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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.

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

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

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• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.

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• Connecting research, writing, and editing agents
• Coordinating specialist agents through a manager
• Branching workflows based on analysis results

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

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• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

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• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.

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• Detect attempts to bypass an AI app's safety rules
• Mask personal information before processing
• Add fact-checking to generated answers

Convert language models to GGUF and reduce their size through quantization, which stores model weights at lower precision. Guides cover compression choices, local inference on CPUs and Apple Silicon, and integration with tools such as Ollama.

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• Running a language model on a CPU-only computer
• Choosing model compression for available memory
• Configuring Metal acceleration on Apple Silicon

Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

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• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.

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• Train a small model on Shakespeare using a CPU
• Prepare custom text for model training
• Further train a pretrained GPT-2 model

Configure and manage BrowserWing to create, save, and replay browser automation scripts. Use AI exploration to generate repeatable workflows, export scripts as SKILL.md files for agents, and diagnose service or execution problems.

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• Setting up BrowserWing and its AI model connection
• Generating reusable scripts through AI website exploration
• Exporting browser scripts as SKILL.md files

Set up, configure, update, and troubleshoot persistent memory for OpenClaw agents. The plugin stores conversations locally and supports automatic recall, task summaries, reusable skills derived from successful tasks, and a visual memory manager.

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• Setting up persistent memory for OpenClaw
• Updating or troubleshooting the memory plugin
• Choosing local or external memory search models

Adapt large language models to specific tasks through fine-tuning, or additional training. Prepares and validates datasets, configures LoRA or QLoRA to train a small subset of parameters, and supplies training settings, evaluation scripts, and deployment optimization guidance.

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• Preparing and validating JSONL training data
• Choosing LoRA or QLoRA for available GPU memory
• Configuring training and monitoring overfitting

Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.

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• Design an AI application that answers using internal documents
• Compare vector databases and document splitting strategies
• Improve search through keyword matching and result reranking

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

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• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Check whether AI crawlers, the bots that collect web content for AI services, can access a website. Get a crawler access table, an assessment score, and recommendations based on robots.txt, page tags, HTTP headers, and rendering requirements.

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• Checking which AI crawlers can access a website
• Reviewing unintended blocks in robots.txt
• Investigating content that requires JavaScript to display

Create logos, illustrations, product images, and other visuals from text, or edit and combine reference images. The skill guides generation through Hive, waiting for results, and delivering saved images, with costs charged to Hive credits.

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• Create a logo or illustration from a written brief
• Change the background of a product photo
• Place an existing logo on a mockup

Keep chat-ui model settings aligned with the HuggingFace router. Add descriptions for newly available models, check reasoning support, enable artifact panels based on model size, and remove settings for models no longer served.

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• Add newly released models to chat-ui settings
• Remove settings for models no longer served
• Check reasoning support and artifact eligibility

Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.

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• Identify the framework used by existing Python agent code
• Choose an executor for Claude SDK or OpenAI Agents SDK code
• Check whether a framework has native support

Test Omnigent’s Antigravity SDK integration against a live local server. Build test agents and run real conversations to check Gemini authentication, model selection, tool connections, and failures across the full execution path.

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• Test authentication and real Gemini responses
• Verify tool connections and sub-agent delegation
• Investigate model selection failures

Create an Omnigent agent directory using validated templates for configuration and instructions. Choose how the agent runs, then add web search, shell access, external tool connections, or subagents only when the task requires them.

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• Creating a minimal Omnigent agent
• Configuring an agent with web search
• Adding shell access or MCP tool connections

Look up how to configure and organize Omnigent agents. Use examples to choose an executor, the agent's runtime, arrange instructions and skills, add local tools or external services, and define specialized child agents.

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• Check agent directory layout and config.yaml settings
• Choose an executor and harness
• Add skills, custom tools, or MCP services

Review development plans with external AI tools, then revise and review them again while actionable concerns remain. Choose reviewers and a cycle limit to work toward a plan with resolved review findings.

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• Review a development plan before implementation
• Revise plans using feedback from multiple AI reviewers
• Use a local model server to review plans

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.

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