Llm Infrastructure
ListedProvides LLMs guidance and assistance for building AI and machine learning applications.
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llm-infrastructure. phrazzld/claude-config is deleted or private, so it cannot be installed from.We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.
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Publisher
phrazzld
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Live repository
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About this skill
What it does
This skill provides access to and management of Large Language Model (LLM) infrastructure. It allows agents to configure, deploy, and monitor LLMs, including managing resources like GPUs and memory. The skill enables automated scaling and optimization of LLM deployments based on demand.
When to use it
- Deploying a new LLM application: Automate the setup and configuration of necessary infrastructure for your AI agent's LLM-powered features.
- Scaling an existing LLM workload: Dynamically adjust resources (GPUs, memory) to handle increased user traffic or processing demands.
- Optimizing LLM performance: Monitor resource utilization and fine-tune configurations to improve speed and efficiency.
- Managing multiple LLMs: Easily switch between different models and versions based on specific task requirements.
Key capabilities
- LLM deployment management
- Resource allocation (GPUs, memory)
- Automated scaling
- Performance monitoring
- Model versioning
Example prompts
- "Deploy the 'llama-2-70b' model with 8 GPUs."
- "Scale up LLM resources by 50% to handle peak load."
- "Show me the current GPU utilization for all deployed models."
- “Switch to version 1.2 of the ‘gpt-3.5-turbo’ model.”
Tips & gotchas
The skill requires a pre-existing LLM deployment environment (e.g., cloud provider account with access to GPUs). Ensure your agent has appropriate permissions and credentials configured for managing infrastructure resources.