Update to MUSITU Axiom

{
  "description": "Estimate TAM, SAM, SOM and adjacent opportunity ranges using evidence-backed top-down, bottom-up, and triangulated approaches.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 250
    },
    {
      "relative_path": "references/COMMERCIAL.md",
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    },
    {
      "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",
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    },
    {
      "relative_path": "scripts/scenario_grid.py",
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    }
  ],
  "name": "musitu-market-sizing",
  "skill_md_contents": "---\nname: musitu-market-sizing\ndescription: \"Estimate TAM, SAM, SOM and adjacent opportunity ranges using evidence-backed top-down, bottom-up, and triangulated approaches.\"\n---\n# MUSITU Market Sizing\nRead `references/QUALITY.md` and classify each input as evidence or assumption.\n\n## Workflow\n1. Define market boundary, geography and time horizon.\n2. Inventory observable evidence and assumptions.\n3. Build independent top-down and bottom-up estimates.\n4. Triangulate discrepancies rather than averaging blindly.\n5. Run sensitivity on adoption, pricing and population assumptions.\n6. Avoid false precision.\n7. State which new evidence would reduce uncertainty most.\n8. Return a range, scenarios and decision implication.\n"
}