dbt Model Builder
AltimateAI/data-engineering-skills
Creates or modifies dbt models in line with a project's own conventions, then runs dbt build and dbt show to check the output instead of stopping at compile.
Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-prevent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-prevent --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "monte-carlo-prevent" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-prevent into .claude/skills/monte-carlo-prevent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-prevent", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-preventType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-prevent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-prevent --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/monte-carlo-prevent .agents/skills/monte-carlo-prevent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "monte-carlo-prevent" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-prevent into .agents/skills/monte-carlo-prevent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-prevent", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-prevent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-prevent --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/monte-carlo-prevent .cursor/skills/monte-carlo-prevent && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "monte-carlo-prevent" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-prevent into .cursor/skills/monte-carlo-prevent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-prevent", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/monte-carlo-prevent--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-prevent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-prevent --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/monte-carlo-prevent .gemini/skills/monte-carlo-prevent && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "monte-carlo-prevent" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-prevent into .gemini/skills/monte-carlo-prevent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-prevent", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-preventInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-prevent -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/monte-carlo-prevent .github/skills/monte-carlo-prevent && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "monte-carlo-prevent" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-prevent into .github/skills/monte-carlo-prevent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-prevent", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-prevent -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills monte-carlo-prevent --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/monte-carlo-prevent .opencode/skills/monte-carlo-prevent && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "monte-carlo-prevent" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/monte-carlo-prevent into .opencode/skills/monte-carlo-prevent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "monte-carlo-prevent", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
monte-carlo-preventSurfaces 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.
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.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 1,589 words, ~3,257 tokens.
.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.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:
references/workflows.md (relative to this file)references/parameters.md (relative to this file)references/TROUBLESHOOTING.md (relative to this file)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.
Do not invoke Monte Carlo tools for:
If uncertain whether a file is a dbt model, check for {{ ref() }} or {{ source() }} Jinja references — if absent, do not activate.
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.
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:
<column> column"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.
Before calling Edit, Write, or MultiEdit on any .sql or dbt model
file, you MUST check:
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:
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.
All tools are available via the monte-carlo MCP server.
| Tool | Purpose |
|---|---|
testConnection | Verify auth and connectivity |
search | Find tables/assets by name |
getTable | Schema, stats, metadata for a table |
getAssetLineage | Upstream/downstream dependencies (call with mcons array + direction) |
getAlerts | Active incidents and alerts |
getMonitors | Monitor configs — filter by table using mcons array |
getQueriesForTable | Recent query history |
getQueryData | Full SQL for a specific query |
createValidationMonitorMac | Generate validation monitors-as-code YAML |
createMetricMonitorMac | Generate metric monitors-as-code YAML |
createComparisonMonitorMac | Generate comparison monitors-as-code YAML |
createCustomSqlMonitorMac | Generate custom SQL monitors-as-code YAML |
getValidationPredicates | List available validation rule types |
updateAlert | Update alert status/severity |
setAlertOwner | Assign alert ownership |
createOrUpdateAlertComment | Add comments to alerts |
getAudiences | List notification audiences |
getDomains | List MC domains |
getUser | Current user info |
getCurrentTime | ISO timestamp for API calls |
Each workflow has detailed step-by-step instructions in references/workflows.md (Read tool).
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.
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.
When: User is investigating an active data quality incident. What: Lists open alerts, checks table state, traces lineage for root cause, reviews recent queries.
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.
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.
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.
These markers coordinate between the skill and the plugin's hooks. Output each on its own line when the condition is met.
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.
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.
© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (references) in skills/monte-carlo-prevent of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit b84d35a
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Monte Carlo Prevent this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.3k | Automated safety check: Pass | MIT | |
| dbt Model BuilderAltimateAI/data-engineering-skills | 128 | — | ~890 | Automated safety check: Pass | MIT | |
| dbt Error DebuggingAltimateAI/data-engineering-skills | 128 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Analytics Engineerborghei/Claude-Skills | 891 | — | ~3.4k | Automated safety check: Pass | MIT | |
| Migrating SQL To DbtAltimateAI/data-engineering-skills | 128 | — | ~762 | Automated safety check: Pass | MIT | |
| Databricks JobsKilo-Org/kilo-marketplace | 190 | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence |
AltimateAI/data-engineering-skills
Creates or modifies dbt models in line with a project's own conventions, then runs dbt build and dbt show to check the output instead of stopping at compile.
AltimateAI/data-engineering-skills
Walks through fixing dbt compilation, database and test errors: read the full error, check upstream models, apply a fix, then verify with dbt build and a data preview.
borghei/Claude-Skills
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
AltimateAI/data-engineering-skills
Converts legacy SQL to modular dbt models. An agent skill from AltimateAI/data-engineering-skills.
Kilo-Org/kilo-marketplace
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI.
mohitagw15856/pm-claude-skills
Design the data quality checks for a table or pipeline across the standard dimensions.
sickn33/agentic-awesome-skills
Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.
sickn33/agentic-awesome-skills
Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.
sickn33/agentic-awesome-skills
Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.
sickn33/agentic-awesome-skills
Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.
sickn33/agentic-awesome-skills
Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.
sickn33/agentic-awesome-skills
Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.
Categories
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.
Monte Carlo Prevent fits situations like: tasks that involve Data pipelines and ETL; tasks that involve Data governance; tasks that involve SQL.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Monte Carlo Prevent is instructions for the agent only.
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.
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.
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.
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.
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.
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.