How do I install the Groq skill?

Run npx skillmds@latest add lev-os/groq in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Groq?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

lev-os (@lev-os) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Groq by lev-os — Agent Skill

Name: Groq
Rating: 4.666666666666667 (3 reviews)
Author: lev-os

Groq

[WHAT] Fast LLM inference via Groq API (chat) + Ollama (embeddings) [HOW] Groq for chat completions (llama-3.3-70b-versatile), Ollama nomic-embed-text for embeddings [WHEN] Need fast inference, embedding text for RAG, chat completions [WHY] Groq provides fastest LLM inference; Ollama handles local embeddings (Groq has no embedding API) Triggers: "groq embed", "groq chat", "groq complete", "embed with groq", "fast llm"

lev-os 23cf45e 4 files · 7.2 KB Updated Sep 10, 2026

File contents

groq

Fast LLM inference via Groq API for chat, Ollama for embeddings.

Setup

Environment:

GROQ_API_KEY - Required for chat completions
Ollama running locally for embeddings (ollama serve)

Install dependencies:

cd ~/.claude/skills/groq
pip install groq requests

Pull embedding model (first time):

ollama pull nomic-embed-text

Usage

Chat Completion

# Simple chat
./scripts/chat.py "Explain quantum computing in 2 sentences"

# With system prompt
./scripts/chat.py "Write a haiku" --system "You are a poet"

# Different model
./scripts/chat.py "Hello" --model llama-3.1-8b-instant

# JSON output
./scripts/chat.py "List 3 colors as JSON array" --json

Embeddings

# Embed text (returns JSON array of floats)
./scripts/embed.sh "Hello world"

# Embed from stdin
echo "Some text to embed" | ./scripts/embed.sh

# Python direct
./scripts/embed.py "Hello world"

Models

Chat Models (Groq)

Model	Context	Speed	Use Case
`llama-3.3-70b-versatile`	128k	Fast	Default, general purpose
`llama-3.1-8b-instant`	128k	Fastest	Simple tasks
`llama3-70b-8192`	8k	Fast	Legacy
`gemma2-9b-it`	8k	Fast	Instruction following

Embedding Model (Ollama)

Model	Dimensions	Notes
`nomic-embed-text`	768	Local, fast, good quality

Output Format

Chat

Plain text response to stdout. Errors to stderr.

Embed

JSON array of floats:

[0.123, -0.456, 0.789, ...]

When to Use

Scenario	Command
Quick question	`./scripts/chat.py "What is X?"`
Code generation	`./scripts/chat.py "Write Python for Y"`
Embed for RAG	`./scripts/embed.sh "document text"`
Batch embed	`cat docs.txt \| while read line; do ./scripts/embed.sh "$line"; done`

Error Handling

Missing GROQ_API_KEY: Chat fails with clear error
Ollama not running: Embed falls back to error message
Rate limits: Groq has generous limits but will return 429 if exceeded

Related Skills

Skill	Use When
oracle	Need GPT-5, Claude, multi-model comparison
lev-find	Unified search with embeddings already indexed
brave-search	Web search, not embeddings

---name: groqdescription: [WHAT] Fast LLM inference via Groq API (chat) + Ollama (embeddings) [HOW] Groq for chat completions (llama-3.3-70b-versatile), Ollama nomic-embed-text for embeddings [WHEN] Need fast inference, embedding text for RAG, chat completions [WHY] Groq provides fastest LLM inference; Ollama handles local embeddings (Groq has no embedding API) Triggers: "groq embed", "groq chat", "groq complete", "embed with groq", "fast llm"---# groqFast LLM inference via Groq API for chat, Ollama for embeddings.## Setup**Environment:**- `GROQ_API_KEY` - Required for chat completions- Ollama running locally for embeddings (`ollama serve`)**Install dependencies:**```bashcd ~/.claude/skills/groqpip install groq requests```**Pull embedding model (first time):**```bashollama pull nomic-embed-text```## Usage### Chat Completion```bash# Simple chat./scripts/chat.py "Explain quantum computing in 2 sentences"# With system prompt./scripts/chat.py "Write a haiku" --system "You are a poet"# Different model./scripts/chat.py "Hello" --model llama-3.1-8b-instant# JSON output./scripts/chat.py "List 3 colors as JSON array" --json```### Embeddings```bash# Embed text (returns JSON array of floats)./scripts/embed.sh "Hello world"# Embed from stdinecho "Some text to embed" | ./scripts/embed.sh# Python direct./scripts/embed.py "Hello world"```## Models### Chat Models (Groq)| Model | Context | Speed | Use Case ||-------|---------|-------|----------|| `llama-3.3-70b-versatile` | 128k | Fast | Default, general purpose || `llama-3.1-8b-instant` | 128k | Fastest | Simple tasks || `llama3-70b-8192` | 8k | Fast | Legacy || `gemma2-9b-it` | 8k | Fast | Instruction following |### Embedding Model (Ollama)| Model | Dimensions | Notes ||-------|------------|-------|| `nomic-embed-text` | 768 | Local, fast, good quality |## Output Format### ChatPlain text response to stdout. Errors to stderr.### EmbedJSON array of floats:```json[0.123, -0.456, 0.789, ...]```## When to Use| Scenario | Command ||----------|---------|| Quick question | `./scripts/chat.py "What is X?"` || Code generation | `./scripts/chat.py "Write Python for Y"` || Embed for RAG | `./scripts/embed.sh "document text"` || Batch embed | `cat docs.txt \| while read line; do ./scripts/embed.sh "$line"; done` |## Error Handling- Missing `GROQ_API_KEY`: Chat fails with clear error- Ollama not running: Embed falls back to error message- Rate limits: Groq has generous limits but will return 429 if exceeded## Related Skills| Skill | Use When ||-------|----------|| **oracle** | Need GPT-5, Claude, multi-model comparison || **lev-find** | Unified search with embeddings already indexed || **brave-search** | Web search, not embeddings |

Frequently asked questions