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 performance-diagnosisgit 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/performance-diagnosis)<a href="https://agentmods.dev/skills/monte-carlo-data/mc-agent-toolkit/performance-diagnosis"><img src="https://agentmods.dev/badge/skills/monte-carlo-data/mc-agent-toolkit/performance-diagnosis.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 | $0.00075 | $0.01777 |
| Opus 5 | $0.00037 | $0.00889 |
| Sonnet 5 | $0.00015 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
monte-carlo-performance-diagnosis 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 7d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monte Carlo Performance Diagnosis Skill
This skill helps diagnose data pipeline performance issues using Monte Carlo's cross-platform observability data. It works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.
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:
- Tiered investigation approach:
references/investigation-tiers.md(relative to this file) - Query analysis patterns:
references/query-analysis.md(relative to this file)
When to activate this skill
Activate when the user:
- Asks about slow pipelines, jobs, or queries
- Wants to find expensive or costly queries
- Mentions performance regressions or degradation
- Asks "why is this pipeline slow?" or "what's using the most compute?"
- Wants to compare performance over time or find bottleneck tasks
- Asks about failed or futile query patterns
When NOT to activate this skill
Do not activate when the user is:
- Investigating data quality issues (use the prevent skill)
- Looking at storage costs (use the storage-cost-analysis skill)
- Creating monitors (use the monitoring-advisor skill)
- Just querying data or exploring table contents
Prerequisites
The following MCP tools must be available (connect to Monte Carlo's MCP server):
Discovery tools (Tier 1):
get_jobs_performance-- find slow/failing jobs across Airflow, dbt, Databricksget_top_slow_queries-- find slowest query groups by total runtime
What ships with it
3 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.
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.
- 7d ago First seen · 148 lines · 75 tokens per session scan A d3d74b5ccaef
monte-carlo-performance-diagnosis is a skill published in the GitHub repository monte-carlo-data/mc-agent-toolkit (91 stars, last pushed 13d ago), licensed Apache-2.0. It adds 75 tokens to every session and 1,777 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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