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Get Started Free →Production readiness checklist for Claude-powered applications — Use when working with prod-checklist patterns. error handling, monitoring, fallbacks, cost controls, and security. Trigger with "anthropic production", "claude production ready", "anthropic launch checklist", "go live with claude".
.claude/skills/jeremylongshore-clade-prod-checklist/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 24 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 27% | 0% |
Before going live with a Claude-powered app, verify every item below.
RateLimitError (429) → backoff and retryOverloadedError (529) → fallback model or queueAuthenticationError (401) → alert team, don't retryInvalidRequestError (400) → log and fix, don't retryclient.messages.stream() for user-facing responsesstop_reason checked: end_turn vs max_tokens (incomplete)max_tokens set to realistic values (not 4096 for short answers)maxRetries set (default 2 is fine for most)timeout option)Promise.all)Each section above is a verifiable checklist. Work through Authentication & Security, Error Handling, Streaming, Cost Controls, Monitoring, Reliability, Content & Compliance, and Performance sections.
See clade-observability for monitoring setup.
Each section contains production-ready code examples. Copy and adapt them to your use case.
Integrate the patterns that match your requirements. Test each change individually.
Run your test suite to confirm the integration works correctly.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 22,837 | 17,214 | -25% | 1 | 1 | 0% | 3,713 | 4,093 | +10% | 0 | 0 | — |
case-02 | pass→pass | 14,883 | 23,725 | +59% | 1 | 1 | 0% | 2,634 | 3,333 | +27% | 0 | 0 | — |
case-03 | pass→pass | 15,210 | 10,718 | -30% | 1 | 1 | 0% | 2,956 | 3,054 | +3% | 0 | 0 | — |
case-04 | pass→pass | 10,948 | 7,405 | -32% | 1 | 1 | 0% | 1,953 | 2,215 | +13% | 0 | 0 | — |
case-05 | pass→pass | 12,881 | 9,498 | -26% | 1 | 1 | 0% | 2,264 | 2,635 | +16% | 0 | 0 | — |
case-06 | pass→pass | 16,341 | 17,876 | +9% | 1 | 1 | 0% | 2,840 | 4,247 | +50% | 0 | 0 | — |
case-16 | pass→pass | 9,731 | 6,506 | -33% | 1 | 1 | 0% | 1,505 | 2,008 | +33% | 0 | 0 | — |
case-07 | pass→pass | 9,659 | 5,714 | -41% | 1 | 1 | 0% | 1,636 | 2,030 | +24% | 0 | 0 | — |
case-08 | pass→pass | 13,385 | 15,753 | +18% | 1 | 1 | 0% | 2,181 | 3,639 | +67% | 0 | 0 | — |
case-09 | pass→pass | 10,221 | 7,171 | -30% | 1 | 1 | 0% | 1,678 | 2,101 | +25% | 0 | 0 | — |
case-10 | fail→pass | 8,876 | 4,400 | -50% | 1 | 1 | 0% | 1,584 | 1,571 | -1% | 0 | 0 | — |
case-11 | pass→pass | 7,323 | 5,119 | -30% | 1 | 1 | 0% | 1,175 | 1,767 | +50% | 0 | 0 | — |
case-22 | pass→pass | 9,350 | 7,694 | -18% | 1 | 1 | 0% | 1,618 | 2,367 | +46% | 0 | 0 | — |
case-12 | pass→pass | 12,351 | 4,376 | -65% | 1 | 1 | 0% | 1,904 | 1,620 | -15% | 0 | 0 | — |
case-13 | pass→pass | 11,213 | 7,450 | -34% | 1 | 1 | 0% | 1,838 | 2,116 | +15% | 0 | 0 | — |
case-14 | pass→pass | 8,932 | 7,348 | -18% | 1 | 1 | 0% | 1,516 | 2,001 | +32% | 0 | 0 | — |
case-15 | pass→pass | 12,159 | 4,750 | -61% | 1 | 1 | 0% | 1,857 | 1,689 | -9% | 0 | 0 | — |
case-17 | fail→pass | 9,350 | 2,109 | -77% | 1 | 1 | 0% | 1,456 | 1,212 | -17% | 0 | 0 | — |
case-18 | pass→pass | 15,866 | 18,071 | +14% | 1 | 1 | 0% | 2,458 | 3,848 | +57% | 0 | 0 | — |
case-19 | pass→pass | 11,167 | 5,001 | -55% | 1 | 1 | 0% | 1,718 | 1,770 | +3% | 0 | 0 | — |
case-20 | fail→pass | 14,254 | 12,610 | -12% | 1 | 1 | 0% | 2,494 | 3,057 | +23% | 0 | 0 | — |
case-21 | pass→pass | 16,106 | 12,512 | -22% | 1 | 1 | 0% | 3,229 | 3,608 | +12% | 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 +18 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.