复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Monte Carlo's official toolkit for AI coding agents.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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.
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.
| Skill | Description | Details |
|---|---|---|
| Asset Health | Single-table health report: freshness, active alerts, monitor coverage, importance, and upstream issues. Run before building on a table. | README |
| Skill | Description | Details |
|---|---|---|
| Incident Response (workflow) | Orchestrates full incident lifecycle — triage → root cause → remediation → prevent recurrence. | SKILL |
| Automated Triage | Scores and prioritizes active alerts; runs deep troubleshooting on high-signal ones. | SKILL |
| Analyze Root Cause | Investigates incidents via lineage tracing, ETL checks, query analysis, and data profiling. | README |
| Remediation | Proposes and executes fixes for data-quality alerts; assesses blast radius before acting, or escalates with full context. | README |
| Troubleshoot Agent Traces | Investigates AI-agent alerts (evaluation, metric, trajectory, validation) and agent traces — kicks off the trace troubleshooting agent and guides a backend-aware manual investigation. | README |
| Skill | Description | Details |
|---|---|---|
| Proactive Monitoring (workflow) | Sequences coverage analysis → gap identification → monitor creation into a guided flow. | SKILL |
| Monitoring Advisor | Identifies 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 MaC | Create, 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 Monitor | Recommends sensitivity, segment, and schedule changes to reduce alert noise on an existing metric monitor. | SKILL |
| Skill | Description | Details |
|---|---|---|
| Prevent | Edit-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 Notebook | Generates targeted SQL validation queries for a dbt PR or local repo change. | README |
| Skill | Description | Details |
|---|---|---|
| Storage Cost Analysis | Identifies storage waste (unread, zombie, dead-end tables); uses lineage to verify cleanup is safe and estimates savings. | README |
| Performance Diagnosis | Diagnoses slow pipelines and expensive queries across Airflow, dbt, Databricks, and other platforms. | README |
| Skill | Description | Details |
|---|---|---|
| Push Ingestion | Generates collection scripts to push metadata, lineage, or query logs to Monte Carlo from any data source. | README |
| Connection Auth Rules | Builds Connection Auth Rules JSON for a Monte Carlo connection type using live connector schemas. | SKILL |
| Instrument Agent | Instruments 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 |
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.
/plugin marketplace add monte-carlo-data/mc-agent-toolkit
/plugin install mc-agent-toolkit@mc-marketplace
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.
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.
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.
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.
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.
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.
See CONTRIBUTING.md for guidelines on adding skills, creating plugins, and submitting pull requests. It also covers plugin architecture and releasing new versions.
This project is licensed under the Apache-2.0 license — see LICENSE for details.
See SECURITY.md for reporting vulnerabilities.
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: 1.0.0This 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.
Activate when:
/mc-proactive-monitoringmonitoring-advisor directlyprevent skill (auto-activates via hooks)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
Before starting, determine which step to enter based on the user's context:
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.
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:
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.
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:
The user can create monitors for all identified gaps or select specific ones.
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