Pipeline Preflight

Pre-execution audit before AI generation: required inputs, files, constraints, tools, and prior outputs present. Halt instead of running cold. Use before agent runs, RAG pipelines, or multi-step chains. Scope boundary: human/study prep → `workspace-mise-en-place`; craft routing → `craft-systems-primer`.

CatCorner22 Updated

File contents

AI pipeline pre-flight (mise en place)

#2 · Domain: Culinary arts · Category: quality-control · Difficulty: 🟢 Low

Core principle

No heat until ingredients are prepped, measured, and in place.

AI problem addressed

Pipelines run with missing context then backfill assumptions.

Implementation

Checklist before generate:

  • Required input variables present and non-empty
  • Referenced prior outputs exist in context
  • Uploaded files attached and parsed
  • System constraints loaded
  • Required tools authenticated HALT and report gaps if any fail.

Boundaries

  • Prototype as prompt scaffold (🟢) before full pipeline middleware (🟡/🔴)
  • Category router: ai-transfer-quality-control
  • Catalog: ai-transfer-ecosystem-primer
  • Runtime plugin id: mise_en_place

CatCorner22/Cursor_Skills/tree/main/skills/ai-transfer/pipeline-preflight commit 8042fd7406

Frequently asked questions

npx skillmds@latest add catcorner22/pipeline-preflight