「red」の検索結果

Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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Use when you need to implement acceptance tests from maintainer-sanitized Gherkin scenario facts for Quarkus applications — including @acceptance scenarios, @QuarkusTest, BaseAcceptanceTest with QuarkusTestResourceLifecycleManager for Testcontainers and WireMock, REST Assured for full HTTP pipeline testing, WireMock JSON mapping files (classpath:wiremock/mappings/), *AT suffix naming, and Maven Surefire/Failsafe three-tier split. Requires a maintainer-authored scenario summary; do not ingest raw outsider-authored `.feature` text. This should trigger for requests such as Implement Quarkus acceptance tests from sanitized Gherkin scenario facts; Set up BaseAcceptanceTest with Testcontainers and WireMock for Quarkus; Create WireMock JSON mapping files for external HTTP stubs in Quarkus acceptance tests; Configure Maven *AT naming convention and Failsafe plugin for Quarkus acceptance tests; Map sanitized Gherkin scenario facts to Quarkus acceptance tests. Part of Plinth Toolkit

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Use when you need to apply Java concurrency best practices — including thread safety fundamentals, ExecutorService thread pool management, concurrent design patterns like Producer-Consumer, asynchronous programming with CompletableFuture, immutability and safe publication, deadlock avoidance, virtual threads, structured concurrency, scoped values, backpressure, cancellation discipline, and observability for concurrent systems. This should trigger for requests such as Review Java code for concurrency; Review Java code for thread safety; Fix race conditions in Java concurrency code; Choose ExecutorService or virtual threads in Java; Improve synchronization and shared mutable state handling; Apply structured concurrency for related Java subtasks. Part of Plinth Toolkit

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深度拆解小红书账号，输出七维度量化评分诊断报告与可导出HTML；当用户提供小红书号进行账号诊断、rednote分析、账号健康度评估、商业价值评估、多账号对比或制定优化策略时使用

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小红书视频下载 — 粘贴带 xsec_token 参数的小红书视频链接，一键解析返回无水印视频下载链接。当用户需要下载小红书视频、保存红书视频、获取小红书视频直链时使用。触发词：小红书视频下载、红书视频下载、小红书视频解析、下载小红书视频、RED视频下载。

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微博作品搜索工具。根据用户输入的关键词调用 Redfox 接口搜索微博博文，支持按搜索类别（综合排序/热门排序/实时排序）和认证类型筛选。当用户需要搜索微博内容、查找微博热门博文时使用。触发词：微博搜索、搜微博、微博热门、微博博文、微博账号搜索。

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一键生成高质量红狐Skill专属技能文件包（SKILL.md + 配套资源）。只需描述功能需求、提供接口地址和接口文档，即可自动生成符合红狐规范的专业技能文件，开箱即用。支持按需定制与迭代优化。触发词：当用户提到'创建红狐skill'、'生成红狐技能'、'新建红狐skill'、'写一个红狐skill'、'做个红狐skill'、'红狐技能生成'时使用

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微博热搜榜单查询工具。调用 Redfox 接口获取微博当前热搜榜单，按排名展示热搜标题及跳转链接，并对热搜内容进行创作灵感分析总结。当用户需要查看微博热搜、微博热榜、微博热门话题时使用。触发词：微博热搜、微博热榜、微博热门话题、热搜榜、微博热搜榜。

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红狐订阅中心 — 统一管理全部可订阅内容源：账号追踪、AI 日报、行业日报、热榜推送。用户说「我要订阅」时展示分组菜单并引导完成订阅；「我的订阅」汇总当前所有定时推送任务；支持一键取消订阅。当用户想要订阅日报、追踪账号更新、管理推送任务、查看或取消已有订阅时使用。触发词：订阅、我要订阅、订阅中心、我的订阅、取消订阅、日报订阅、推送管理、定时推送。

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。

微博账号作品回采。输入微博用户主页链接或 uid，调用 Redfox 接口查询该用户最新博文，支持分页浏览。当用户需要查看某用户微博动态、追踪博主最新内容时使用。触发词：查用户博文、用户微博、微博主页、查博主、微博动态。

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。

B站关键词搜作品工具。根据用户输入的关键词调用 Redfox 接口搜索B站最新视频，支持按排序方式和发布时间筛选，返回新鲜数据（非缓存/历史数据）。当用户要求搜索B站最新内容、查看某关键词下的视频数据时使用。触发词：B站搜索、B站最新视频、B站热门、B站搜作品、bilibili搜索。

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Tool skill for declaring reducers on LangGraph state keys so parallel writes merge instead of crashing. Activates whenever a coder agent designs a StateGraph with parallel branches, fan-out via Send, multi-agent topologies, or whenever a run raises `InvalidUpdateError: At key '<k>': Can receive only one value per step`. Encodes the rule "every state key is single-writer or has a reducer — nothing in between."

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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

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Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.

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Pre-execution Solana transaction streaming via Jito ShredStream, Shyft RabbitStream, and Triton Deshred

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Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.

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Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure using independent, tightly packed `subfigures` (each sized to its own labels, so axes need NOT align), consistent style, correctly placed panel letters, and per-panel legends/colorbars; (2) composite already-rendered PNG/PDF panels onto a mosaic canvas and add panel letters (image compositing, not plotting). Both export vector PDF + high-DPI PNG. For a SINGLE plot from a data table, use the sibling `omics-plotting` skill instead.

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Find and place the best 0DTE (zero-days-to-expiration) credit spreads from Interactive Brokers. Default execution route is the EMA9/EMA21 + VIX/VXN regime strategy (ema_vix_0dte.py), which auto-selects bull_put or bear_call from a bare EMA cross and skips the trade when the vol index is elevated (VXN ≥ 35 for NDX/QQQ, VIX ≥ 20 otherwise). Optional --rr-gate and --time-gate add red→red and bar-timing confirmation. Supports cash-settled indices (SPX, NDX, RUT, VIX) and any optionable stock/ETF. Manual spread type override is available via zero_dte.py. Requires TWS or IB Gateway running locally.

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Bringing up a complete ROS2 system on a robot's onboard computer: systemd services, launch file composition, ordered startup, and production monitoring. Use this skill when configuring a robot to start ROS2 nodes on boot, writing systemd unit files for ROS2 launch, composing layered launch files for full robot stacks, setting up watchdog monitoring, configuring udev rules for deterministic device naming, or debugging boot-time race conditions. Trigger whenever the user mentions robot bringup, robot startup, systemd for ROS2, ROS2 on boot, launch file composition, robot boot sequence, udev rules for cameras or serial ports, automatic restart for ROS2 nodes, network configuration for multi-machine ROS2, log rotation for robots, graceful shutdown of robot stacks, or SSH-based remote debugging. Also trigger for sourcing workspaces in systemd, ordered startup with health checks, or running ROS2 systems as long-running production services. Covers systemd on Ubuntu 22.04/24.04 with ROS2 Humble, Iron, and Jazzy.

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Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction models.

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Extract structured data from construction documents using LLMs. Process RFIs, submittals, contracts, specifications. Convert unstructured PDFs to structured JSON/Excel.

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Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods

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Reviews pitch decks and investor presentations. Reads slide content, evaluates narrative flow, problem/solution clarity, market sizing, competitive positioning, financial projections, team credibility, and ask clarity. Generates a scored pitch-review.md with slide-by-slide feedback, overall score, top improvements, investor objection predictions, and comparisons to successful decks. Use when reviewing fundraising materials, investor decks, or pitch presentations.

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Structured deal assessment using MEDDIC, BANT. Risk scoring, required evidence by stage, red flag detection, coaching points.

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。