Menu
Explain AXPA findings for technical teams, AX key users, and management.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Analyze slow AX frontends, problematic users/clients, broad inventory queries, and machine-wide impact without installing client parsers.
Build or run Autonomous Ops workflows for AX Performance Advisor, including investigation queues, evidence acquisition plans, follow-up questions, change drafts, validation planning, and readiness gates.
Generate AI learning and decision-quality artifacts such as recommendation memory, similarity search, acceptance simulation, narrative variants, anomaly explanations, and action confidence tuning.
Generate autonomous AI/USP investigation artifacts including evidence scout, investigation tree, root-cause debate, recommendation quality gate, KPI storyboard, and anonymized pattern library.
Generate guarded admin execution previews with policy gates, confirmation tokens, audit records, and rollback/validation metadata.
Generate advanced operational USP outputs including SLO burn rate, maintenance sequencing, cost of delay, release gates, retention candidates, known issue matching, and executive briefings.
| name | ai-finding-explainer |
| description | Explain AXPA findings for technical teams, AX key users, and management. |
Use scripts/ai_insights.py and read findingExplainers. Present three layers: technical cause, AX/business meaning, and management risk. Include severity, confidence, approval path, and validation evidence.