optimization-from-data-orchestrator
von nvidia
Coordinate uploaded data plus a natural-language question into interpretation, clarification, cuOpt solve, and a user-facing answer.
npx skills add https://github.com/nvidia/cuopt-examples --skill optimization-from-data-orchestratorOptimization 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.
optimization-intent-router— optimization family (LP/MILP/QP/routing)optimization-mode-router— only if replay/audit/export signalstabular-optimization-ingestion— table roles (interpretation only)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