Agent skill

Monte Carlo Prevent

by sickn33 in sickn33/agentic-awesome-skills

Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.

MITAuto-check passedData & Analytics

Install Monte Carlo Prevent

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

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-prevent --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-prevent .claude/skills/monte-carlo-prevent && 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-prevent
GitHub stars
47k
Used in
1 other repo
Token cost
~3.3k tokens
SKILL.md length
1,589 words
Files
4 (incl. references)
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.

  • Works in 5 steps: Table health check → Add a monitor → Alert triage → …
  • Tasks that involve Data pipelines and ETL
  • SKILL.md covers When to activate this skill, When NOT to activate this skill, REQUIRED: Change impact… and Pre-edit gate — check before…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Monte Carlo Prevent is an agent skill from sickn33/agentic-awesome-skills. Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/TROUBLESHOOTING.md`, `references/parameters.md` and `references/workflows.md`).

It sits in Data & Analytics, covering Data pipelines and ETL, Data governance and SQL. It works with SQL and dbt. 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 Data governance
  • Tasks that involve SQL

Example prompts

  • “Use the monte-carlo-prevent skill to surface Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits”
  • “/monte-carlo-prevent”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Table health check
  2. Add a monitor
  3. Alert triage
  4. Change impact assessment — REQUIRED before modifying a model
  5. Change validation queries

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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 Prevent loads about 3.3k tokens when it runs, and up to ~9.6k if it reads all its reference files. Until then it costs about 34 tokens; SKILL.md has 1,589 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.6k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 1,589 words, ~3,257 tokens.

Download SKILL.mdSave it as .claude/skills/monte-carlo-prevent/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
monte-carlo-prevent
description
Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.
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, dbt, schema, monte-carlo, lineage
tools
claude, cursor, codex

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 .sql file or dbt model (files in models/) → run Workflow 1

  • Mentions 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

Before calling Edit, Write, or MultiEdit on any .sql or dbt model file, you MUST check:

  1. Has the synthesis step been run for THIS SPECIFIC CHANGE in the current prompt?
  2. If YES → proceed with the edit
  3. If NO → stop immediately, run Workflow 4, present the full report with synthesis connected to this specific change. If risk is High or Medium: ask "Do you want me to proceed with the edit?" and wait for explicit confirmation. If risk is Low: use judgment — proceed if straightforward and no concerns found, otherwise ask before editing.

Important: "Workflow 4 already ran this session" is NOT sufficient to proceed. Each distinct change prompt requires its own synthesis step connecting the MC findings to that specific change.

The synthesis must reference the specific columns, filters, or logic being changed in the current prompt — not just general table health.

Example:

  • ✅ "Given 34 downstream models depend on is_paying_workspace, adding 'MC Internal' to the exclusion list will exclude these workspaces from all downstream health scores and exports. Confirm?"
  • ❌ "Workflow 4 already ran. Making the edit now."

The only exception: if the user explicitly acknowledges the risk and confirms they want to skip (e.g. "I know the risks, just make the change") — proceed but note the skipped assessment.

Available MCP tools

All tools are available via the monte-carlo MCP server.

ToolPurpose
testConnectionVerify auth and connectivity
searchFind tables/assets by name
getTableSchema, stats, metadata for a table
getAssetLineageUpstream/downstream dependencies (call with mcons array + direction)
getAlertsActive incidents and alerts
getMonitorsMonitor configs — filter by table using mcons array
getQueriesForTableRecent query history
getQueryDataFull SQL for a specific query
createValidationMonitorMacGenerate validation monitors-as-code YAML
createMetricMonitorMacGenerate metric monitors-as-code YAML
createComparisonMonitorMacGenerate comparison monitors-as-code YAML
createCustomSqlMonitorMacGenerate custom SQL monitors-as-code YAML
getValidationPredicatesList available validation rule types
updateAlertUpdate alert status/severity
setAlertOwnerAssign alert ownership
createOrUpdateAlertCommentAdd comments to alerts
getAudiencesList notification audiences
getDomainsList MC domains
getUserCurrent user info
getCurrentTimeISO timestamp for API calls
Show full SKILL.md (629 more words)Show less

Core workflows

Each workflow has detailed step-by-step instructions in references/workflows.md (Read tool).

1. Table health check

When: User opens a dbt model or mentions a table. What: Surfaces health, lineage, alerts, and risk signals. Auto-escalates to Workflow 4 if change intent is detected and risk signals are present.

2. Add a monitor

When: New column, filter, or business rule is added to a model. What: Suggests and generates monitors-as-code YAML using the appropriate create*MonitorMac tool. Saves to monitors/<table_name>.yml.

3. Alert triage

When: User is investigating an active data quality incident. What: Lists open alerts, checks table state, traces lineage for root cause, reviews recent queries.

4. Change impact assessment — REQUIRED before modifying a model

When: Any intent to modify a dbt model's logic, columns, joins, or filters. What: Surfaces blast radius, downstream dependencies, active incidents, monitor coverage, and query exposure. Produces a risk-tiered report with synthesis connecting findings to specific code recommendations. See references/workflows.md for the full assessment sequence, report format, and synthesis rules.

