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Agent skill

Monte Carlo Validation Notebook

by sickn33 in sickn33/agentic-awesome-skills

Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.

MITAuto-check passedData & Analytics

Install Monte Carlo Validation Notebook

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-validation-notebook -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-validation-notebook --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/monte-carlo-validation-notebook .claude/skills/monte-carlo-validation-notebook && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
monte-carlo-validation-notebook
GitHub stars
47k
Used in
1 other repo
Token cost
~1.1k tokens
SKILL.md length
431 words
Files
4 (incl. scripts, references)
Skills in repo
1,493
Repo updated
First seen
Licence
MIT

At a glance

Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.

  • Tasks that involve Data pipelines and ETL
  • SKILL.md covers Detailed Guide, When to Use and Limitations
  • Runs Python scripts from its folder; calls gh, pip3 and pip; reaches getmontecarlo.com
  • Tasks that involve SQL

What it does

Monte Carlo Validation Notebook is an agent skill from sickn33/agentic-awesome-skills. Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/detailed-guide.md`, `scripts/generate_notebook_url.py` and `scripts/resolve_dbt_schema.py`).

It sits in Data & Analytics, covering Data pipelines and ETL and SQL. It works with dbt, SQL and GitHub. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Tasks that involve Data pipelines and ETL
  • Tasks that involve SQL

Example prompts

  • “Use the monte-carlo-validation-notebook skill to generate SQL validation notebooks for dbt PR changes with before/after comparison queries”
  • “/monte-carlo-validation-notebook”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • gh
    • pip3
    • pip
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • getmontecarlo.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Monte Carlo Validation Notebook loads about 1.1k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 31 tokens; SKILL.md has 431 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 431 words, ~1,068 tokens.

Download SKILL.mdSave it as .claude/skills/monte-carlo-validation-notebook/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
monte-carlo-validation-notebook
description
Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.
category
data
risk
safe
source
community
source_repo
monte-carlo-data/mc-agent-toolkit
source_type
community
date_added
2026-04-08
author
monte-carlo-data
tags
data-observability, validation, dbt, monte-carlo, sql-notebook
tools
claude, cursor, codex

Tip: This skill works well with Sonnet. Run /model sonnet before invoking for faster generation.

Generate a SQL Notebook with validation queries for dbt changes.

Arguments: $ARGUMENTS

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

When to Use

Use this skill when the user wants to validate dbt model or snapshot changes with Monte Carlo SQL Notebook queries, either from a GitHub PR or a local dbt repository.

Parse the arguments:

  • Target (required): first argument — a GitHub PR URL or local dbt repo path
  • MC Base URL (optional): --mc-base-url <URL> — defaults to https://getmontecarlo.com
  • Models (optional): --models <model1,model2,...> — comma-separated list of model filenames (without .sql extension) to generate queries for. Only these models will be included. By default, all changed models are included up to a maximum of 10.

Setup

Prerequisites:

  • gh (GitHub CLI) — required for PR mode. Must be authenticated (gh auth status).
  • python3 — required for helper scripts.
  • pyyaml — install with pip3 install pyyaml (or pip install pyyaml, uv pip install pyyaml, etc.)

Note: Generated SQL uses ANSI-compatible syntax that works across Snowflake, BigQuery, Redshift, and Athena. Minor adjustments may be needed for specific warehouse quirks.

This skill includes two helper scripts in ${CLAUDE_PLUGIN_ROOT}/skills/monte-carlo-validation-notebook/scripts/:

  • resolve_dbt_schema.py - Resolves dbt model output schemas from dbt_project.yml routing rules and model config overrides.
  • generate_notebook_url.py - Encodes notebook YAML into a base64 import URL and opens it in the browser.
Show full SKILL.md (175 more words)Show less

Mode Detection

Auto-detect mode from the target argument:

  • If target looks like a URL (contains :// or github.com) -> PR mode
  • If target is a path (., /path/to/repo, relative path) -> Local mode

Context

This command generates a SQL Notebook containing validation queries for dbt changes. The notebook can be opened in the MC Bridge SQL Notebook interface for interactive validation.

