2,477 件 ・ 関連度順
概要と使いどころ
WebSocket listener that SDK workers connect to. Use to expose the engine to trusted (internal) and untrusted (RBAC-protected) workers, gate function and trigger registration, and audit or transform every invocation through a middleware function. Mounts channel WebSocket endpoints on every listener.
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。
Show the architecture of a public GitHub repository with GitDiagram (gitdiagram.com) - a written explanation, its main components and source paths, how they connect, and a Mermaid diagram. Use when the user wants to understand, map, visualize or get an overview of a GitHub repository or open-source project's architecture, structure or data flow, or asks for an architecture diagram of a repo, e.g. "/gitdiagram fastapi/fastapi" or "how is vercel/next.js structured?".
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。
OAuth 2.0 and OpenID Connect expert for authorization flows, PKCE, and token management
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 Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.
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。
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
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。
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific proof obligation benefits from formal verification; use proof-writer for ordinary mathematical drafting.
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。
Profile a target (script, process, GPU, memory, interconnect) for performance analysis. Use when user says "profile", "benchmark", "bottleneck", or wants performance analysis.
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。
Two-way sync between a local paper directory and an Overleaf project, so ARIS audit/edit workflows stay on the local copy while collaborators edit in the Overleaf web UI. Use when user says "同步 overleaf", "overleaf sync", "推送到 overleaf", "connect overleaf", "Overleaf 桥接", "pull overleaf", "push overleaf", or wants to bridge their ARIS paper directory with an Overleaf project.
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 an MCP-connected agent should use Memori tools for targeted recall, summaries, post-compaction briefs, durable memory augmentation, quota checks, signup, feedback, preferences, prior context, or cross-session continuity.
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 deploying KubeEye for cluster inspection, creating InspectRule/InspectPlan resources, or retrieving inspection results. Covers InstallPlan-based deployment, OPA/PromQL/FileChange/Sysctl/Systemd/NodeInfo/FileFilter/ServiceConnect/CustomCommand rule types, and report retrieval. Always consult this skill when the user mentions KubeEye, cluster inspection, InspectRule, InspectPlan, or inspection reports.
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 agents through using the OpenShell CLI (openshell) for sandbox management, gateway registration, provider configuration and refresh, profile management, policy iteration, settings, service exposure, BYOC workflows, and attached-provider inference. Covers basic through advanced multi-step workflows. Trigger keywords - openshell, sandbox create, sandbox exec, sandbox connect, logs, provider create, profile list, profile describe, provider refresh, policy set, policy get, settings, service expose, forward, port forward, BYOC, bring your own container, inference, use openshell, run openshell, CLI usage, manage sandbox, manage provider, gateway add, gateway select.
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。
Connect a new user's accounts, learn their real work, choose a voice, and set up concrete help.
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。
Deploy the QM package from an organization-owned repository into Fly.io or AWS, onboard an administrator, configure connectors, and optionally activate Slack.
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。
Assemble and deliver one short morning digest for the person who owns the cron — what changed overnight in the sources they already connected, and what it means for their day.
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。
Deploy the QM package from an organization-owned deployment repository to Fly.io or AWS, onboard an administrator, configure connectors, and optionally activate Slack.
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 the Autoware autonomous-driving stack against CARLA on UE 5.8 over CARLA's native ROS 2 interface — no bridge process. Reports the DDS contract (rt/vehicle/status/* out, rt/control/command/* in), the Autoware-only sensor blueprints, generates the lanelet2 + point-cloud map artifacts Autoware needs instead of an .xodr, and drives the two shipped modes (classical NDT stack, or the camera-only VAD end-to-end model). Use when the user asks to "run Autoware on CARLA", "connect an AV stack", "drive the ego from Autoware", "generate a lanelet2 map", or asks which topics CARLA exchanges with Autoware.
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。
Launches a CARLA RPC server a carla.Client can connect to, working out for itself what you pointed it at — a downloaded/extracted release, a package cooked inside a checkout, or a source checkout run through the UE4 editor — headless or windowed, and shuts it down cleanly. ROS2=1 starts it with the native ROS 2 interface active (--ros2, --rmw=fastdds/cyclonedds/zenoh, --ros-domain-id). Use when the user asks to "run/start the CARLA server", "boot CARLA headless", "launch CARLA with a window", "run the CARLA I downloaded", "start CARLA with ROS2 enabled", or "serve a packaged CARLA build".
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。
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
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。
Builds security-focused full-stack web applications by implementing integrated frontend and backend components with layered security at every level. Covers the complete stack from database to UI, enforcing auth, input validation, output encoding, and parameterized queries across all layers. Use when implementing features across frontend and backend, building REST APIs with corresponding UI, connecting frontend components to backend endpoints, creating end-to-end data flows from database to UI, or implementing CRUD operations with UI forms. Distinct from frontend-only, backend-only, or API-only skills in that it simultaneously addresses all three perspectives—Frontend, Backend, and Security—within a single implementation workflow. Invoke for full-stack feature work, web app development, authenticated API routes with views, microservices, real-time features, monorepo architecture, or technology selection decisions.
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, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio/HTTP/SSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server/client projects using TypeScript or Python SDKs.
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。
Rails 7+ specialist that optimizes Active Record queries with includes/eager_load, implements Turbo Frames and Turbo Streams for partial page updates, configures Action Cable for WebSocket connections, sets up Sidekiq workers for background job processing, and writes comprehensive RSpec test suites. Use when building Rails 7+ web applications with Hotwire, real-time features, or background job processing. Invoke for Active Record optimization, Turbo Frames/Streams, Action Cable, Sidekiq, RSpec Rails.
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 Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
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。
Read before automating LinkedIn with browser_* tools. LinkedIn combines shadow DOM (#interop-outlet), strict Trusted Types CSP that silently drops innerHTML, Lexical composer, native beforeunload dialogs that hang the bridge, and aggressive spam filters — each has bitten us at least once. Verified flows for profile messaging, connection-request acceptance, feed composition, and search. Requires hive.browser-automation. Verified against logged-in production 2026-04-11.
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 a Telegram notification channel (Sentinel) for a colony by driving the browser — create a bot via @BotFather in Telegram Web, store its token, detect the chat to notify, and turn Sentinel on so the colony can ping the user on Telegram and accept replies. Use when the user asks to "set up Telegram notifications", "get pinged on Telegram", "connect Telegram for alerts", "set up Sentinel on Telegram", or clicks the in-app "Set this up with the agent" button on the Telegram step. Requires hive.browser-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。