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 incident-responsegit 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/incident-response)<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/incident-response"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/incident-response.svg" alt="Measured on agentmods" 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 | $0.01951 |
| Opus 5 | $0.00023 | $0.00975 |
| Sonnet 5 | $0.00009 | $0.00390 |
| Haiku 4.5 | $0.00005 | $0.00195 |
Grade A, and why
monte-carlo-incident-response 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 8d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monte Carlo Incident Response Workflow
This workflow orchestrates the full lifecycle of a data incident by sequencing existing Monte Carlo skills. It does not contain investigation or remediation logic itself — each step loads the relevant skill's SKILL.md which has the actual instructions.
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.
When to activate this workflow
Activate when:
- Context detection routes here (active alerts detected + incident intent)
- User invokes
/mc-incident-response - User asks to "respond to an incident", "handle this alert", "triage and fix"
- User describes a data quality problem: "data is broken", "table is stale", "alert firing"
When NOT to activate this workflow
- User wants to create monitors or check coverage without an active incident — use proactive monitoring workflow
- User is editing a dbt model — defer to
preventskill (auto-activates via hooks) - User wants to check table health without an incident context — use
asset-healthdirectly - A skill is already active and handling the user's request
Workflow Steps
Step 1 (conditional): Triage — when user has multiple/unknown alerts
Step 2: Root Cause Analysis — the core investigation
Step 3: Remediation — fix or escalate
Step 4 (optional): Prevent Recurrence — add monitoring
Determine entry point
Before starting, determine which step to enter based on the user's context:
- User has no specific alert ("I have alerts firing", "what's going on?") → Start at Step 1: Triage
- User has a specific alert ID or table ("alert ABC-123", "stg_payments is stale") → Skip to Step 2: Root Cause Analysis
- User knows the root cause ("the ETL job failed, help me fix it") → Skip to Step 3: Remediation
- Alert is an agent-monitor alert (
alert_typesstarting with "Agent ", or the user's issue is about an AI agent) → for the investigation, read../troubleshoot-agent-traces/SKILL.mdinstead of../analyze-root-cause/SKILL.md; the remediation and monitoring steps still apply - Ambiguous → Ask: "Do you have a specific alert or table you want to investigate, or should I check your recent alerts first?"
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.
- 8d ago First seen · 147 lines · 47 tokens per session scan A 21de933bbf82
monte-carlo-incident-response is a skill published in the GitHub repository monte-carlo-data/mc-agent-toolkit (91 stars, last pushed 14d ago), licensed Apache-2.0. It adds 47 tokens to every session and 1,951 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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