3,295 件 ・ 関連度順
概要と使いどころ
Ahrefs API analyst (extension). Reads referring domains, backlinks, organic keywords, and content explorer data via the tested @ahrefs/mcp@0.0.11 server. Pairs with seo-backlinks for multi-source confidence weighting.
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。
Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.
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。
MCP Apps integration for json-render. Use when building MCP servers that render interactive UIs in Claude, ChatGPT, Cursor, or VS Code, or when integrating json-render with the Model Context Protocol.
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。
React expert for hooks, state management, Server Components, and performance optimization
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 should be used for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools. Route project-shape, pipeline architecture, and task-model-fit decisions to project-development; route deciding whether to introduce sub-agents to multi-agent-patterns.
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。
MCP server integrating Slither + Aderyn + SWC patterns into Claude Code for smart contract auditing. Use when analyzing Solidity files, running DeFi-specific detectors, or generating invariants. 10 MCP tools, 86 SWC detectors, DeFi preset pack, CI/CD workflow.
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。
Rent, manage, and destroy GPU instances on vast.ai. Use when user says "rent gpu", "vast.ai", "rent a server", "cloud gpu", or needs on-demand GPU without owning hardware.
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。
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
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 publication-quality academic illustrations through a local Codex app-server bridge that uses Codex native image generation. This is a separate experimental alternative to `paper-illustration`, intended for Claude Code users who want a GPT-image-style renderer without modifying the original skill.
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 installing or configuring the WizTelemetry Platform Service extension for KubeSphere, which provides the common APIServer backend services for all WizTelemetry observability extensions
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 real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, ML options pricing, balanced news synthesis, and meme search via the Helium 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。
Official GSAP skill for GSAP plugins — registration, ScrollToPlugin, ScrollSmoother, Flip, Draggable, Inertia, Observer, SplitText, ScrambleText, SVG and physics plugins, CustomEase, EasePack, CustomWiggle, CustomBounce, GSDevTools. Use when the user asks about a GSAP plugin, scroll-to, flip animations, draggable, SVG drawing, or plugin registration.
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。
Debug inference clients that use an attached provider and its native endpoint, including hosted APIs and host-local Ollama, vLLM, SGLang, TRT-LLM, LM Studio, or NIM. Use for provider attachment, endpoint policy, credential substitution, topology, and migration from the removed managed inference endpoint. Trigger keywords - debug inference, managed inference endpoint, local inference, ollama, lm studio, vllm, sglang, trtllm, NIM, inference failing, model server unreachable, credential_endpoint_mismatch, host.openshell.internal.
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。
Arrange the tabs and split panes of a Superset workspace in the desktop app from an agent. Use when the user asks to split a pane, open a tab, run a command in a new pane, resize, swap, move, focus, close, or even out panes, lay out a dashboard of terminals, or show them something side by side, including "split this and run the tests on the right", "open the logs in a new tab", "make this pane bigger", "put the server and the tests side by side", "close those extra panes". Not for coordinating worker agents (use superset:orchestrate) or driving web pages (use superset:browser).
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。
Finds undocumented and rotting documentation in a CARLA checkout — unreleased features, Python bindings missing from the API reference, documented API that no longer exists, sensors and catalogue actors missing from their reference pages, broken links, and pages unreachable from the nav. Static only, no build or server. Use when the user asks to "audit the docs", "find undocumented features", "what needs documenting", or is preparing a release or a new default branch.
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。
Imports FBX meshes into CARLA on UE 5.8 as spawnable props — drives the editor headlessly to import the mesh, derives the prop size from its measured bounds, and registers it in PropParameters.json so it appears in the blueprint library as static.prop.<name>. Handles the FBX unit-scale problem, verifies the result against a running server, and can revert cleanly. Use when the user asks to "import a prop", "add a custom mesh/object to CARLA", "make my model spawnable", or a prop imports but never shows up in the blueprint library.
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。
Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.
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。
Reads and sets the weather on a running CARLA server via get_weather/set_weather, and turns natural-language weather requests ("heavy rain at sunset", "light fog at night", "clear noon", "dust storm") into exact WeatherParameters — cloudiness, precipitation, puddles, wind, fog, sun position, wetness, dust. Use when the user asks to "set/change the weather", "make it rainy/foggy/sunny/night", "apply a weather preset", or describes any sky/lighting condition.
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。
Installs the CARLA Python client (the `carla` package) into a chosen interpreter so the other python-api skills work — from a wheel bundled with your release or checkout, from PyPI (`pip install carla==X.Y.Z`), or by putting an older release's .egg on PYTHONPATH. Detects what is already present, picks the source that matches your simulator's version, pins numpy<2, and verifies `import carla` plus the client/server version match. Use when the user asks to "install the CARLA Python API", "pip install carla", "set up the carla python package", or when a skill reports "cannot import carla".
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。
Reads a specific actor's live telemetry from a running CARLA server — location and rotation, velocity and speed, acceleration, angular velocity, and for vehicles the control input (throttle/steer/brake), front-wheel steer angle and mass. All kinematics come from one world snapshot so they are frame-consistent. Use when the user asks "how fast is it going", "where/what heading is the car", "show the ego's telemetry", or "stream a vehicle's state". Resolve ambiguous targets with the world-data skill.
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。
Imports a custom RoadRunner map (FBX geometry + XODR OpenDRIVE) into a CARLA source build as a drivable map — reads the map from the directory the user names, runs CARLA's Import.py to cook the level, Traffic Manager graph and (standard maps only) pedestrian navmesh, and verifies it loads on a server. Handles both standard maps and large tiled maps; large maps get no navmesh, matching upstream CARLA. Use when the user asks to "import a map into CARLA", "add a custom/RoadRunner map", "ingest an FBX+XODR map", "make import", bring in a "large/tiled map", or fix missing walkers / a missing pedestrian navmesh.
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。
Loads, reloads, and reshapes the map on a running CARLA server via the Python API — get_available_maps, load_world and reload_world (with or without resetting world settings), generate a world from an OpenDRIVE .xodr, and load/unload map layers. Use when the user asks to "load a map", "change the town", "reload the world", "list available maps", "load an xodr/opendrive map", or "load/unload map layers", and when settings (sync mode, fixed delta) must survive the switch.
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。
Directly drives a specific vehicle on a running CARLA server — raw VehicleControl (throttle/steer/brake/reverse/hand-brake), Ackermann speed control, vehicle lights, opening/closing doors, and physics-control tuning; ros-info reports the native ROS 2 command topics (vehicle_control_cmd, ackermann_control_cmd) for the hero. Targets the ego by default. Use when the user asks to "drive/steer the car", "accelerate/brake/reverse", "set a target speed", "turn on the headlights/blinkers/brake lights", "open a door", "drive the car from ROS", or "change the vehicle's mass/drag/physics". This is manual control (autopilot off) — for TM-driven traffic use 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。
Runs a single ScenarioRunner Python scenario (or a whole group) against a running CARLA server — FollowLeadingVehicle, ControlLoss, DynamicObjectCrossing, SignalizedJunctionLeftTurn and the rest of srunner/examples — in sync or async mode, with result output, recording and manual/agent control of the ego. Lists what is runnable on the checked-out branch and on the loaded map. Use when the user asks to "run a scenario", "test FollowLeadingVehicle", "list scenarios", "run a CARLA traffic scenario", or reports a scenario that is "not supported".
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。