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Get Started Free →Execute Groq production deployment checklist and go-live procedures. Use when deploying Groq integrations to production, preparing for launch, or implementing go-live procedures. Trigger with phrases like "groq production", "deploy groq", "groq go-live", "groq launch checklist".
.claude/skills/jeremylongshore-groq-prod-checklist/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 58% | 0% |
Complete pre-launch checklist for deploying Groq-powered applications to production. Covers API key security, model selection, rate limit planning, fallback strategies, and monitoring setup. Work top-to-bottom: each section is a gate that must be green before the go-live verification runs.
Deep code (fallback function, health-check endpoint, go-live script) lives in references/ so this file stays scannable — drill in when you reach that step.
Read the target app's Groq integration and config, then walk each gate below. Tick every box; an unchecked item is a launch blocker.
.env files)gsk_ pattern in codellama-3.3-70b-versatile)llama-3.1-8b-instant)max_tokens set to actual expected output size (not context max)retry-after header implementedp-queue or similar)Groq.APIError, Groq.APIConnectionError)completionWithFallback pattern in references/implementation.md/api/health (or /healthz) probes Groq with a 1-token request and returns 503 when degraded — full route in references/implementation.mdgroq-incident-runbook)Run the pre-flight curl script against production — status, key, health endpoint, and rate-limit headroom must all pass. Full script and pass/fail table in references/go-live.md.
Working through this skill produces a go / no-go launch decision:
Any unchecked security or auth item (Sections 1, 2) is a hard blocker; unchecked monitoring or spending items (Sections 6, 7) are P3 blockers that may launch with a tracked follow-up.
Wire these alerts before go-live so production failures page the right severity:
| Alert | Condition | Severity | |-------|-----------|----------| | API errors spike | 5xx rate > 5/min | P1 | | Latency degraded | p95 > 1000ms | P2 | | Rate limited | 429 count > 5/min | P2 | | Auth failure | Any 401 error | P1 | | Spending near cap | >90% of monthly budget | P3 |
Minimal fallback skeleton — try the primary model, fall back to the fast model on 429/5xx:
typescripttry { return await groq.chat.completions.create({ model: "llama-3.3-70b-versatile", messages, timeout: 15_000 }); } catch (err: any) { if (err.status === 429 || err.status >= 500) { return await groq.chat.completions.create({ model: "llama-3.1-8b-instant", messages, timeout: 10_000 }); } throw err; }
Once launched, keep the integration current: schedule model-deprecation reviews against the Groq deprecations page, and for version upgrades follow the groq-upgrade-migration skill. If an incident fires an alert above, escalate through the groq-incident-runbook.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 19,550 | 9,951 | -49% | 1 | 1 | 0% | 2,277 | 3,203 | +41% | 0 | 0 | — |
case-01 | fail→fail | 43,792 | 36,250 | -17% | 1 | 1 | 0% | 7,272 | 6,387 | -12% | 0 | 0 | — |
case-02 | fail→pass | 55,328 | 27,620 | -50% | 1 | 1 | 0% | 7,483 | 5,883 | -21% | 0 | 0 | — |
case-03 | fail→fail | 35,449 | 30,111 | -15% | 1 | 1 | 0% | 5,085 | 6,413 | +26% | 0 | 0 | — |
case-04 | fail→pass | 17,300 | 22,147 | +28% | 1 | 1 | 0% | 3,070 | 4,081 | +33% | 0 | 0 | — |
case-06 | fail→pass | 25,829 | 20,063 | -22% | 1 | 1 | 0% | 3,676 | 4,352 | +18% | 0 | 0 | — |
case-07 | pass→pass | 24,379 | 16,449 | -33% | 1 | 1 | 0% | 3,144 | 4,690 | +49% | 0 | 0 | — |
case-08 | fail→pass | 24,009 | 18,769 | -22% | 1 | 1 | 0% | 3,000 | 4,082 | +36% | 0 | 0 | — |
case-09 | fail→pass | 17,074 | 16,597 | -3% | 1 | 1 | 0% | 3,052 | 4,820 | +58% | 0 | 0 | — |
case-10 | fail→pass | 23,472 | 14,667 | -38% | 1 | 1 | 0% | 2,996 | 4,202 | +40% | 0 | 0 | — |
case-11 | fail→pass | 18,435 | 12,988 | -30% | 1 | 1 | 0% | 2,211 | 2,989 | +35% | 0 | 0 | — |
case-12 | fail→pass | 19,072 | 8,447 | -56% | 1 | 1 | 0% | 2,426 | 2,152 | -11% | 0 | 0 | — |
case-13 | fail→pass | 18,981 | 8,915 | -53% | 1 | 1 | 0% | 1,884 | 2,244 | +19% | 0 | 0 | — |
case-14 | fail→pass | 12,709 | 5,480 | -57% | 1 | 1 | 0% | 2,133 | 2,440 | +14% | 0 | 0 | — |
case-15 | fail→pass | 17,877 | 8,040 | -55% | 1 | 1 | 0% | 1,955 | 2,071 | +6% | 0 | 0 | — |
case-16 | pass→pass | 12,064 | 8,364 | -31% | 1 | 1 | 0% | 1,514 | 3,346 | +121% | 0 | 0 | — |
case-17 | fail→pass | 15,084 | 2,836 | -81% | 1 | 1 | 0% | 1,398 | 2,078 | +49% | 0 | 0 | — |
case-18 | pass→pass | 7,614 | 11,225 | +47% | 1 | 1 | 0% | 1,328 | 2,469 | +86% | 0 | 0 | — |
case-19 | pass→pass | 21,973 | 13,404 | -39% | 1 | 1 | 0% | 2,677 | 3,774 | +41% | 0 | 0 | — |
case-20 | pass→pass | 16,828 | 21,956 | +30% | 1 | 1 | 0% | 3,488 | 5,136 | +47% | 0 | 0 | — |
case-21 | pass→pass | 25,063 | 29,287 | +17% | 1 | 1 | 0% | 3,583 | 5,220 | +46% | 0 | 0 | — |
case-22 | pass→pass | 25,657 | 29,227 | +14% | 1 | 1 | 0% | 3,246 | 5,415 | +67% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +55 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.