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.
npx skills add monte-carlo-data/mc-agent-toolkit --skill proactive-monitoringgit clone --depth 1 https://github.com/monte-carlo-data/mc-agent-toolkitWrote 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.
[](https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/proactive-monitoring)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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-advisordirectly - User is responding to an active incident — use incident response workflow
- User is editing a dbt model — defer to
preventskill (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.
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.
- 12d ago First seen · 117 lines · 47 tokens per session scan A 3807a337357e
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.
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