hatch3r-ai-feature

# AI Feature Workflow (Eval-Driven) ## Quick Start **Applies when:** the project ships an LLM/AI feature. On a project with no AI feature, skip — do not scaffold AI machinery pre-emptively (per `rules/hatch3r-right-sizing.md`). Run this skill before shipping any LLM-driven feature. It defines the canonical eval-driven loop (write eval, write prompt, measure, iterate) and the production-readiness gates. Skipping any of the 9 steps = the feature is not done. This skill is the implementation counterpart to `rules/hatch3r-ai-evals.md` (backend governance) and `rules/hatch3r-ai-ux-patterns.md` (UI governance). The rules define the bar; this skill defines the route to clearing the bar. ## Step 0 — Detect Ambiguity (P8 B1) Before any work, scan the invocation for unresolved questions in scope, intent, acceptance criteria, target environment, or irreversibility. If any are found, ask the user via the platform-native question tool per `agents/shared/user-question-protocol.md`. Do not proceed under silent assumption. Default path, not an exception. Triggers for THIS skill: task class (classification vs open-ended vs RAG vs agentic), model pin (Sonnet vs Opus vs Haiku), eval threshold values, budget per request (cost cap), and fallback policy (graceful degrade vs hard fail). ## Fan-out Discipline (P8 B2) Fan-out scales with task size; token cost never justifies serializing independent work (`rules/hatch3r-fan-out-discipline.md` P8 B2; `agents/shared/efficiency-patterns.md`). Emi...