groq-data-handlingUse 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 complia…
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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 jeremy@intentsolutions.io tags:
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).
groq-sdk package installed (npm i groq-sdk).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.crypto module (for the audit hash) — no install needed.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.
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
}
Wrap the completion call — call safeCompletion(...) instead of the raw
groq.chat.completions.create, so input and response both pass the sanitizer.
Track usage — trackUsage(model, completion.usage, sessionId) records
token counts and estimated cost per call using a per-model price table.
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 references/examples.md.
[EMAIL], [PHONE], [SSN],
[CARD], [IP] placeholders swapped in for detected PII, plus a hadPII
boolean and a list of redacted categories.type: "groq_usage") with model,
token counts, and estimatedCostUsd.type: "groq_audit") carrying a
prompt hash, piiDetected, responseFiltered, and the usage record.totalCost, totalTokens,
totalCalls, and a per-model breakdown (see the sample in
references/examples.md).| Issue | Cause | Solution |
|---|---|---|
| PII leaks in response | Model echoes sensitive input | Apply response filtering on all completions |
| Cost spike | 70B model for all requests | Route simple tasks to 8B |
| Missing usage data | Streaming mode | Use non-streaming for tracked requests, or estimate |
| Audit gaps | Not all code paths use wrapper | Lint rule: ban direct groq.chat.completions.create |
GROQ_API_KEY not set | Key missing from environment | Export the key before running; the SDK throws on an unauthenticated call |
Minimal end-to-end use once the helpers are in place:
const { content, audit } = await auditedCompletion(sessionId, messages);
// content is PII-filtered; audit is a hash-only record safe to persist
For enterprise access controls, see the groq-enterprise-rbac skill.