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/context-detectionnpx skills add monte-carlo-data/mc-agent-toolkit --skill context-detectiongit 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/context-detection)<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/context-detection"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/context-detection.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 | $0.00046 | $0.02275 |
| Opus 5 | $0.00023 | $0.01137 |
| Sonnet 5 | $0.00009 | $0.00455 |
| Haiku 4.5 | $0.00005 | $0.00228 |
Grade A, and why
monte-carlo-context-detection 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 4d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monte Carlo Context Detection
This skill determines which Monte Carlo skill or workflow best fits the user's current context. It activates reactively for ambiguous or multi-step data-related messages, gathers signals, and routes to the right skill or workflow.
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 file for signal definitions: references/signal-definitions.md (relative to this file). Read it before routing.
When to activate this skill
This skill is activated by the CLAUDE.md routing table when:
- The user's message relates to data quality, alerts, incidents, coverage, or Monte Carlo — but doesn't clearly match a single skill in the routing table
- The user's intent is ambiguous or could span multiple skills
- The user asks a broad question like "help me with my data" or "what's going on?"
When NOT to activate this skill
- A skill or workflow is already active in the conversation — the active skill owns the conversation, do not intercept
- The user's message clearly matches a single skill in the CLAUDE.md routing table — route directly, no need for context detection
- The user is editing a dbt model — defer to the
preventskill which auto-activates via hooks - The user's message is not data-related at all
Workflow: Reactive Routing
This skill is purely reactive — it activates for ambiguous or multi-step data-related messages and routes them.
Follow these steps in order.
Step 0: Fast-path clear intent (stop early if matched)
Before doing anything else, check whether the user's message unambiguously matches a single existing skill. If so, skip the rest of this workflow and immediately load that skill — do NOT read references/signal-definitions.md, do NOT make API probes.
What ships with it
1 file 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.
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.
- 4d ago First seen · 136 lines · 46 tokens per session scan A 37dddfa67c33
monte-carlo-context-detection is a skill published in the GitHub repository monte-carlo-data/mc-agent-toolkit (91 stars, last pushed 10d ago), licensed Apache-2.0. It adds 46 tokens to every session and 2,275 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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