groq-data-handling

name	groq-data-handling
description	Use when you need to keep PII out of Groq API calls, filter model responses, audit-log conversations, or track token cost and usage for a Groq integration. Implements prompt sanitization, PII redaction, response filtering, and usage tracking. Trigger with phrases like "groq data", "groq PII", "groq GDPR", "groq data retention", "groq privacy", "groq compliance".
allowed-tools	Read, Write, Edit
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","groq","compliance"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Groq Data Handling

Overview

Manage data flowing through Groq's inference API. This skill wires a privacy pipeline around the Groq SDK: sanitize prompts before they are sent, filter responses after they return, redact PII, hash-log an audit trail, and track token usage and cost. Key fact: Groq does not use API data for model training (Groq Privacy Policy).

Prerequisites

• Node.js project with the groq-sdk package installed (npm i groq-sdk).
• A Groq API key exported as GROQ_API_KEY. The SDK reads it automatically from the environment — new Groq() needs no explicit argument. Never hardcode the key; keep it in an untracked .env or your secret manager.
• Node's built-in crypto module (for the audit hash) — no install needed.

Instructions

The pipeline layers in four stages; drop simple add-ons (moderation, cost reporting) on top. Each snippet below is the skeleton — the full, copy-ready code for every stage is in references/implementation.md.

1. Sanitize input — run a PII rule table over every message before it leaves your process, flagging which categories were caught:

   function sanitizeMessages(messages: any[]): { messages: any[]; hadPII: boolean } {
     // apply PII_RULES to each message's content; return redacted copy + flag
   }
   

2. Wrap the completion call — call safeCompletion(...) instead of the raw groq.chat.completions.create, so input and response both pass the sanitizer.

3. Track usage — trackUsage(model, completion.usage, sessionId) records token counts and estimated cost per call using a per-model price table.

4. Audit — auditedCompletion(...) ties it together and logs a SHA-256 hash of the prompt (never the prompt text) so the audit trail carries no sensitive content.

For content moderation via Llama Guard and a daily cost report, see .