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Monte Carlo Proactive Monitoring

Organization
monte-carlo-data
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.

Overview

Publishermonte-carlo-data
Repositorymc-agent-toolkit
Skill namemonte-carlo-proactive-monitoring
Stars
93
Forks
5
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by monte-carlo-data on GitHub. Read the source before you install it.

Installation

Install the Monte Carlo Proactive Monitoring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/proactive-monitoring
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/monte-carlo-data/mc-agent-toolkit.git /tmp/mc-agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/mc-agent-toolkit/skills/proactive-monitoring .claude/skills/monte-carlo-proactive-monitoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Monte Carlo Proactive Monitoring in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Monte Carlo Proactive Monitoring on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Monte Carlo Proactive Monitoring is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Frequently asked questions

What does the Monte Carlo Proactive Monitoring AI skill do?

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.

Why use Monte Carlo Proactive Monitoring on TypingMind?

Because you install it once and use it with any model. Monte Carlo Proactive Monitoring is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Monte Carlo Proactive Monitoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/monte-carlo-data/mc-agent-toolkit/tree/main/skills/proactive-monitoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Monte Carlo Proactive Monitoring?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Monte Carlo Proactive Monitoring?

As many as you like. As long as a model supports skills, you can use Monte Carlo Proactive Monitoring with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Monte Carlo Proactive Monitoring AI skill free?

Yes. It is published on GitHub by monte-carlo-data under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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