clade-prod-checklist
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".
What this skill does
# Anthropic Production Checklist ## Overview Before going live with a Claude-powered app, verify every item below. ## Authentication & Security - [ ] API key stored in secrets manager (not in code or env file on disk) - [ ] Key rotated — not the same one used during development - [ ] Server-side only — no key exposed to client/browser - [ ] Per-user rate limiting in place - [ ] Input validation: max length, content filtering - [ ] System prompt includes injection guardrails ## Output - All checklist items verified (authentication, error handling, streaming, cost, monitoring, reliability, content, performance) - Production API key configured with appropriate spending limits - Monitoring and alerting in place - Fallback behavior tested for API outages ## Error Handling - [ ] All Anthropic API calls wrapped in try/catch - [ ] `RateLimitError` (429) → backoff and retry - [ ] `OverloadedError` (529) → fallback model or queue - [ ] `AuthenticationError` (401) → alert team, don't retry - [ ] `InvalidRequestError` (400) → log and fix, don't retry - [ ] Network errors → retry with backoff - [ ] Request IDs logged for every error (for support tickets) ## Streaming - [ ] Using `client.messages.stream()` for user-facing responses - [ ] Stream errors handled (connection drops, incomplete responses) - [ ] `stop_reason` checked: `end_turn` vs `max_tokens` (incomplete) ## Cost Controls - [ ] `max_tokens` set to realistic values (not 4096 for short answers) - [ ] Correct model for each task (Haiku for simple, Sonnet for balanced) - [ ] Prompt caching enabled for repeated system prompts - [ ] Usage logging in place — tracking tokens and cost per request - [ ] Spending alerts set in Anthropic console ## Monitoring - [ ] Response latency tracked (TTFT and total) - [ ] Token usage tracked (input/output per request) - [ ] Error rates dashboarded (by error type) - [ ] Anthropic status page monitored ([status.anthropic.com](https://status.anthropic.com)) ## Reliability - [ ] SDK `maxRetries` set (default 2 is fine for most) - [ ] Timeout configured for your use case (`timeout` option) - [ ] Single client instance reused (not created per request) - [ ] Graceful degradation if Claude is down (cached responses, fallback) ## Content & Compliance - [ ] System prompt tested against edge cases and adversarial inputs - [ ] Output validated before showing to users (JSON parsing, length) - [ ] Data retention settings configured in Anthropic console - [ ] No unnecessary PII in prompts - [ ] Usage policy compliance (Anthropic's Acceptable Use Policy) ## Performance - [ ] p95 latency acceptable for your UX - [ ] Prompt caching for latency-sensitive paths - [ ] Parallel requests where possible (`Promise.all`) - [ ] Client-side streaming UI implemented ## Examples Each section above is a verifiable checklist. Work through Authentication & Security, Error Handling, Streaming, Cost Controls, Monitoring, Reliability, Content & Compliance, and Performance sections. ## Resources - [API Best Practices](https://docs.anthropic.com/en/docs/build-with-claude) - [Error Handling](https://docs.anthropic.com/en/api/errors) - [Rate Limits](https://docs.anthropic.com/en/api/rate-limits) - Acceptable Use Policy ## Next Steps See `clade-observability` for monitoring setup. ## Prerequisites - All other anthropic skills reviewed - Application feature-complete and tested locally - Production API key created (separate from dev) - Deployment platform selected ## Instructions ### Step 1: Review the patterns below Each section contains production-ready code examples. Copy and adapt them to your use case. ### Step 2: Apply to your codebase Integrate the patterns that match your requirements. Test each change individually. ### Step 3: Verify Run your test suite to confirm the integration works correctly.
Related in AI Agents
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IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
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llm-wiki
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skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.