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agent-ops-runbook

Generate a production-ready operations runbook for deploying AI agents. Covers pre-deployment checklists, shadow mode → supervised → autonomous rollout stages, monitoring dashboards, rollback proce...

ソース情報

リポジトリ
modbender/skill-library-mcp
ソースの最終更新活動
2026年3月6日 18:33
検出された SKILL.md の言語
英語
スター
12
フォーク
3

インストール方法

デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。

ソースファイルを確認

インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。

SKILL.md を表示中

SKILL.md
ソースの指示 · 読み取り専用プレビュー
name
Agent Ops Runbook
description
Generate a production-ready operations runbook for deploying AI agents. Covers pre-deployment checklists, shadow mode → supervised → autonomous rollout stages, monitoring dashboards, rollback proce...
# Agent Ops Runbook Generate a production-ready operations runbook for deploying AI agents. Covers pre-deployment checklists, shadow mode → supervised → autonomous rollout stages, monitoring dashboards, rollback procedures, cost management, and incident response templates. ## When to Use - Deploying an AI agent to production - Building monitoring and alerting for agent systems - Creating rollback procedures for autonomous workflows - Estimating and controlling agent operational costs ## Instructions When the user asks for an agent ops runbook or deployment plan: 1. Ask which agent function they're deploying (support, sales, document processing, etc.) 2. Ask about their risk tolerance (conservative, moderate, aggressive rollout) 3. Generate a complete runbook with: - Pre-deployment checklist specific to their function - 3-stage rollout plan with metrics and gates - Monitoring alerts (critical + warning thresholds) - Rollback procedures (3 levels: prompt, feature, full) - Cost estimates based on their expected volume - 90-day implementation timeline - Incident response template 4. Include specific metric targets: - Accuracy vs human baseline: >90% - Error rate: <2% - Cost per task benchmarks by function - Human escalation rate: 5-15% 5. Flag risks specific to their industry (compliance, PII, financial accuracy) Output format: Markdown document ready to share with engineering and ops teams.
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