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monte-carlo-proactive-monitoring

Monte Carlo's official toolkit for AI coding agents.

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项目 README

来源文件:README.md

抓取于 2026年9月13日

MC Agent Toolkit

Monte Carlo's official toolkit for AI coding agents. Brings data observability — lineage, monitoring, validation, alerting, and metadata ingestion — directly into your development workflow. The toolkit bundles multiple skills into a single plugin that works across supported editors.

Using Claude.ai (web or desktop) instead of a coding agent? Monte Carlo is a verified connector in the Claude directory — install it directly.

Features

The toolkit bundles the following capabilities as a single mc-agent-toolkit plugin. Each feature is a skill that can also be used standalone.

Skills are grouped by the job they help you do. Orchestrated workflows sequence individual skills into guided multi-step flows; atomic skills can be invoked directly by name. Both are loaded the same way.

Trust — pre-query and pre-build checks

SkillDescriptionDetails
Asset HealthSingle-table health report: freshness, active alerts, monitor coverage, importance, and upstream issues. Run before building on a table.README

Incident Response — triage, investigate, fix

SkillDescriptionDetails
Incident Response (workflow)Orchestrates full incident lifecycle — triage → root cause → remediation → prevent recurrence.SKILL
Automated TriageScores and prioritizes active alerts; runs deep troubleshooting on high-signal ones.SKILL
Analyze Root CauseInvestigates incidents via lineage tracing, ETL checks, query analysis, and data profiling.README
RemediationProposes and executes fixes for data-quality alerts; assesses blast radius before acting, or escalates with full context.README
Troubleshoot Agent TracesInvestigates AI-agent alerts (evaluation, metric, trajectory, validation) and agent traces — kicks off the trace troubleshooting agent and guides a backend-aware manual investigation.README

Monitoring — coverage gaps, monitor creation, noise reduction

SkillDescriptionDetails
Proactive Monitoring (workflow)Sequences coverage analysis → gap identification → monitor creation into a guided flow.SKILL
Monitoring AdvisorIdentifies coverage gaps and creates monitors for warehouse tables or AI agents — validates tables and fields against your live workspace, emits monitors-as-code YAML.README
Manage MaCCreate, edit, validate, and import Monitors-as-Code YAML files — authors new monitors from scratch, modifies existing files, validates against the published JSON Schema, and exports live monitors to YAML.SKILL
Tune MonitorRecommends sensitivity, segment, and schedule changes to reduce alert noise on an existing metric monitor.SKILL

Prevent — catch issues before they ship

SkillDescriptionDetails
PreventEdit-lifecycle safety net for dbt/SQL: surfaces blast radius and monitor gaps before edits, generates monitors-as-code for new logic. Auto-activates via hooks.README
Generate Validation NotebookGenerates targeted SQL validation queries for a dbt PR or local repo change.README

Optimize — cost and performance

SkillDescriptionDetails
Storage Cost AnalysisIdentifies storage waste (unread, zombie, dead-end tables); uses lineage to verify cleanup is safe and estimates savings.README
Performance DiagnosisDiagnoses slow pipelines and expensive queries across Airflow, dbt, Databricks, and other platforms.README

Setup — ingestion and connections

SkillDescriptionDetails
Push IngestionGenerates collection scripts to push metadata, lineage, or query logs to Monte Carlo from any data source.README
Connection Auth RulesBuilds Connection Auth Rules JSON for a Monte Carlo connection type using live connector schemas.SKILL
Instrument AgentInstruments a Python AI agent for Monte Carlo Agent Observability — detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, sets up tracing, and verifies traces in Monte Carlo. Asks before editing.SKILL

Installing the plugin (recommended)

Monte Carlo recommends installing the mc-agent-toolkit plugin. The plugin bundles all skills together with hooks, the Monte Carlo MCP server, and agent-specific capabilities — no separate MCP configuration or authentication setup needed. See the plugins page for the full list of supported coding agents.

