power` track). Distinct from `sprint-plan`, which decomposes a fresh vision.

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。

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding 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。

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and 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。

Diagnose why an existing implementation differs from reference evidence using AE/SSIM, pixel, section, computed-style, and transition diffs. Use for post-implementation mismatch or repair guidance; not baseline capture or full clone generation.

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。

Expert-level neural engineering covering neural interfaces, brain-computer interfaces, neural signal processing, deep brain stimulation, and neuroprosthetics.

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。

Expert-level graph theory knowledge. Use when working with graph algorithms, network flows, matching, graph coloring, planar graphs, spectral graph theory, random graphs, or graph applications in computer science and networks. Also use when the user mentions 'shortest path', 'minimum spanning tree', 'network flow', 'bipartite matching', 'graph coloring', 'clique', 'independent set', 'vertex cover', 'planarity', 'graph isomorphism', 'adjacency matrix', or 'Laplacian'.

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。

Universal desktop application controller for Windows. Automate any desktop app — send messages, click buttons, type text, take screenshots, and interact with native Windows applications. Use this skill when the user asks to: "control my computer", "automate desktop", "send a message on [any chat app]", "click on [something]", "type into [app]", "take a screenshot", "操控电脑", "自动化桌面", "给某某发消息", "打开某某软件", "截个屏", or any task involving interacting with Windows desktop applications. WeChat triggers: "send a WeChat message", "message someone on WeChat", "打开微信发消息", "给某某发微信", "微信发送", "微信发消息", "用微信发", "帮我发微信". Email/Web triggers: "打开邮箱", "发邮件", "send email", "open browser", "操控浏览器", "网页操作". Supports: WeChat, WeCom, DingTalk, Feishu/Lark, QQ, Telegram, Slack, Teams, any browser-based web app, and any other Windows desktop application. Inspired by OpenAI's playwright-interactive skill, combining code-based automation with visual screenshot feedback loops. ROUTING: For web/Electron apps (email, Slack, browser), prefer Playwright with CSS selectors for speed and precision. For native desktop apps (WeChat, QQ, DingTalk), use Win32 API automation.

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 to run a depth-bounded self-correction loop (attempt → critic verdict → re-attempt) as a tunable test-time compute knob — a do-and-judge specialisation, default off, capability-gated.

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。

Geração de imagens com tecnologia de ponta usando Stable Diffusion via HuggingFace Diffusers. Use quando gerar imagens a partir de prompts em texto, realizar tradução de imagem para imagem, inpainting ou construir pipelines de difusão customizados.

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。

Modelo de fundação para segmentação de imagens com transferência zero-shot. Use quando precisar segmentar qualquer objeto em imagens usando pontos, caixas ou máscaras como prompts, ou gerar automaticamente todas as máscaras de objetos em uma imagem.

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。

Load this skill when writing, reviewing, or interpreting behavioral accessibility automation — tests that manipulate a rendered page (resizing the viewport, pressing real keys, capturing and comparing screenshots) rather than only inspecting static markup or a single computed accessibility tree. Covers Reflow risk (SC 1.4.10) and Focus Visible risk (SC 2.4.7) checks. Absolutely always report indicator results separately from confirmed failures. Under no circumstances treat a clean behavioral scan as WCAG conformance, or collapse `cantTell`/test-error/indicator outcomes into a single pass/fail signal.

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。

[DevOps] Use when deploying to Cloudflare (Workers, R2, D1, KV, Pages), Docker, or GCP (Compute Engine, GKE, Cloud Run).

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 designing a user-initiated macOS emergency switch that helps the user save context before stopping, switching tasks, or leaving the computer — capture must be transparent, local-first, and easy to delete.

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 or update this Windows computer's fixed third-party Codex helper toolchain when the user requests local helper-tool maintenance. Always show the complete registered list and obtain confirmation for each update group; exclude Codex itself and general runtimes unless explicitly added.

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Plan, scaffold, implement, and integrate a complete new emulated computer or console in the EMULATORS repository. Use when Codex creates a new machine, platform, hardware model, or emulator executable; decides which existing CPUs, chips, memory, I/O, video, sound, ROM, and framework classes to reuse or extend; or coordinates several emulator subsystems into a bootable tested system.

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。

Generate a monthly digest of the skills in this repo's flow system — a render-only HTML + plain-text email listing every skill grouped by job, plus a "what was added / updated / removed since last digest" band computed from git history. Use when asked for a "skills digest", "what skills do we have", "skills report", "what skills changed", or to keep the skill surface from sprawling.

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。

Efficiently extract and convert the contents of any local file—such as PDF, DOCX, DOC, ODT, RTF, XLSX, XLS, or HTML—into clean, well-formatted markdown saved to disk. Use this skill whenever the user requests to parse, read, or extract information from a file on their computer, including phrases like “parse this PDF”, “convert this document”, “read this file”, “extract text from”, or when a local file path (not a URL) is provided. This skill offers advanced options like generating AI-powered summaries and answering questions based on the file's content. Prefer this tool over `scrape` when handling local files to deliver precise, structured outputs for downstream 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。

Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations based on workload requirements, performance needs, and budget constraints. No Azure account required — uses public documentation and the Azure Retail Prices API. WHEN: recommend VM size, which VM should I use, choose Azure VM, VM ...

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。

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".

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。