5. Change validation queries

When: Explicit engineer request only (e.g. "validate this change", "ready to commit"). What: Generates 3-5 targeted SQL queries to verify the change behaved as intended. Uses Workflow 4 context — requires both impact assessment and file edit in session.


Post-synthesis confirmation rules

Always end the synthesis with one clear, specific recommendation in plain English: "Given the above, I recommend: [specific action]"

If the risk is High or Medium: STOP and wait for confirmation before editing any file. You must ask the engineer and receive an explicit "yes", "go ahead", "proceed", or similar confirmation before making code changes. Say: "Do you want me to proceed with the edit?" Do NOT say: "Proceeding with the edit." — that skips the engineer's decision.

If the risk is Low: Use your judgment based on the synthesis findings. If the change is straightforward and the synthesis found no concerns, you may proceed. If anything is surprising or worth flagging, ask before editing.


Session markers

These markers coordinate between the skill and the plugin's hooks. Output each on its own line when the condition is met.

Impact check complete

After the engineer confirms (High/Medium) or after presenting the synthesis (Low), output one marker per assessed table. IMPORTANT: use only the table/model name, not the full MCON:

<!-- MC_IMPACT_CHECK_COMPLETE: <table_name> -->

(Use the model filename without .sql extension — NOT "acme.analytics.orders" or "prod.public.client_hub")

How many markers to emit depends on how the assessment was triggered:

Hook-triggered (the pre-edit hook blocked an edit and instructed you to run the assessment): Be strict — only emit markers for tables whose lineage and monitor coverage were fetched directly via Monte Carlo tools in this session. If the engineer describes changes to multiple tables but only one was formally assessed, emit only one marker. The pre-edit hook will gate the other tables and prompt for their own Workflow 4 runs.

Voluntarily invoked (the engineer proactively asked for an impact assessment): Be looser — emit markers for all tables the assessment meaningfully covered, even if some were assessed via lineage context rather than direct MC tool calls. The engineer is already safety-conscious; don't force redundant assessments for tables they clearly considered.

Monitor coverage gap

When Workflow 4 finds zero custom monitors on a table's affected columns, output:

<!-- MC_MONITOR_GAP: <table_name> -->

Use only the table/model name (NOT the full MCON). This allows the plugin's hooks to remind the engineer about monitor coverage at commit time. Only output this marker when the gap is specifically about the columns or logic being changed — not for general table-level monitor absence.

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 (references) in skills/monte-carlo-prevent of sickn33/agentic-awesome-skills.

  • SKILL.md
  • references/TROUBLESHOOTING.md
  • references/parameters.md
  • references/workflows.md

Open the folder on GitHubat commit b84d35a

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 Prevent 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 Prevent compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Monte Carlo Prevent this skillsickn33/agentic-awesome-skills47k1 repos~3.3kAutomated safety check: PassMIT
dbt Model BuilderAltimateAI/data-engineering-skills128—~890Automated safety check: PassMIT
dbt Error DebuggingAltimateAI/data-engineering-skills128—~1.1kAutomated safety check: PassMIT
Analytics Engineerborghei/Claude-Skills891—~3.4kAutomated safety check: PassMIT
Migrating SQL To DbtAltimateAI/data-engineering-skills128—~762Automated safety check: PassMIT
Databricks JobsKilo-Org/kilo-marketplace1901 repos~3.1kAutomated safety check: PassCustom licence

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Works with

Questions about Monte Carlo Prevent

What does Monte Carlo Prevent do?

Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits. Monte Carlo Prevent is an agent skill from sickn33/agentic-awesome-skills. Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.

When should I use Monte Carlo Prevent?

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

How do I install Monte Carlo Prevent in Claude Code?

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

How do I install Monte Carlo Prevent in Codex?

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

Can I use Monte Carlo Prevent 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-prevent -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-prevent, .gemini/skills/monte-carlo-prevent, .github/skills/monte-carlo-prevent and .opencode/skills/monte-carlo-prevent in your project.

What does Monte Carlo Prevent need to run?

SKILL.md names no scripts, command-line tools or credentials: Monte Carlo Prevent is instructions for the agent only.

Does Monte Carlo Prevent access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Monte Carlo Prevent 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. Review the folder before installing.

What licence does Monte Carlo Prevent use?

Monte Carlo Prevent 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 Prevent use?

About 3.3k tokens (SKILL.md is roughly 13k 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 6.4k tokens, read only when the agent opens those files.

What are the alternatives to Monte Carlo Prevent?

Skills that share tags, products or a category with Monte Carlo Prevent: dbt Model Builder (AltimateAI/data-engineering-skills, 128 stars), dbt Error Debugging (AltimateAI/data-engineering-skills, 128 stars), Analytics Engineer (borghei/Claude-Skills, 891 stars) and Migrating SQL To Dbt (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 Prevent?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 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.