Update to MUSITU Axiom

{
  "description": "Perform statistical inference, hypothesis testing, effect estimation, regression-style analysis, experiments, uncertainty quantification, and robustness checks using MUSITU Axiom.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 268
    },
    {
      "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-statistics-experiments",
  "skill_md_contents": "---\nname: musitu-statistics-experiments\ndescription: \"Perform statistical inference, hypothesis testing, effect estimation, regression-style analysis, experiments, uncertainty quantification, and robustness checks using MUSITU Axiom.\"\n---\n# MUSITU Statistics & Experiments\nRead `references/QUALITY.md`.\n\n## Workflow\n1. State the estimand or hypothesis.\n2. Check study and data assumptions.\n3. Select the exposed statistical operation.\n4. Compute effect size and uncertainty.\n5. Assess practical as well as statistical significance.\n6. Apply multiplicity correction where required.\n7. Run robustness checks.\n8. Distinguish association from causation and return decision meaning.\n"
}