Groq API Inference

What This Skill Does

The Groq API Inference skill provides a structured framework for interacting with Groq's high-speed LPU infrastructure. It acts as a specialized agent capability that manages the lifecycle of LLM requests—ranging from simple text generation to complex tool-calling and audio transcription. By enforcing rigorous patterns for credential handling, model routing, and error management, it ensures that your AI interactions are performant, reliable, and production-ready. The skill automates the complexity of selecting the optimal model for specific tasks, managing exponential backoff for rate limits, and validating JSON output to prevent downstream pipeline failures.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/ivangdavila/groq-api After installation, initialize your workspace by following the instructions in the setup.md file located in the generated ~/groq-api/ directory to verify your API credentials and set your default workflow preferences.

Use Cases

• Production Chatbots: Building low-latency customer support or assistant agents that require sub-second responses.
• Data Extraction: Using structured outputs to transform messy, unstructured text into validated JSON schemas for database injection.
• Speech-to-Text Pipelines: Leveraging specialized Whisper-based inference to transcribe audio files with high accuracy and low latency.
• A/B Testing Prompts: Rapidly iterating on system instructions or model parameters while logging performance and output quality in the experiments/ folder.

Example Prompts

1. "Check the current available models on Groq and update my memory-template.md with the latest Llama 3 variant for our chat workflow."
2. "Draft a retry logic script for our current inference loop that handles 429 rate limit errors with exponential backoff."
3. "Transcribe the uploaded audio file meeting.mp3 using the Groq Whisper endpoint and save the result as a sanitized JSON file in ~/groq-api/logs/."

Tips & Limitations

• Always verify models: Groq frequently updates their fleet; always use the /models endpoint to fetch the current list instead of hardcoding identifiers.
• Backoff is mandatory: If you encounter a 429 status code, ensure your implementation pauses before retrying. Ignoring this will lead to persistent blocking.
• Validation first: Never trust raw output. Always run your result through a schema validator before passing it to code execution or external APIs.
• Security: While the skill manages requests, never commit your GROQ_API_KEY to any repository. Keep sensitive information local and rely on environment variables.