shipkit-prompt-audit

# shipkit-prompt-audit - LLM Prompt Architecture Audit **Purpose**: Find structural problems in how your app talks to LLMs — monolithic prompts, missing fallbacks, sequential bottlenecks, unvalidated outputs, unsafe inputs. **What this is NOT**: Not a prompt text quality checker. Not "make this prompt better." This audits the **engineering architecture** around prompts — how they're decomposed, chained, validated, and recovered from failure. --- ## When to Invoke - `/shipkit-prompt-audit` — audit all LLM integrations - `/shipkit-prompt-audit src/ai/` — focus on specific directory - "Audit my prompts", "Check prompt architecture", "LLM pipeline review" - "Are my AI calls structured well?" - "Check my prompts for anti-patterns" **Workflow position**: - After implementing AI features, before shipping - When AI features feel slow or unreliable - During architecture review of LLM-heavy applications - When scaling from prototype to production AI --- ## Prerequisites **Required**: - Project has LLM integrations (API calls to OpenAI, Anthropic, Gemini, etc.) **Recommended**: - `.shipkit/stack.json` — Knows which AI SDKs are in use - `.shipkit/architecture.json` — Knows pipeline design intent **If no LLM integrations found**: Report cleanly and exit. Don't fabricate findings. --- ## Process ### Completion Tracking After discovering integration points (Step 1), create tasks: - `TaskCreate`: "Map pipeline topology" - `TaskCreate`: "Audit all 10 dimensions (PA-DEC through ...