The output is an import URL that opens directly in the notebook interface:

<MC_BASE_URL>/notebooks/import#<base64-encoded-yaml>

Key Features:

  • Database Parameters: Two text parameters (prod_db and dev_db) for selecting databases
  • Schema Inference: Automatically infers schema per model from dbt_project.yml and model configs
  • Single-table queries: Basic validation queries using {{prod_db}}.<SCHEMA>.<TABLE>
  • Comparison queries: Before/after queries comparing {{prod_db}} vs {{dev_db}}
  • Flexible usage: Users can set both parameters to the same database for single-database analysis

Notebook YAML Spec Reference

Key structure:

yaml
version: 1
metadata:
  id: string           # kebab-case + random suffix
  name: string         # display name
  created_at: string   # ISO 8601
  updated_at: string   # ISO 8601
default_context:       # optional database/schema context
  database: string
  schema: string
cells:
  - id: string
    type: sql | markdown | parameter
    content: string    # SQL, markdown, or parameter config (JSON)
    display_type: table | bar | timeseries

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/monte-carlo-validation-notebook of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/detailed-guide.md
  • scripts/generate_notebook_url.py
  • scripts/resolve_dbt_schema.py

Open the folder on GitHubat commit 680176d

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Monte Carlo Validation Notebook next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Monte Carlo Validation Notebook compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Monte Carlo Validation Notebook this skillsickn33/agentic-awesome-skills47k1 repos~1.1kAutomated safety check: PassMIT
Dinobase Business Data Querieskappa90/dinobase263—~1.5kAutomated safety check: PassCustom licence
Analytics Engineerborghei/Claude-Skills886—~3.4kAutomated safety check: PassMIT
Answering Natural Language Questions With DbtKilo-Org/kilo-marketplace190—~1.9kAutomated safety check: PassApache-2.0
dbt Model BuilderAltimateAI/data-engineering-skills128—~890Automated safety check: PassMIT
dbt Error DebuggingAltimateAI/data-engineering-skills128—~1.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Monte Carlo Validation Notebook

What does Monte Carlo Validation Notebook do?

Generates SQL validation notebooks for dbt PR changes with before/after comparison queries. Monte Carlo Validation Notebook is an agent skill from sickn33/agentic-awesome-skills. Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.

When should I use Monte Carlo Validation Notebook?

Monte Carlo Validation Notebook fits situations like: tasks that involve Data pipelines and ETL; tasks that involve SQL.

How do I install Monte Carlo Validation Notebook in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-validation-notebook -a claude-code`. Or copy the skill folder (skills/monte-carlo-validation-notebook in sickn33/agentic-awesome-skills) into .claude/skills/monte-carlo-validation-notebook in your project. Claude Code loads it when a task matches its description.

How do I install Monte Carlo Validation Notebook in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-validation-notebook -a codex`. Or copy the skill folder (skills/monte-carlo-validation-notebook in sickn33/agentic-awesome-skills) into .agents/skills/monte-carlo-validation-notebook in your project. Codex loads it when a task matches its description.

Can I use Monte Carlo Validation Notebook in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-validation-notebook -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/monte-carlo-validation-notebook, .gemini/skills/monte-carlo-validation-notebook, .github/skills/monte-carlo-validation-notebook and .opencode/skills/monte-carlo-validation-notebook in your project.

What does Monte Carlo Validation Notebook need to run?

Going by SKILL.md and its folder, Monte Carlo Validation Notebook needs Python for the scripts in its folder and the command-line tools its instructions call (gh, pip3, pip and uv). Our summary lists: Python 3.

Does Monte Carlo Validation Notebook access the network?

SKILL.md names 1 domain. In commands or code: getmontecarlo.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Monte Carlo Validation Notebook safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Monte Carlo Validation Notebook use?

Monte Carlo Validation Notebook is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Monte Carlo Validation Notebook use?

About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.4k tokens, read only when the agent opens those files.

What are the alternatives to Monte Carlo Validation Notebook?

Skills that share tags, products or a category with Monte Carlo Validation Notebook: Dinobase Business Data Queries (kappa90/dinobase, 263 stars), Analytics Engineer (borghei/Claude-Skills, 886 stars), Answering Natural Language Questions With Dbt (Kilo-Org/kilo-marketplace, 190 stars) and dbt Model Builder (AltimateAI/data-engineering-skills, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Monte Carlo Validation Notebook?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.