Claude Code

  1. Add the marketplace:
    /plugin marketplace add monte-carlo-data/mc-agent-toolkit
    
  2. Install the plugin:
    /plugin install mc-agent-toolkit@mc-marketplace
    
  3. Updates — claude plugin update pulls in the latest skill and hook changes.

See the Claude Code plugin README for detailed setup and usage.

For other coding agents (Cursor, Copilot CLI, OpenCode, Codex, Cortex Code), see the plugins page for installation guides.

Using skills directly (advanced)

Skills can also be used standalone without the plugin. This is for users who want to install individual skills via registries or use them with agents not listed above.

Prerequisites

  • A Monte Carlo account with Editor role or above

  • Monte Carlo MCP server — configure with:

    claude mcp add --transport http monte-carlo-mcp https://mcp.getmontecarlo.com/mcp
    

    Then authenticate: run /mcp in your editor, select monte-carlo-mcp, and complete the OAuth flow.

    See official docs for other MCP clients and advanced options.

    Legacy: header-based auth (for MCP clients without HTTP transport)

    If your MCP client doesn't support HTTP transport, use .mcp.json.example with npx mcp-remote and header-based authentication. See the MCP server docs for details.

Installation

npx skills add monte-carlo-data/mc-agent-toolkit --skill prevent

Or copy directly:

cp -r skills/prevent ~/.claude/skills/prevent

See the skills directory for the full list and individual READMEs.

Telemetry

All six editor plugins send an anonymous install beacon — a Toolkit Installed event carrying an opaque per-install UUID, a per-session UUID, the toolkit version, and the editor name — once per machine per toolkit version (first install and after each version change), so we can count installations and version adoption. The Claude Code and Cortex Code plugins additionally send anonymous skill-usage telemetry (which skills are invoked, how often). As of v1.13.3, the same install_id and toolkit version also ride as HTTP headers on authenticated MCP requests to the Monte Carlo MCP server, so the otherwise-anonymous install can be correlated with the account's MCP tool usage server-side. No prompts, skill arguments, or code are ever sent, and telemetry is fail-open and non-blocking. To disable all of it, set MC_AGENT_TOOLKIT_TELEMETRY_DISABLED=1. See each plugin's README (e.g. Claude Code, Cortex Code) for details.

Contributing

See CONTRIBUTING.md for guidelines on adding skills, creating plugins, and submitting pull requests. It also covers plugin architecture and releasing new versions.

License

This project is licensed under the Apache-2.0 license — see LICENSE for details.

Security

See SECURITY.md for reporting vulnerabilities.

DevOps 与部署数据与 AI

中风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:1 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/monte-carlo-data/mc-agent-toolkit.git
  3. 将 "skills/proactive-monitoring" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/monte-carlo-data/mc-agent-toolkit.git
  3. 将 "skills/proactive-monitoring" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/monte-carlo-data/mc-agent-toolkit.git
  3. 将 "skills/proactive-monitoring" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/monte-carlo-data/mc-agent-toolkit.git
  3. 将 "skills/proactive-monitoring" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/monte-carlo-data/mc-agent-toolkit.git
  3. 将 "skills/proactive-monitoring" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: monte-carlo-proactive-monitoring
description: 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.
when_to_use: |
  Invoke when the user wants to IMPROVE monitoring coverage across their data estate — identify gaps, prioritize what to monitor, or take a systematic approach to observability.
  Example triggers: "what should I monitor?", "where are my coverage gaps?", "improve monitoring across my warehouse", "help me prioritize which tables to monitor", "audit my coverage".

  Covers: warehouse/use-case discovery → gap analysis → monitor prioritization → handoff to monitoring-advisor for actual monitor creation.

  Do NOT invoke when the user has a specific incident to investigate (use incident-response) or wants to create a single known monitor on a known table (use monitoring-advisor directly).
bucket: Agent-routing
version: 1.0.0

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.

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