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Get help setting up, configuring, and troubleshooting Paseo using current public documentation. Identify the affected host and connection method, then use relevant diagnostics to investigate provider, version, workspace, or connectivity problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Get guidance on Paseo setup and configuration
• Investigate local or remote connection problems
• Diagnose missing providers or command errors

Automate website tasks such as navigating pages, filling forms, clicking buttons, taking screenshots, and extracting data. The skill also supports web app testing, with specialized guides for Electron desktop apps, Slack, and cloud browsers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automate form entry and page navigation
• Capture screenshots and extract page data
• Explore web apps for bugs

Develop and migrate LangBot messaging adapters for Event-Based Agents, its event-driven architecture. Check conversion into shared event and message formats, implement platform APIs, and verify behavior through unit tests and real platform interactions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a messaging platform adapter to LangBot
• Migrating an existing adapter to the EBA layout
• Testing event and message conversion in both directions

Build AI agents by connecting workflow blocks in AutoGPT's visual editor. This skill covers multi-step automation, scheduled runs, webhooks that start work after external events, execution monitoring, and custom agent development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI workflow in a visual editor
• Start an agent when an external event occurs
• Run a multi-step workflow on a schedule

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Guides building AI applications that search documents before answering questions, an approach called RAG. Covers importing files and web data, creating searchable indexes, adding conversational interfaces, and evaluating whether answers match the retrieved material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a question-answering system for internal documents
• Create a chatbot that remembers conversation context
• Search information across files, web pages, and databases

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Guides reinforcement learning, where models learn from rewards, using torchforge and PyTorch. Covers reward design, custom loss functions, and distributed training so researchers can experiment with algorithms while keeping infrastructure concerns separate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom loss functions
• Scale training across multiple GPUs or nodes

Organize PyTorch training code with LightningModule and Trainer. This skill guides training, validation, logging, model checkpoint saving, early stopping when progress stalls, and configuration for running the same model across multiple GPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize existing PyTorch training code
• Add validation and metric logging
• Save the best models and stop stalled training

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed

Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a local LLM on an Apple Silicon Mac
• Trying LLM inference on a CPU-only machine
• Choosing a model format that fits available memory

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Build holaOS apps for connecting external services or managing work through a dashboard. The skill uses shared SDK building blocks for stored records, actions, and synchronization, with the dashboard and agent tools working on the same data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an external service integration
• Creating a CRM dashboard
• Building a content planning workspace

Automate an existing Chrome session through hive-browser commands. Covers page inspection, screenshot-based clicks, text entry, and result checks, including controls hidden inside nested web components and recovery steps for connection timeouts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fill in web forms through Chrome
• Click visible controls missing from page snapshots
• Check that a rich-text editor accepted input

Get help understanding and configuring Octop, a self-hosted AI assistant. The skill explains features, points to relevant documentation, and supports model switching, messaging connections, skill management, scheduled tasks, backups, and upgrades.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn about Octop and find installation instructions
• Switch AI models or configure providers
• Connect messaging services such as Discord

Research game modding methods and tools across public websites and archived pages. Recover information from dead forum links, compare reports, and produce findings with source URLs, version details, credibility notes, and clearly identified gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find earlier modding work for a game
• Recover information from dead forum links
• Check reports about a tool's supported versions

Investigate organizations, domains, media locations, and cryptocurrency flows using open-source intelligence, or OSINT. The skill helps select relevant research tools, connect findings across sources, and preserve evidence with timestamps, screenshots, file hashes, and reproducible logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Mapping an organization's publicly exposed infrastructure
• Connecting public company records and social profiles
• Investigating image locations and video evidence

Plan customer acquisition as a solo founder, from choosing tools and sales channels to assigning work to AI agents. Organize priorities by revenue stage, divide time between building and selling, and assess when hiring makes sense.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning customer acquisition as a solo founder
• Dividing marketing and sales work among AI agents
• Choosing between self-service signup and sales calls

Plan a product launch across discovery sites, social media, and communities. Select channels for the intended audience, build a waitlist, prepare launch materials, coordinate launch-day activities, and organize follow-up promotion and measurement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a Product Hunt launch
• Coordinating announcements across several channels
• Building a waitlist before release

Check an OpenClaw environment for risky settings, unsafe skills, known software vulnerabilities, and privacy exposure. It combines local checks with optional online threat lookups and also supports reviewing individual skills before installation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running an OpenClaw security health check
• Reviewing a skill before installation
• Auditing risks across installed skills