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From antigravity-awesome-skills
Surfaces Monte Carlo data observability context including table health, alerts, lineage, and blast radius before dbt model or SQL edits.
How this skill is triggered — by the user, by Claude, or both
Slash command
/antigravity-awesome-skills:monte-carlo-preventThe summary Claude sees in its skill listing — used to decide when to auto-load this 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.
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:
(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:
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.
npx claudepluginhub sickn33/antigravity-awesome-skills --plugin antigravity-awesome-skillsSurfaces Monte Carlo data observability context—table health, alerts, lineage, blast radius—before modifying SQL/dbt models, helping prevent incidents.
Builds and modifies dbt models with SQL transformations using ref() and source(), creates tests, validates results with dbt show. For dbt projects: modeling, debugging errors, data exploration, testing, change evaluation.
Manage data quality in DataHub: create and run assertions, check outcomes, raise/resolve incidents, and diagnose health problems across your data estate.