togetherai-skills
NewAgent Skills for Together AI platform — inference, training, embeddings, audio, video, images, function calling, and infrastructure. Covers serverless chat completions, image/video generation, fine-tuning, batch inference, evaluations, sandboxes, dedicated endpoints, and GPU clusters.
Summary
This skill provides access to Together AI's platform for AI inference, training, and infrastructure, enabling developers to generate text, images, audio, and video, fine-tune models, run batch inference, and manage dedicated endpoints and GPU clusters directly from Claude Code.
Install & Usage
mkdir -p .claude/skillsAdd the configuration to .claude/skills/togetherai-skills.md
/togetherai-skillsUse Cases
Usage Examples
/togetherai-skills generate a chat completion with model 'mistralai/Mixtral-8x7B-Instruct-v0.1' and prompt 'Explain quantum computing in simple terms.'
/togetherai-skills create an image from prompt 'A serene mountain landscape at sunset' using model 'stabilityai/stable-diffusion-xl-base-1.0'
/togetherai-skills list available models for text generation
Security Audits
Frequently Asked Questions
What is togetherai-skills?
This skill provides access to Together AI's platform for AI inference, training, and infrastructure, enabling developers to generate text, images, audio, and video, fine-tune models, run batch inference, and manage dedicated endpoints and GPU clusters directly from Claude Code.
How to install togetherai-skills?
To install togetherai-skills: create the skills directory (mkdir -p .claude/skills), then add the config to .claude/skills/togetherai-skills.md. Finally, /togetherai-skills in Claude Code.
What is togetherai-skills best for?
togetherai-skills is a skill categorized under Development. It is designed for: agent. Created by Together AI.
What can I use togetherai-skills for?
togetherai-skills is useful for: Generate serverless chat completions using models like Llama or Mistral for building conversational agents.; Create images or videos from text prompts using Together AI's generation APIs.; Fine-tune a custom model on your dataset for domain-specific tasks.; Run batch inference on large datasets for offline processing.; Evaluate model performance using built-in evaluation tools.; Provision and manage dedicated endpoints or GPU clusters for production workloads..