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monte-carlo-asset-health

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.

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/asset-health" 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/asset-health" 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/asset-health" 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/asset-health" 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/asset-health" 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-asset-health
description: 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.
bucket: Trust
version: 1.0.0

Monte Carlo Asset Health Skill

This skill checks the health of a data asset using Monte Carlo's observability platform. It produces a structured health report covering freshness, alerts, monitoring coverage, importance, and upstream dependency health.

Monte Carlo tool routing (required): Always call Monte Carlo MCP tools through this plugin's bundled server, whose fully-qualified tool names are mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__<tool> (e.g. mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts). Bare tool names used in this skill (get_alerts, search, get_table, …) refer to that bundled server. If the session also has a separately-configured monte-carlo-mcp server, do not route to it — it may point at a different endpoint or credentials.

REQUIRED: Read reference files before executing

You MUST read both reference files using the Read tool before making any MCP tool calls. These files are the source of truth for tool calls, parameters, and response interpretation. This file only defines when to activate and how to format the output.

  1. references/workflows.md (relative to this file) — exact tool calls, phases, and execution order
  2. references/parameters.md (relative to this file) — parameter conventions and field details

Do NOT make any MCP tool calls until you have read both files.

When to activate this skill

Activate when the user:

  • Asks about health: "how is table X doing?", "check health of X", "is X healthy?"
  • Asks about status: "what's the status of X?", "status of orders table"
  • Asks to check on a table: "check on X table", "check on X"
  • Asks about reliability, freshness, or quality of a specific asset
  • References a table in context of incident triage or change planning

When NOT to activate this skill

  • Profiling or exploring table data (row counts, column stats, distributions) → use explore-table
  • Creating or suggesting monitors → use monitoring-advisor
  • Active incident triage (investigating root cause of a firing alert) → use prevent skill Workflow 3

Health report format

CRITICAL: Only report data returned by the tools defined in references/workflows.md. Do NOT call additional tools, do NOT infer or fabricate metrics. Each row below specifies exactly which tool provides its value.

All sections (Active Alerts, Monitors, Upstream Issues, Recommendations) must always appear with their heading. Never omit a section — if there is no data, show the empty-state text defined below.

Never use emoji shortcodes (like :warning: or :arrow_up:). Use Unicode emoji characters directly (like ⚠️) or plain text. Shortcodes render as raw text in the terminal.

Always display URLs as bare URLs, never as markdown links (e.g., [text](url)).

{MC_WEBAPP_URL} appears throughout this template. Every occurrence must be replaced with the actual value returned by calling get_mc_webapp_url(). Never hardcode or guess this URL — it varies by environment.

Present results in this structure:

## Health Check: <table_name>

**Tags:** `tag1:value1`, `tag2:value2` (or "None" if no tags)
**Link:** {MC_WEBAPP_URL}/assets/{mcon}
**Warehouse:** snowflake-prod (Snowflake)
**Status: 🟢 Healthy / 🟡 Degraded / 🔴 Unhealthy** | **Importance:** 0.85 (key asset ⭐️)
**Avg Reads/Day:** ~538 | **Avg Writes/Day:** ~12

| Metric        | Value                          | Signal |
|---------------|--------------------------------|--------|
| Last Activity | Apr 6, 2025                    | 🟢 Recent    |
| Alerts        | 2 active                       | 🔴 Has alerts |
| Monitoring    | 3 active monitors              | 🟢 Monitored  |
| Upstream      | 1/3 sources unhealthy          | 🔴 Issues     |

### Active Alerts

| Date  | Type           | Priority | Status           | Link                                                    |
|-------|----------------|----------|------------------|---------------------------------------------------------|
| Apr 8 | Metric anomaly | P3       | Not acknowledged | {MC_WEBAPP_URL}/alerts/{alert_uuid} |
| Apr 7 | Freshness      | P2       | Acknowledged     | {MC_WEBAPP_URL}/alerts/{alert_uuid} |

If there are more than 5 active alerts, display only 5. Do NOT put the overflow
message inside the table as a row. Instead, put it as plain text on the line
immediately after the table:

There are N more alerts not shown for brevity

If there are zero active alerts, show:
No active alerts in the last 7 days.

### Monitors

| Type        | Name                                    | Incidents (7d) | Status              |
|-------------|-----------------------------------------|----------------|---------------------|
| TABLE       | Orders freshness and schema             | 3              | Running hourly      |
| METRIC      | Revenue row count                       | 0              | Never executed      |
| BULK_METRIC | Warehouse volume check                  | 21             | ⚠️ 1 table has errors |

If there are zero monitors, show:
No monitors configured for this table.

### Upstream Issues
- raw_orders — FRESHNESS alert: not updated in 8h
- raw_payments — healthy
- dim_customers — healthy

> Want me to check further upstream for **raw_orders**?

If there are no upstream dependencies, show:
No upstream dependencies found.

### Diagnosis

1-2 sentences summarizing what is causing the table to be unhealthy, or
confirming it is healthy. This should naturally lead into the recommendations.

Example (unhealthy):
Upstream table raw_orders has not been updated in 8 hours, which is likely
causing staleness in this table. There are also 2 unacknowledged alerts.

Example (healthy):
Table is healthy — no active alerts, monitored, and all upstream sources
are in good shape.

### Recommendations
- Investigate upstream raw_orders freshness — likely root cause of this table's staleness
- Acknowledge or investigate the 2 active alerts

If there are no recommendations, show:
No recommendations — table looks healthy.

Metric definitions — exact data sources

Each metric row MUST use only the specified data source. Do not add, infer, or embellish values beyond what the tool returns.

MetricData sourceWhat to showSignal
Last Activityget_table → last_activityDate of last activity (e.g., "Apr 6, 2025")🟢 Recent (within 7 days) / 🟡 Stale (older than 7 days)
Alertsget_alerts → count"N active" or "No active alerts"🔴 Has alerts / 🟢 No alerts
Monitoringget_monitors → count where is_paused is false"N active monitors" or "0 active monitors (M paused)". Include relevant details from monitor fields (incident counts, error counts, types).🟢 Monitored (≥1 active) / 🔴 Unmonitored (0 active)
Upstreamget_asset_lineage (upstream) + Phase 3 checks"N/M sources unhealthy" or "All N sources healthy"🔴 Issues (any unhealthy) / 🟢 Healthy (all healthy)

Importance is shown next to the Status line (not in the metrics table). Source: get_table → importance_score + is_important. Show "X.XX (key asset ⭐️)" if key asset or importance > 0.8, otherwise just "X.XX".

Avg Reads/Day and Avg Writes/Day are shown below the Status line. Source: get_table → table_stats.avg_reads_per_active_day and table_stats.avg_writes_per_active_day.

Do NOT include downstream data. This skill only queries upstream lineage.

Status determination

  • 🔴 Unhealthy: Any active alerts on the asset (from get_alerts with statuses ["NOT_ACKNOWLEDGED", "ACKNOWLEDGED", "WORK_IN_PROGRESS"] — see parameters.md)
  • 🟡 Degraded: No active alerts, but 0 active monitors on a high-importance asset (importance > 0.8 or key asset)
  • 🟢 Healthy: No active alerts and has at least 1 active monitor

Tags

Display tags from the search tool's properties field. Show as inline badges: key:value. If no tags exist, show "None". Always include the Tags line.

Warehouse

Display the warehouse name and type from the search result. Always include this line.

Recommendations

Only include recommendations derivable from collected data:

  • Upstream health issues that may be root causes
  • Active alerts that need acknowledgment or investigation
  • Do NOT recommend specific monitor types — that is outside this skill's scope

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