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Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

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.

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

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

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

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

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• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

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.

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

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.

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

Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.

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• Running a language model on a CPU-only computer
• Setting up local chat on an Apple Silicon Mac
• Choosing model quantization to fit available memory

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

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• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.

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• Converting an existing PyTorch training loop
• Adding validation, logs, and model checkpoints
• Configuring training across multiple GPUs

Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.

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• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Build and operate AutoGPT agents that carry out multi-step workflows. Covers a visual editor for connecting processing blocks, scheduled and webhook triggers, deployment, execution monitoring, and benchmarks for evaluating agent performance.

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• Build an AI workflow in a visual editor
• Run agents on a schedule or external event
• Add custom agent abilities with Forge

Present clear choices when a task needs a human decision, then pause execution until the user replies. This reusable interaction pattern supports filing decisions, priority selection, and confirmation before destructive actions, with an option to decline.

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• Clarifying requests with several possible meanings
• Confirming before bulk deletion or overwriting
• Choosing where to file meeting notes

Finish a NanoClaw v1-to-v2 migration after the migration script completes. The skill checks real message routing, assigns owner access, coordinates legacy memory migration, reviews container settings, and helps decide how to carry over customizations.

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• Finish follow-up work after the v2 migration script
• Review owner permissions and sender access
• Migrate legacy memory and check container settings

Adapt NanoClaw by adding messaging channels, connecting services, changing response instructions, or adjusting message routing. The skill clarifies the request, selects dedicated skills where available, applies changes, and explains how to verify them.

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• Adding Telegram or Slack as an input channel
• Connecting an external service to a specific agent group
• Changing an agent group's persona or response style

Move an existing OpenClaw setup to NanoClaw v2 through a guided conversation. Review identity, memory, credentials, groups, and scheduled tasks, then decide what to transfer and preview changes before applying them.

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• Moving an existing OpenClaw setup to NanoClaw v2
• Transferring agent personality and stored memories
• Sorting credentials into their required storage locations

Add Discord bot support to NanoClaw for conversations in server channels or direct messages. The skill installs and tests the integration, guides bot setup, checks credentials, and finds the owner's DM channel for subsequent agent connection.

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• Preparing to chat with a NanoClaw agent on Discord
• Setting up a bot for direct messages
• Checking Discord bot credentials and message settings

Set up the first NanoClaw agent on a configured messaging channel. The skill identifies the operator and DM destination, selects or creates an agent group, and sends a welcome message to verify delivery through the service.

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• Connecting the first NanoClaw agent to a DM
• Linking an existing agent group to a conversation
• Creating an agent with a chosen display name

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Develop applications with the Claude API using examples from Anthropic's official cookbooks. Explore document summarization, image analysis, document retrieval, external tool calls, and prompt caching through practical Python code and implementation guidance.

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• Learning basic Claude API integration
• Building an assistant that searches documents
• Extracting text and information from images

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

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• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Operate Screenpipe from the terminal to set up continuous recording, check capture health, and search local history. Manage Pipes, scheduled AI automations, and configure and test connections to external services.

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• Set up continuous screen and audio recording
• Diagnose recording and storage issues
• Search recorded screen and audio history

Use Screenpipe’s local REST API, an interface for programmatic requests, to search screen and meeting history, review app activity and active time, retrieve synced-device records, export recordings, and rerun audio transcription.

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• Reviewing daily app use and active screen time
• Finding decisions in recorded meetings
• Searching history from a synced device

Search Screenpipe recordings, screen text, audio transcripts, and synced-device history through its local API. Use the evidence to review activity, find meeting details, and inspect saved workflows, including recorded steps that can inform automation.

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• Review activity from a specific day or time range
• Find recorded meeting remarks and screen text
• Search work history on another synced device

Create and manage scheduled AI automations in screenpipe from the command line. Enable or disable jobs, test runs, inspect logs, and configure service connections such as Telegram and Slack, including connection status checks.

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• Creating scheduled AI automations
• Enabling or disabling existing automations
• Investigating failures through execution logs