optimization-from-data-orchestrator
bởi nvidia
Phối hợp dữ liệu đã tải lên với câu hỏi ngôn ngữ tự nhiên để thực hiện diễn giải, làm rõ, giải quyết cuOpt và đưa ra câu trả lời cho người dùng.
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