Reduce ad‑hoc prompts with reusable, auto‑discovered AI-callable workflow files

Many engineering organizations today cope with a patchwork of ad‑hoc prompt scripts, brittle notebook snippets, and ungoverned “prompt files” scattered across repos, which leaves platform, ML infra, and developer productivity teams repeatedly rewriting the same logic and losing auditability. This problem scales: there are roughly 120,000 mid and large engineering organizations that could benefit, and even conservative uptake at $120K ACV implies a $14.4B addressable market. The operational cost is real — duplicated effort, inconsistent outputs, and security risks — but the tooling to treat AI workflows like first‑class, reusable artifacts is still immature. The product would be a Git‑native system that auto‑discovers file‑driven AI workflow definitions (think SKILL.md–style conventions), indexes metadata and JSON schemas, exposes them as LLM‑callable functions (compatible with modern function‑calling runtimes), and generates SDKs/CLI hooks plus governance and CI checks for composition and testing. It would prioritize deterministic composition, runtime validation, and a searchable enterprise catalog so teams can programmatically invoke, compose, and version workflows instead of pasting prompts. Timing favors this approach because LLM runtimes now support richer structured outputs and deterministic function calling, organizations are demanding platformized, governed primitives rather than ad‑hoc scripts, and open file‑first conventions accelerate discovery and sharing. The market score (92/100) and revenue potential (88/100) reflect a large, willing buyer base, but adoption challenges include standardizing file formats across orgs, handling cross‑repo permissions, and building the network effects of a marketplace; success will hinge on excellent Git UX, robust security controls, and seeding early integrations to create defensibility against a medium level of competition.