6,441 件 ・ 関連度順
概要と使いどころ
在授权渗透测试中系统性挖掘服务端请求伪造（SSRF）。当目标存在"服务端按用户可控的 URL/主机/地址发起出站请求"的功能——如 URL 预览、图片/视频抓取、Webhook、集成回调、导入远程资源、PDF/截图渲染、代理转发——需要判定是否可绕过 SSRF 防护访问内网/云元数据/受限服务时使用。适用目标类型 REST API / Web 应用。触发场景包括用户说"测下这个 URL 抓取有没有 SSRF""这个 webhook 能打内网吗""帮我挖 SSRF/内网探测""看看能不能读云元数据"。输出：带原始请求/响应证据的 finding 报告（含 killed 记录）。
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。
Type-safe URL query state management with nuqs 2.x. Use when writing or reviewing code that uses useQueryState, useQueryStates, URL search params, query parameters, nuqs parsers, createLoader, createSearchParamsCache, createSerializer, or URL state in Next.js, React, Remix, React Router, or TanStack Router.
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 working with Baselime — baselime serverless observability platform for AWS Lambda, CloudFlare Workers, and Vercel functions. Covers log and trace querying, alert management, service discovery, and performance analysis of serverless workloads. Use when investigating serverless function performance, querying Baselime events, managing alerts, or analyzing cold start and duration metrics.
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。
Elderly Care Tech Specialist IA — Expert en technologies pour personnes âgées (fall detection, medication reminders, social connection, accessibility)
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。
Serverless and microservices development guidelines covering FastAPI, cloud-native patterns, API gateways, and best practices for scalable serverless architectures.
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。
Set up and run reinforcement learning with verifiable rewards (RLVR/GRPO) for math, code, or custom environments using the Tinker API. Use when the user wants to do RL training, GRPO, reward-based optimization, or train with verifiable rewards.
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。
Guide for using completers — TokenCompleter and MessageCompleter for text generation during RL rollouts and evaluation. Use when the user asks about generating text, completing messages, or using completers in RL environments.
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。
Upstash QStash expert for serverless message queues, scheduled jobs, and reliable HTTP-based task delivery without managing infrastructure. Use when: qstash, upstash queue, serverless cron, scheduled http, message queue serverless.
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 patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integration Use when: neon database, serverless postgres, database branching, neon postgres, postgres serverless.
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。
Search the public web and crawl URLs via the Unique AI Platform's `unique-cli web-search` command, with automatic per-turn citation tracking so cited web facts render as clickable external reference chips and `<sup>N</sup>` footnotes on the Unique platform. ALWAYS use this skill when the user asks to search the public web, research a topic online, look up news/articles/regulations, fetch the contents of one or more URLs, or perform any "two-phase" web research (search → review snippets → crawl selected URLs for full content). Routes through the Unique AI Platform's `/web-search-api` endpoints, so the engine (Google, Brave, Tavily, Jina, Firecrawl, …) and crawler are resolved server-side from the current environment — you never need to manage engine API keys yourself. Use this instead of fabricating answers from training data whenever fresh / source-cited information is needed. Do NOT use this skill for the internal knowledge base (`unique-cli search`) or for arbitrary tool execution (`unique-cli mcp`).
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。
Full SEO audit: Google Search Console data + URL Inspection API + PageSpeed Insights API + technical crawl + keyword research + metadata audit + schema markup audit + search intent analysis + Core Web Vitals monitoring. Feeds real GSC data and PageSpeed metrics into AI to surface quick wins, diagnose traffic drops, find content gaps, identify metadata mismatches, detect schema gaps, monitor page performance, and produce an actionable 30-day plan. Use this skill whenever the user asks about SEO, search rankings, organic traffic, Google Search Console, keyword performance, traffic drops, content gaps, search visibility, technical SEO, meta tags, schema markup, structured data, URL indexing, keyword research, indexing issues, page speed, performance, Core Web Vitals, LCP, INP, CLS, or Lighthouse scores. Also trigger on: "why is my traffic down", "what keywords am I ranking for", "improve my rankings", "check my search console", "SEO audit", "analyze my SEO", "technical SEO", "meta tags", "indexing issues", "crawl errors", "content strategy", "keyword cannibalization", "search intent", "schema markup", "structured data", "URL inspection", "page speed", "performance score", "core web vitals", "lighthouse", or any organic search question. If in doubt, trigger. This skill handles everything from quick GSC checks to deep technical audits with performance monitoring.
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。
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
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。
Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Use when deciding bulk vs single-cell (bulk amplicon/RNA-seq -> MiXCR analyze preset -> VDJtools/immunarch depth-normalized diversity and overlap -> figures; 10x paired VDJ -> MiXCR 10x preset or Cell Ranger -> scirpy gene-expression integration, chain QC, clonotype clusters); and TCR vs BCR (TCR -> exact CDR3-nt+V/J clonotypes, VDJtools diversity is fine; BCR -> somatic hypermutation makes exact clonotypes wrong -> Immcantation distToNearest/findThreshold clonal clustering, germline reconstruction, SHM, Dowser lineages); selecting the MiXCR 4.x preset by chemistry and activating its license; downsampling to equal depth before comparing diversity or overlap; and optionally annotating antigen specificity.
