본문으로 건너뛰기
cccskills
무료GitHub에 공개

monte-carlo-proactive-monitoring

Guide users from coverage analysis to monitor creation. USE WHEN user asks what should I monitor, where are my gaps, improve coverage, or wants a systematic approach to monitoring across their data estate.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Monte Carlo Proactive Monitoring Workflow

This workflow guides users through improving their monitoring coverage by sequencing existing Monte Carlo skills. It does not contain coverage analysis or monitor creation logic itself — each step loads the relevant skill's SKILL.md which has the actual instructions.

When to activate this workflow

Activate when:

  • Context detection routes here (coverage intent + data project detected)
  • User invokes /mc-proactive-monitoring
  • User asks "what should I monitor?", "where are my gaps?", "improve coverage"
  • User wants a systematic approach to monitoring — not just creating one specific monitor

When NOT to activate this workflow

  • User already knows exactly what monitor to create (e.g., "create a freshness monitor on X") — route to monitoring-advisor directly
  • User is responding to an active incident — use incident response workflow
  • User is editing a dbt model — defer to prevent skill (auto-activates via hooks)
  • A skill is already active and handling the user's request

Workflow Steps

Step 1 (conditional): Assess current state — when user has specific tables in mind
Step 2: Identify gaps — the core of this workflow
Step 3: Create monitors — act on identified gaps

Determine entry point

Before starting, determine which step to enter based on the user's context:

  • User mentions specific tables ("what monitoring do I have on stg_payments?", "check my orders tables") → Start at Step 1: Assess Current State
  • User has a model file open with a specific table → Start at Step 1: Assess Current State
  • User wants estate-wide coverage ("where are my gaps?", "what should I monitor?") → Skip to Step 2: Identify Gaps
  • Ambiguous → Ask: "Would you like to check specific tables first, or look at coverage across your estate?"

Step 1: Assess Current State (conditional)

Skill: Read and follow ../asset-health/SKILL.md

Goal: Check health of the specific tables the user cares about — freshness, alerts, existing monitoring coverage, importance score, upstream dependencies.

When to run: Only when the user has specific tables in mind or a model file open. Provides table-level context before the broader coverage analysis.

Transition to Step 2: After the health report, offer the broader view:

"[Table] has [summary of health and existing monitors]. Want me to analyze monitoring coverage more broadly — across your warehouse or use cases — to find where the gaps are?"

If the user says yes, proceed to Step 2. If they're satisfied with the table-level view, stop.


Step 2: Identify Gaps

Skill: Read and follow ../monitoring-advisor/SKILL.md

When loading monitoring-advisor for this step, frame the request as coverage analysis — not direct monitor creation. The monitoring-advisor skill has two flows; this step uses the coverage analysis flow:

  • Warehouse discovery → use-case exploration → coverage analysis → gap identification

Goal: Analyze coverage across warehouses and use cases, identify unmonitored tables, prioritize by importance and anomaly activity.

This is the core step. Most workflow entries start here.

Transition to Step 3: When gaps are identified and the user wants to act:

"I've identified [N] monitoring gaps, prioritized by importance. Ready to create monitors for the top priorities?"

If yes, proceed to Step 3 (which stays within monitoring-advisor). If no, stop.


Step 3: Create Monitors

Skill: Continues within ../monitoring-advisor/SKILL.md — transitions from coverage analysis flow to direct monitor creation flow.

This step does NOT load a separate skill. The monitoring-advisor skill handles both gap identification (Step 2) and monitor creation (Step 3). The workflow just signals the transition from "analysis" to "creation."

Goal: Create monitors-as-code YAML for the identified gaps. For each gap:

  1. Determine the appropriate monitor type (freshness, volume, validation, custom SQL, comparison)
  2. Generate the monitor configuration
  3. Output as monitors-as-code YAML

The user can create monitors for all identified gaps or select specific ones.


Orchestration Rules

  • Users can enter at any step. The entry point section above determines where to start.
  • Each step loads the actual skill's SKILL.md via relative path. This workflow does not replicate skill logic — it sequences it.
  • Context carries forward through conversation naturally.
  • No state tracking or hooks. This is purely prompt-driven sequencing.
  • User can exit anytime.
  • If the user already knows what monitor to create (skipping Steps 1 and 2), they should not be in this workflow — context detection routes them to monitoring-advisor directly.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

monte-carlo-data/mc-agent-toolkit932026년 10월 24일 업데이트

Build a Connection Auth Rules for a Monte Carlo connection type. Fetches live connector schemas and transform steps from the apollo-agent repo.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

monte-carlo-data/mc-agent-toolkit932026년 10월 24일 업데이트

Generate SQL validation notebooks for dbt changes. Pass a GitHub PR URL or local dbt repo path.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

monte-carlo-data/mc-agent-toolkit932026년 10월 24일 업데이트

Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

monte-carlo-data/mc-agent-toolkit932026년 10월 24일 업데이트

Check the health of a data table/asset using Monte Carlo. Activates on "how is table X", "check health of X", "is X healthy", "status of X", "check on X table", or any health/status question about a data asset.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

monte-carlo-data/mc-agent-toolkit932026년 10월 24일 업데이트

Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

monte-carlo-data/mc-agent-toolkit932026년 10월 24일 업데이트

monte-carlo-data의 스킬 모두 보기

이 스킬의 문제 신고