monte-carlo-proactive-monitoring

monte-carlo-proactive-monitoring is a skill for Claude Code from monte-carlo-data/mc-agent-toolkit. It costs 47 tokens per session (1,238 once invoked), scanned A, original, Apache-2.0.

A workflow for deciding what to monitor across a data estate, meaning the collection of an organization's data systems and tables. It assesses monitoring coverage, identifies gaps, and then guides monitor creation.

In plain words
What is it for?
Use it when asking what data should be monitored, where monitoring gaps exist, how to improve coverage, or how to plan monitoring across a data project.
Why use it?
It provides a structured way to find important unmonitored data instead of creating checks one at a time without an overall view. It also distinguishes coverage planning from responding to an active incident.

Skill for Claude Code

Written for Claude Code: when-to-use in frontmatter.

Good fit Use it when asking what data should be monitored, where monitoring gaps exist, how to improve coverage, or how to plan monitoring across a data project.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add monte-carlo-data/mc-agent-toolkit --skill proactive-monitoring
Clone the repo
git clone --depth 1 https://github.com/monte-carlo-data/mc-agent-toolkit

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for monte-carlo-proactive-monitoring

README.md
[![agentmods](https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring/github.svg)](https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring)
Your own site
<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for monte-carlo-proactive-monitoring

Your own site · 80×15
<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,238 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00047 $0.01238
Opus 5 $0.00023 $0.00619
Sonnet 5 $0.00009 $0.00248
Haiku 4.5 $0.00005 $0.00124

Measured 12d ago against content hash 3807a337357e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

monte-carlo-proactive-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/proactive-monitoring/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 117 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 117 lines · 47 tokens per session scan A 3807a337357e

Subscribe to this mod's changes

monte-carlo-proactive-monitoring is a skill published in the GitHub repository monte-carlo-data/mc-agent-toolkit (91 stars, last pushed 3d ago), licensed Apache-2.0. It adds 47 tokens to every session and 1,238 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

data-charts-tako

Search and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more.

gooseworks-ai/goose-skills · 35 tokens

apollo-lead-finder

Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Creates Apollo lists. Deduplicates against existing contacts by LinkedIn URL.

gooseworks-ai/goose-skills · 51 tokens

monorepo-management

Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency management. Use when setting up monorepos, optimizing builds, or managing shared dependencies.

wshobson/agents · 54 tokens

browse-and-evaluate

Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.

MoizIbnYousaf/Ai-Agent-Skills · 43 tokens

render-airdrop-carousel

Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an…

gooseworks-ai/goose-skills · 207 tokens

render-3d-product-showcase

Assemble a premium 3D product-showcase ad from a config — four beat clips (an orbiting hero rotation, a macro push-in, a physics reveal, a typographic close) normalized to the brand-color canvas, hard-concatenated in order, closed on a deterministic Playwright brand end card, and mixed under one instrumental bed at…

gooseworks-ai/goose-skills · 159 tokens