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 agentmods add skills/monte-carlo-data/mc-agent-toolkit/analyze-root-causenpx skills add monte-carlo-data/mc-agent-toolkit --skill analyze-root-causegit 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/analyze-root-cause)<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/analyze-root-cause"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/analyze-root-cause.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00073 | $0.03723 |
| Opus 5 | $0.00036 | $0.01861 |
| Sonnet 5 | $0.00015 | $0.00745 |
| Haiku 4.5 | $0.00007 | $0.00372 |
Grade A, and why
monte-carlo-analyze-root-cause 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 6d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monte Carlo Root Cause Analysis Skill
This skill helps investigate data incidents — freshness delays, volume anomalies, schema changes, field metric drift, and ETL failures — by guiding the agent through a systematic investigation using Monte Carlo's MCP tools. It combines observability metadata with optional direct data querying to find the root cause.
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-configuredmonte-carlo-mcpserver, do not route to it — it may point at a different endpoint or credentials.
Reference files live next to this skill file. Use the Read tool (not MCP resources) to access them:
- Investigation playbooks by issue type:
references/<type>-investigation.md - Data exploration patterns:
references/data-exploration.md - Intake when no incident ID:
references/intake-no-incident.md - Common root cause catalog:
references/common-root-causes.md
When to activate this skill
Activate when the user:
- Mentions a Monte Carlo alert, incident, or anomaly
- Asks "why is this table stale?" or "why did row count drop?"
- Wants to investigate a data quality issue
- Asks about freshness, volume, or schema problems
- Mentions pipeline failures (Airflow, dbt, Databricks)
- Says things like "debug this alert", "investigate this incident", "root cause analysis"
When NOT to activate this skill
Do not activate when the user is:
- Creating monitors (use the monitoring-advisor skill)
- Investigating agent-monitor alerts (agent evaluation, agent metric, agent trajectory, agent validation) or AI-agent traces/conversations (use the
monte-carlo-troubleshoot-agent-tracesskill — read../troubleshoot-agent-traces/SKILL.md) - Running impact assessments before code changes (use the prevent skill)
- Looking at storage costs (use the storage-cost-analysis skill)
- Exploring pipeline performance without a specific incident (use the performance-diagnosis skill)
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 4.7 KB
- references/common-root-causes.md 4.0 KB
- references/data-exploration.md 4.8 KB
- references/etl-failure-investigation.md 2.7 KB
- references/field-anomaly-investigation.md 4.0 KB
- references/freshness-investigation.md 3.0 KB
- references/intake-no-incident.md 3.5 KB
- references/query-change-investigation.md 2.2 KB
- references/schema-investigation.md 1.8 KB
- references/volume-investigation.md 2.4 KB
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.
- 6d ago First seen · 227 lines · 73 tokens per session scan A 432ae7ec457f
monte-carlo-analyze-root-cause is a skill published in the GitHub repository monte-carlo-data/mc-agent-toolkit (91 stars, last pushed 12d ago), licensed Apache-2.0. It adds 73 tokens to every session and 3,723 once invoked, about $0.0004 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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