Update to Aivana CIO Decision Twin

{
  "name": "ai-portfolio-governance",
  "description": "Govern AI use cases as a portfolio with business owner, data owner, model risk tier, evaluation baseline, human review and rollback path. Use when the user needs ai portfolio governance for CIO decision support.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 139
    }
  ],
  "skill_md_contents": "---\nname: ai-portfolio-governance\ndescription: Govern AI use cases as a portfolio with business owner, data owner, model risk tier, evaluation baseline, human review and rollback path. Use when the user needs ai portfolio governance for CIO decision support.\n---\n\n# AI Portfolio Governance\n\n## Mission\n\nMove AI initiatives from scattered experiments to governed portfolio decisions.\n\n## Inputs\n\nAccept AI use-case lists, model notes, data-risk context, privacy findings, automation plans, value hypotheses and approval requests.\n\n## Workflow\n\n1. Identify AI use-case and automation signals.\n2. Map owner, data owner, risk tier, evaluation and control gaps.\n3. Separate pilot, production and prohibited/blocked states.\n4. Draft portfolio controls and approval gate.\n5. Recommend next governance action.\n\n## Output Format\n\n- Executive Summary\n- Governance Level\n- AI Signals\n- Portfolio Controls\n- Approval Gate\n- Recommended Action\n- Evidence & Assumptions\n\n## Guardrails\n\nDo not claim AI approval. Human governance remains required.\n"
}