optimization-from-data-orchestrator

# Optimization From Data Orchestrator Top-level coordinator when a user provides tabular data and wants a constructive plan (schedule, assign, allocate, route — any wording). **NemoClaw:** read `cuopt-sandbox/references/activation.md` for skill order and cuOpt-before-heuristic rules. ## When to use **Both** must hold: - tabular data provided or expected (CSV, etc.) - user wants a **plan from that data** (any phrasing; minimize/optimal not required) Skip for **analytics-only** requests (summarize, chart, filter), fully pre-specified math outside this flow, or explicit replayable/auditable path. ## Sequence **Step 0 (NemoClaw — do not skip):** See `cuopt-sandbox` — probe → env → smoke. No schedule/heuristic output before smoke passes. 1. **`optimization-intent-router`** — optimization family (LP/MILP/QP/routing) 2. **`optimization-mode-router`** — only if replay/audit/export signals 3. **`tabular-optimization-ingestion`** — table roles (interpretation only) 4. **`cuopt-model-mapper`** — clarify if needed, map to cuOpt, solve Handoffs after step 4: - LP / MILP / QP → `numerical-optimization-formulation` → `cuopt-numerical-optimization-api-python` - Routing → `routing-formulation` → `cuopt-routing-api-python` ## Guardrails - First solver that emits assignments/schedules must be **cuOpt** after step 0 - Ingestion steps do not authorize heuristic or greedy stand-ins - Do not skip intent classification; do not use cuOpt for pure analytics - One focused clarification beats a long questionnaire