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。
Gets actor and level 3D bounding boxes and projects them into a camera image as 2D boxes — the dataset-annotation workflow. Lists box geometry, draws 3D boxes in the world via debug, or captures one camera frame and writes an annotated PNG plus a JSON of 2D boxes for matching actors. Use when the user asks to "get bounding boxes", "draw boxes around the cars", "project boxes onto the camera", or "generate a detection dataset / annotations".
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。
Controls the map's light sources through the CARLA LightManager — turn street lamps, building/facade lights, vehicle lights or all of them on/off, set their colour and intensity, and toggle the automatic lights-at-night cycle. Use when the user asks to "turn on the street lights", "light up the city at night", "turn off building lights", "change the street-light colour", or "make lights come on automatically at night". Distinct from hiding lamp meshes (toggle-env-objects) and the sun (set-weather).
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。
Listens to a CARLA sensor and either saves its data to files, shows it live in a window, prints a one-shot summary, or (ros-info) reports the native ROS 2 topics, QoS and enabled-for-ROS state so you can echo it from ROS instead. Cameras save as PNG (depth/semantic auto-colourised) and display in a pygame window; lidar saves as .ply and shows as a top-down scatter; IMU/GNSS/radar/collision stream to JSONL or the console. Use when the user asks to "show/view the camera", "display the lidar", "save the sensor data / capture a dataset", or "what is this sensor reading". Select the sensor by id, type, or the actor it's attached to.
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。
Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.
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。
Controls the traffic-light actors on a running CARLA server — set a light green/red/yellow/off, freeze all lights (optionally all-green), set phase timing (green/yellow/red durations), and reset to the normal cycle. Target all lights, one by id, all in a junction, or the nearest. Use when the user asks to "make the light red", "freeze all lights green", "change the light timing", "turn the traffic lights off", or "reset the lights". This is the light actors, not the Traffic Manager (that's control-traffic).
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。
Packages an agent for CARLA Leaderboard submission — builds the docker image with make_docker.sh (plain or ROS variant), checks the four required roots and the egg layout it assumes, sets the agent/track/routes environment inside the image, and verifies the image can import the stack and load the agent before submission. Use when the user asks to "build the leaderboard docker image", "submit to the leaderboard", "package my agent", or make_docker.sh fails.
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。
Moves the CARLA spectator camera, frames an actor, or follows one live — third-person/chase, first-person/driver, top-down, or front views, resolving natural-language targets like "the ego" or "the Toyota Prius" to a specific actor. Use when the user asks to "move the camera/spectator", "watch/follow vehicle X", "3rd-person view of the ego", "1st-person view of the prius", "bird's-eye view", or "look at (x,y,z)".
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。
Tunes the CARLA Traffic Manager that drives autopilot vehicles — global and per-vehicle speed, following distance, running lights/signs, lane-change behaviour, lane offset, TM-managed lights, deterministic seed, hybrid physics, and TM sync mode. Use when the user asks to "make the traffic faster/slower", "let cars run red lights", "keep more distance", "stop lane changes", "make traffic deterministic", or otherwise change how autopilot cars behave. For manually driving one car, use control-vehicle.
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。
Writes an autonomous agent for the CARLA Leaderboard — an AutonomousAgent subclass with setup/sensors/run_step/destroy, a sensor suite inside the track's budget, and the right constructor signature for the leaderboard version (1.0 and 2.x differ). Generates a working skeleton, validates the sensor configuration against the real per-track limits offline, and covers the ROS1/ROS2 agent base classes. Use when the user asks to "write a leaderboard agent", "make my agent work with the leaderboard", "add sensors to my agent", "port my agent to 2.1", or hits SensorConfigurationInvalid.
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。
Inspects a recorded CARLA .log without replaying it — show_recorder_file_info for the header/actors/frames, collision queries between actor categories, and blocked-actor queries for vehicles that got stuck. Use when the user asks to "what's in this recording", "show recorder info", "which vehicles collided", "find collisions in the log", or "which actors were blocked/stuck".
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。
Runs a route through ScenarioRunner's RouteScenario mode (--route / --route-id) with an autonomous agent driving the ego — the same mechanism the CARLA Leaderboard uses, and the only way to reach the ~34 route-only scenario classes (Accident, ParkedObstacle, HazardAtSideLane, InvadingTurn, HardBreakRoute, YieldToEmergencyVehicle, junction and actor-flow scenarios). Inspects route files, lists route ids and their scenarios, and runs one or all of them. Use when the user asks to "run a route", "drive a route with my agent", "run leaderboard scenarios without the leaderboard", or names a route-only scenario type.
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。