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optimization-from-data-orchestrator

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Coordinate uploaded data plus a natural-language question into interpretation, clarification, cuOpt solve, and a user-facing answer.

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Source SKILL.md: https://github.com/NVIDIA/cuopt-examples/blob/HEAD/cuopt_on_nemoclaw/openclaw-skills/optimization-from-data-orchestrator/SKILL.md

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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