[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F6ZFEKJY8JSXBM5KG2SNZB":3,"skill-same:01M6F6ZFEKJY8JSXBM5KG2SNZB":80},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6F6ZFEKJY8JSXBM5KG2SNZB","anth-prod-checklist",null,"Execute production deployment checklist for Claude API integrations.\nUse when deploying Claude-powered features to production,\npreparing for launch, or implementing go-live validation.\nTrigger with phrases like \"anthropic production\", \"deploy claude\",\n\"claude go-live\", \"anthropic launch checklist\", \"production ready claude\".","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fanth-prod-checklist\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"anth prod checklist","checklist","read","with","trigger","io","live","production ready","ready","use","claude api","curl","integrations","Bash","api","saas","code","deployment","launch","go","2026-11-10 10:12:30",false,"---\nname: anth-prod-checklist\ndescription: |\n  Execute production deployment checklist for Claude API integrations.\n  Use when deploying Claude-powered features to production,\n  preparing for launch, or implementing go-live validation.\n  Trigger with phrases like \"anthropic production\", \"deploy claude\",\n  \"claude go-live\", \"anthropic launch checklist\", \"production ready claude\".\nallowed-tools: Read, Bash(curl:*), Grep\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\ntags: [saas, ai, anthropic]\ncompatible-with: claude-code\n---\n\n# Anthropic Production Checklist\n\n## Overview\n\nComplete checklist for deploying Claude API integrations to production with reliability, observability, and cost controls.\n\n## Pre-Launch Checklist\n\n### Authentication & Keys\n- [ ] Production API key from dedicated Workspace\n- [ ] Key stored in secret manager (not env files on servers)\n- [ ] Key rotation procedure documented and tested\n- [ ] Separate keys for each environment (dev\u002Fstaging\u002Fprod)\n\n### Error Handling\n- [ ] All 5 error types handled: `authentication_error`, `invalid_request_error`, `rate_limit_error`, `api_error`, `overloaded_error`\n- [ ] SDK `maxRetries` set (recommended: 3-5 for production)\n- [ ] Custom error logging with `request-id` captured\n- [ ] Circuit breaker for sustained API failures\n\n### Rate Limits & Cost\n- [ ] Usage tier verified at [console.anthropic.com](https:\u002F\u002Fconsole.anthropic.com\u002Fsettings\u002Flimits)\n- [ ] Application-level rate limiting implemented\n- [ ] Cost alerts configured (monthly spend caps)\n- [ ] Model selection optimized (Haiku for simple tasks, Sonnet for complex)\n- [ ] `max_tokens` set to realistic values (not inflated)\n- [ ] Prompt caching enabled for repeated system prompts\n\n### Reliability\n- [ ] Timeout configured (`timeout` parameter, recommended 60-120s)\n- [ ] Graceful degradation when API is unavailable\n- [ ] Health check endpoint tests API connectivity\n\n```python\nasync def health_check():\n    try:\n        # Use token counting as a cheap health probe (no generation cost)\n        count = client.messages.count_tokens(\n            model=\"claude-haiku-4-20250514\",\n            messages=[{\"role\": \"user\", \"content\": \"ping\"}]\n        )\n        return {\"status\": \"healthy\", \"tokens\": count.input_tokens}\n    except Exception as e:\n        return {\"status\": \"degraded\", \"error\": str(e)}\n```\n\n### Observability\n- [ ] Request\u002Fresponse logging (redact content, keep metadata)\n- [ ] Latency tracking (p50, p95, p99)\n- [ ] Token usage tracking (input + output per request)\n- [ ] Cost tracking per feature\u002Fcustomer\n- [ ] Error rate alerting (429s, 5xx, timeouts)\n\n```python\nimport logging\nimport time\n\nlogger = logging.getLogger(\"anthropic\")\n\ndef tracked_create(**kwargs):\n    start = time.monotonic()\n    try:\n        response = client.messages.create(**kwargs)\n        duration = time.monotonic() - start\n        logger.info(\n            \"claude_request\",\n            extra={\n                \"request_id\": response._request_id,\n                \"model\": response.model,\n                \"input_tokens\": response.usage.input_tokens,\n                \"output_tokens\": response.usage.output_tokens,\n                \"duration_ms\": int(duration * 1000),\n                \"stop_reason\": response.stop_reason,\n            }\n        )\n        return response\n    except Exception as e:\n        duration = time.monotonic() - start\n        logger.error(\"claude_error\", extra={\"error\": str(e), \"duration_ms\": int(duration * 1000)})\n        raise\n```\n\n### Content Safety\n- [ ] System prompts reviewed for injection resistance\n- [ ] User input validated and length-limited\n- [ ] Output scanned for sensitive data leakage\n- [ ] Content moderation for user-facing responses\n\n### Infrastructure\n- [ ] Deployment uses canary\u002Frolling strategy\n- [ ] Rollback procedure documented and tested\n- [ ] Runbook created (see `anth-incident-runbook`)\n- [ ] On-call escalation path defined\n\n## Alerting Thresholds\n\n| Metric | Warning | Critical |\n|--------|---------|----------|\n| Error rate (5xx) | > 1% | > 5% |\n| p99 latency | > 10s | > 30s |\n| 429 rate | > 5\u002Fmin | > 20\u002Fmin |\n| Daily cost | > 80% budget | > 100% budget |\n| Auth failures (401\u002F403) | > 0 | > 0 (immediate) |\n\n## Resources\n\n- [API Status](https:\u002F\u002Fstatus.anthropic.com)\n- [Pricing](https:\u002F\u002Fdocs.anthropic.com\u002Fen\u002Fdocs\u002Fabout-claude\u002Fpricing)\n- [Rate Limits](https:\u002F\u002Fdocs.anthropic.com\u002Fen\u002Fapi\u002Frate-limits)\n\n## Next Steps\n\nFor version upgrades, see `anth-upgrade-migration`.