Build search and retrieval systems with Pinecone, a managed database for numerical representations of content. Covers data uploads, filtered similarity searches, combined semantic and keyword search, and connections to LangChain and LlamaIndex.
Run machine learning training and predictions on cloud GPUs, then publish models as APIs that other applications can call. The skill guides Python-based setup, automatic scaling, persistent storage, parallel processing, and scheduled jobs.
Control the Unreal Engine editor to build scenes, place objects, adjust lighting and materials, and frame camera shots. The skill guides inspection, incremental edits, visual checks, saving, and producing screenshots or renders.
Connect Hermes to NeuroSkill to review live focus, mood, and drowsiness indicators from a brain-sensing wearable. Compare recorded sessions, explore sleep data, label notable moments, and receive suggestions for breaks or exercises for research use.
Prepare LLM training datasets with NeMo Curator by removing duplicates, filtering low-quality content, and redacting personal information. The skill also covers image, video, and audio curation, including GPU processing and distributed workflows.
Fine-tune large language models with LoRA and QLoRA, methods that train small additions to a base model while limiting GPU memory use. Configure training, save task-specific adapters, switch between them, and merge them for deployment.
Use LLaVA, a model that understands images and text, to answer questions about pictures, write image descriptions, and build image chat applications. Includes guidance for setup, ongoing conversations, and model memory options.
Create Python workflows for generating images from text, restyling existing pictures, and filling selected regions. Using HuggingFace Diffusers, the skill explains composition controls, style adapters, generation settings, and ways to reduce memory use.
Analyze and modify refusal behavior in open-weight language models without retraining. Use OBLITERATUS to select methods suited to available hardware, compare approaches, and evaluate changes in refusal rates and response quality.
Find and run over 150 cloud AI apps through a shared command-line tool. Generate images and videos, search for information, or upload local images and audio for tasks such as upscaling and avatar generation.
Train language models on pairs of preferred and rejected answers without a separate reference model. The skill guides training setup, model-specific configurations, parameter tuning, and troubleshooting for unstable training or memory limits.
Plan workouts by muscle group or equipment, look up food nutrients, and estimate body metrics. Uses wger and USDA data alongside offline calculators for calorie needs, body fat, lifting capacity, and protein, fat, and carbohydrate targets.
Manage Docker containers, images, storage volumes, and networks with standard commands. Start multi-service applications with Docker Compose, investigate failures through logs, check disk usage, and review Dockerfiles for improvements.
Set up reinforcement learning, training guided by response rewards, for large language models. Use Megatron-LM and SGLang to prepare data, configure training and response generation, and build asynchronous or multi-turn experiments.
Implement and debug large-model training with PyTorch FSDP, which splits training state across devices. Consult documentation and code patterns for sharding choices, mixed precision, and checkpoints when building or reviewing distributed training code.
Helps you discover, authenticate, and call MCP servers from the terminal. MCP connects agents to external tools. You can inspect available tools, manage connections, make one-off calls, and generate command-line wrappers or TypeScript clients.
Explore data and develop Python code in a live Jupyter session that keeps variables between runs. Inspect objects, edit notebook cells, and optionally restart and run the whole notebook to check execution from top to bottom.
Let an AI play Pokemon through an emulator, using screenshots and game memory to guide movement, dialogue, and battles. The skill covers browser viewing, progress tracking, and saving or resuming play with a user-provided ROM.
Explore concepts learned by language models using sparse autoencoders, which separate internal activity into a small set of features. Analyze pretrained models, train custom autoencoders, assess reconstruction quality, and experiment with feature based output adjustments.
Control local language model output with patterns and grammar rules to generate structured JSON, formatted dates, and fixed classification labels. The skill also covers reusable Python functions and workflows that combine multiple generation steps.
Build document search with Chroma, a database that stores text as numerical representations of meaning. Save and update documents, filter by metadata, and connect retrieval to AI applications using LangChain or LlamaIndex.
Build an Excel workbook showing how an acquisition changes earnings per share. Organize purchase price, funding, and synergy assumptions, compare deal scenarios, calculate the synergies needed to avoid reducing earnings per share, and prepare a concise summary.
Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.
Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.
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