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概要と使いどころ
Automate Apollo.io lead generation -- search organizations, discover contacts, enrich prospect data, manage contact stages, and build targeted outreach lists -- using natural language through the Composio MCP integration.
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Automate recruiting and hiring workflows in Ashby -- manage candidates, jobs, applications, interviews, and notes through natural language commands.
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。
Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP 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。
Draft or update a GitHub PR description that explains the problem, changed behavior, and review concerns in plain language.
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。
Author Quarkdown (.qd) documents, a Markdown-superset typesetting language that compiles to HTML, PDF, or plaintext. Use when the user wants to write or edit .qd files, produce typeset output (articles, reports, slides, books, wikis, notes, static sites) beyond what plain Markdown offers, or share structured plans in a presentation-ready format. Quarkdown adds function calls (`.func {arg}`), variables, layouts, math, diagrams, and document types (plain/paged/slides/docs).
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 sandbox security policies from plain-language requirements and optional REST API documentation. Produces L4 or fine-grained L7 network policies and ordered network middleware configuration. Use for API access rules, middleware host selection, failure behavior, or built-in and operator-run middleware attachment. Trigger keywords - generate policy, create policy, update policy, change policy, sandbox policy, network policy, API policy, security policy, allow API, restrict API, network middleware, supervisor middleware.
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。
CLI tool for creating, managing, and exporting AI-generated presentations via the Banana Slides API. Use when the user asks to: (1) generate a PPT/presentation/slides from an idea, outline, or description, (2) export a project to PPTX, PDF, or images, (3) batch-generate multiple presentations, (4) manage projects, pages, materials, or templates programmatically, (5) renovate/redesign an existing PPT or PDF, (6) edit slide images with natural language.
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。
Design taste and process for anything a person will look at in a browser — landing page, dashboard, prototype, deck. The default design driver for a browsable artifact, with an anti-slop playbook for the visual language.
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。
Drive an app running on a Limrun cloud iOS simulator: launch, tap, type, read the accessibility element tree, read app logs and simulator syslog, screenshot, record video, connect the app to local services, play a video file as the camera, set the clipboard, read and write user defaults, post notifications, and run timed action chains. Use after a build (from any builder) when the user wants to see, test, or interact with their app on a simulator, or says 'show me a screenshot', 'tap', 'run the UI test', 'record a video', 'read the logs', 'connect localhost', 'reach my local server from the simulator', 'mock the camera', 'paste into the app', 'change the language', 'simulate Face ID', or 'launch on simulator'. To build the app first, use limrun-xcode-bazel (Bazel workspaces) or limrun-xcode (xcodebuild projects).
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 Scenic, the probabilistic scenario description language, for the CARLA in use — pip installing it into the same interpreter that holds the CARLA client, optionally cloning the upstream repo for the example scenarios the wheel does not ship, and verifying that the `scenic` CLI, the client and Scenic's version-keyed blueprint table all agree. Use when the user asks to "install Scenic", "set up Scenic", "download Scenic", "pip install scenic", wants to run `.scenic` files, or has a Scenic whose model import or blueprint lookup 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。
Explores a CARLA map's road network through the Map/Waypoint API — a natural-language rundown of the whole map ("give me the topology"), drawing the topology to confirm the modelled map matches the visible one, enumerating junctions to resolve phrases like "the 4-way junction in the middle", nearest-waypoint lane facts, and next/previous lane navigation. Use when the user asks about the map's layout, lanes, junctions, topology, or to locate/navigate specific map elements.
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 a new Scenic scenario (.scenic) from a natural-language description — "a car cuts in from the right lane", "a pedestrian crosses in front of the ego", "negotiate an unsignalized four-way" — by turning the request into a spec, generating the file from idioms proven on this build, and validating it by compiling and sampling before any simulator is involved. Use when the user asks to "write a Scenic scenario", "create a .scenic file", "turn this description into Scenic", "generate a scenario from text", or wants a Scenic scenario authored or repaired.
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。
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。
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
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。
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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。
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
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。
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.
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。
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
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 generating target-language dubbing with KrillinAI CLI from SRT subtitles, including TTS audio creation and optional dubbed video 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。
Use when rendering landscape videos with KrillinAI CLI, including original video plus bilingual subtitles or dubbed video plus target-language subtitles.
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。
Designs incremental migration strategies, identifies service boundaries, produces dependency maps and migration roadmaps, and generates API facade designs for aging codebases. Use when modernizing legacy systems, implementing strangler fig pattern or branch by abstraction, decomposing monoliths, upgrading frameworks or languages, or reducing technical debt without disrupting business operations.
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。
Translate content across languages while preserving brand voice and cultural nuance.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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。