القائمة
Correlate query stats, telemetry, policy decisions, and replay memory into deterministic incident hypotheses and next-best actions.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Designs dry-run experiments across query rewrites, indexes, workload twins, and rollout gates.
Produces a board-level briefing for closed-loop autonomous database operations.
Enforces closed-loop dry-run boundaries and blocks autonomous production apply.
Scores whether enough evidence exists to recommend, defer, or escalate an autonomous operator decision.
Compares do-nothing, dry-run-change, rollback, and defer scenarios for an operating objective.
Builds an executive-grade decision evidence packet from runtime signals and confidence checks.
| name | ai-anomaly-triage |
| description | Correlate query stats, telemetry, policy decisions, and replay memory into deterministic incident hypotheses and next-best actions. |
Use this skill when an operator asks why database behavior changed, whether a deployment caused a regression, or what to investigate first during an incident.
Run:
node runtime/runTool.js ai_anomaly_triage '{"engine":"postgres","database":"analytics","deploymentId":"deploy-42","incidentWindowMinutes":45}'
The tool is offline and deterministic. It combines workload stats, telemetry-correlation output, policy-memory signals, and recent replay context. It returns an anomaly score, root-cause hypotheses, next-best actions, and explainability fields listing the exact signal families used.