monte-carlo-prevent
Helps your agent understand data health and the impact of changes before editing SQL or dbt. Shows alerts, lineage, and the potential blast radius of issues.
What it does
This tool helps you check your data's health and understand the impact of changes before you edit data code. It shows information about table quality, active alerts, and how data connects.
When to use it
- ✓When opening or editing a data code file (SQL or dbt).
- ✓When you mention a table or data model name.
- ✓Before making any changes to an existing data model.
Example of what to ask your AI
“Check the health of the 'user_data' table and active alerts before I add a new column.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool connects to Monte Carlo to fetch information about your data quality. It analyzes table health, alerts, and data flow to give you context before you make changes.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-preventOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/monte-carlo-prevent/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Monte Carlo Prevent Skill
This skill brings Monte Carlo's data observability context directly into your editor. When you're modifying a dbt model or SQL pipeline, use it to surface table health, lineage, active alerts, and to generate monitors-as-code without leaving Claude Code.
Reference files live next to this skill file. Use the Read tool (not MCP resources) to access them:
- Full workflow step-by-step instructions:
references/workflows.md(relative to this file) - MCP parameter details:
references/parameters.md(relative to this file) - Troubleshooting:
references/TROUBLESHOOTING.md(relative to this file)
When to activate this skill
Do not wait to be asked. Run the appropriate workflow automatically whenever the user:
References or opens a
.sqlfile or dbt model (files inmodels/) → run Workflow 1Mentions a table name, dataset, or dbt model name in passing → run Workflow 1
Describes a planned change to a model (new column, join update, filter change, refactor) → STOP — run Workflow 4 before writing any code
Adds a new column, metric, or output expression to an existing model → run Workflow 4 first, then ALWAYS offer Workflow 2 regardless of risk tier — do not skip the monitor offer
Asks about data quality, freshness, row counts, or anomalies → run Workflow 1
Wants to triage or respond to a data quality alert → run Workflow 3
Present the results as context the engineer needs before proceeding — not as a response to a question.
When NOT to activate this skill
Do not invoke Monte Carlo tools for:
- Seed files (files in seeds/ directory)
- Analysis files (files in analyses/ directory)
- One-off or ad-hoc SQL scripts not part of a dbt project
- Configuration files (dbt_project.yml, profiles.yml, packages.yml)
- Test files unless the user is specifically asking about data quality
If uncertain whether a file is a dbt model, check for {{ ref() }} or {{ source() }} Jinja references — if absent, do not activate.
Macros and snapshots — gate edits, skip auto-context
Macro files (macros/) and snapshot files (snapshots/) are not models, so
do not auto-fetch Monte Carlo context (Workflow 1) when they are opened. However,
macros are inlined into every model that calls them at compile time — a one-line
macro change can silently alter dozens of models. Snapshots control historical
tracking and are similarly sensitive.
The pre-edit hook gates these files. If the hook fires for a macro or snapshot, identify which models are affected and run the change impact assessment (Workflow 4) for those models before proceeding with the edit.
REQUIRED: Change impact assessment before any SQL edit
Before editing or writing any SQL for a dbt model or pipeline, you MUST run Workflow 4.
This applies whenever the user expresses intent to modify a model — including phrases like:
- "I want to add a column…"
- "Let me add / I'm adding…"
- "I'd like to change / update / rename…"
- "Can you add / modify / refactor…"
- "Let's add…" / "Add a
<column>column" - Any other description of a planned schema or logic change
- "Exclude / filter out / remove [records/customers/rows]…"
- "Adjust / increase / decrease [threshold/parameter/value]…"
- "Fix / bugfix / patch [issue/bug]…"
- "Revert / restore / undo [change/previous behavior]…"
- "Disable / enable [feature/logic/flag]…"
- "Clean up / remove [references/columns/code]…"
- "Implement [backend/feature] for…"
- "Create [models/dbt models] for…" (when modifying existing referenced tables)
- "Increase / decrease / change [max_tokens/threshold/date constant/numeric parameter]…"
- Any change to a hardcoded value, constant, or configuration parameter within SQL
- "Drop / remove / delete [column/field/table]"
- "Rename [column/field] to [new name]"
- "Add [column]" (short imperative form, e.g. "add a created_at column")
- Any single-verb imperative command targeting a column, table, or model (e.g. "drop X", "rename Y", "add Z", "remove W")
Parameter changes (threshold values, date constants, numeric limits) appear safe but silently change model output. Treat them the same as logic changes for impact assessment purposes.
Do not write or edit any SQL until the change impact assessment (Workflow 4) has been presented to the user. The assessment must come first — not after the edit, not in parallel.
Pre-edit gate — check before modifying any file
…
FAQ
When should I use this tool?
Use it whenever you are about to edit data code, especially dbt models. It's crucial before adding columns, changing filters, or refactoring code.
What happens if I don't use the tool before editing?
You might make changes that affect other data code without realizing it, causing unexpected data quality or availability issues.
Does the tool automatically create or modify code?
No, it provides information and alerts for you to make informed decisions. It might suggest creating 'monitors-as-code', but you perform the final edit of your code.
Details
Original description by the author
Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3631
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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