\n\n","# Anthropic Production Checklist\n\n## Overview\n\nComplete checklist for deploying Claude API integrations to production with reliability, observability, and cost controls.\n\n## Pre-Launch Checklist\n\n### Authentication & Keys\n- [ ] Production API key from dedicated Workspace\n- [ ] Key stored in secret manager (not env files on servers)\n- [ ] Key rotation procedure documented and tested\n- [ ] Separate keys for each environment (dev\u002Fstaging\u002Fprod)\n\n### Error Handling\n- [ ] All 5 error types handled: `authentication_error`, `invalid_request_error`, `rate_limit_error`, `api_error`, `overloaded_error`\n- [ ] SDK `maxRetries` set (recommended: 3-5 for production)\n- [ ] Custom error logging with `request-id` captured\n- [ ] Circuit breaker for sustained API failures\n\n### Rate Limits & Cost\n- [ ] Usage tier verified at [console.anthropic.com](https:\u002F\u002Fconsole.anthropic.com\u002Fsettings\u002Flimits)\n- [ ] Application-level rate limiting implemented\n- [ ] Cost alerts configured (monthly spend caps)\n- [ ] Model selection optimized (Haiku for simple tasks, Sonnet for complex)\n- [ ] `max_tokens` set to realistic values (not inflated)\n- [ ] Prompt caching enabled for repeated system prompts\n\n### Reliability\n- [ ] Timeout configured (`timeout` parameter, recommended 60-120s)\n- [ ] Graceful degradation when API is unavailable\n- [ ] Health check endpoint tests API connectivity\n\n```python\nasync def health_check():\n    try:\n        # Use token counting as a cheap health probe (no generation cost)\n        count = client.messages.count_tokens(\n            model=\"claude-haiku-4-20250514\",\n            messages=[{\"role\": \"user\", \"content\": \"ping\"}]\n        )\n        return {\"status\": \"healthy\", \"tokens\": count.input_tokens}\n    except Exception as e:\n        return {\"status\": \"degraded\", \"error\": str(e)}\n```\n\n### Observability\n- [ ] Request\u002Fresponse logging (redact content, keep metadata)\n- [ ] Latency tracking (p50, p95, p99)\n- [ ] Token usage tracking (input + output per request)\n- [ ] Cost tracking per feature\u002Fcustomer\n- [ ] Error rate alerting (429s, 5xx, timeouts)\n\n```python\nimport logging\nimport time\n\nlogger = logging.getLogger(\"anthropic\")\n\ndef tracked_create(**kwargs):\n    start = time.monotonic()\n    try:\n        response = client.messages.create(**kwargs)\n        duration = time.monotonic() - start\n        logger.info(\n            \"claude_request\",\n            extra={\n                \"request_id\": response._request_id,\n                \"model\": response.model,\n                \"input_tokens\": response.usage.input_tokens,\n                \"output_tokens\": response.usage.output_tokens,\n                \"duration_ms\": int(duration * 1000),\n                \"stop_reason\": response.stop_reason,\n            }\n        )\n        return response\n    except Exception as e:\n        duration = time.monotonic() - start\n        logger.error(\"claude_error\", extra={\"error\": str(e), \"duration_ms\": int(duration * 1000)})\n        raise\n```\n\n### Content Safety\n- [ ] System prompts reviewed for injection resistance\n- [ ] User input validated and length-limited\n- [ ] Output scanned for sensitive data leakage\n- [ ] Content moderation for user-facing responses\n\n### Infrastructure\n- [ ] Deployment uses canary\u002Frolling strategy\n- [ ] Rollback procedure documented and tested\n- [ ] Runbook created (see `anth-incident-runbook`)\n- [ ] On-call escalation path defined\n\n## Alerting Thresholds\n\n| Metric | Warning | Critical |\n|--------|---------|----------|\n| Error rate (5xx) | > 1% | > 5% |\n| p99 latency | > 10s | > 30s |\n| 429 rate | > 5\u002Fmin | > 20\u002Fmin |\n| Daily cost | > 80% budget | > 100% budget |\n| Auth failures (401\u002F403) | > 0 | > 0 (immediate) |\n\n## Resources\n\n- [API Status](https:\u002F\u002Fstatus.anthropic.com)\n- [Pricing](https:\u002F\u002Fdocs.anthropic.com\u002Fen\u002Fdocs\u002Fabout-claude\u002Fpricing)\n- [Rate Limits](https:\u002F\u002Fdocs.anthropic.com\u002Fen\u002Fapi\u002Frate-limits)\n\n## Next Steps\n\nFor version upgrades, see 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