Update to MUSITU Axiom

{
  "description": "Solve constrained optimization, resource allocation, capital allocation, scheduling, and decision-science problems with MUSITU Axiom.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 249
    },
    {
      "relative_path": "references/COMMERCIAL.md",
      "size_in_bytes": 751
    },
    {
      "relative_path": "references/PROOF.md",
      "size_in_bytes": 537
    },
    {
      "relative_path": "references/QUALITY.md",
      "size_in_bytes": 2262
    },
    {
      "relative_path": "references/ROUTING.md",
      "size_in_bytes": 504
    },
    {
      "relative_path": "scripts/proof_envelope.py",
      "size_in_bytes": 1023
    },
    {
      "relative_path": "scripts/scenario_grid.py",
      "size_in_bytes": 691
    }
  ],
  "name": "musitu-optimization",
  "skill_md_contents": "---\nname: musitu-optimization\ndescription: \"Solve constrained optimization, resource allocation, capital allocation, scheduling, and decision-science problems with MUSITU Axiom.\"\n---\n# MUSITU Optimization\nRead `references/QUALITY.md`.\n\n## Workflow\n1. Formalize objective, variables, bounds, constraints and units.\n2. Check feasibility before solving.\n3. Execute the appropriate exposed optimization capability.\n4. Identify binding constraints.\n5. Run sensitivity on scarce resources and limits.\n6. Compare with a baseline or current policy.\n7. Identify near-optimal alternatives and instability.\n8. Return an implementable decision with feasibility status.\n"
}