AI Agent & Automation Workflows

Build a Connection Auth Rules for a Monte Carlo connection type. Fetches live connector schemas and transform steps from the apollo-agent repo.

Investigate data incidents and find root causes using Monte Carlo's observability data.

Check the health of a data table/asset using Monte Carlo.

Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.

Create, edit, validate, and import Monitors-as-Code YAML files. CLI-first; falls back to MC MCP tools, then manual validation.

Analyze data coverage, create monitors for warehouse tables and AI agents. Covers coverage gaps, use-case analysis, data monitor creation, and agent observability.

Automatically activates when a dbt model, SQL file, or table is referenced. Surfaces Monte Carlo context — table health, active alerts, lineage, blast radius — before any code is written, and uses those findings to shape code recommendations. Generates and optionally deploys monitors for new transformation logic. After a change is made, generates targeted SQL validation queries to verify the change behaved as intended. Do not wait to be asked: run the appropriate workflow as soon as a relevant file or table is referenced.

Guide users from coverage analysis to monitor creation. USE WHEN user asks what should I monitor, where are my gaps, improve coverage, or wants a systematic approach to monitoring across their data estate.

Reinforces an AI agent by turning Monte Carlo's agent-health diagnosis into code fixes. Reads the daily health report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-agent).

Analyze a warehouse for stale, unused, or redundant tables via the analyzestoragecosts MCP tool.

Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).

Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocationid tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.

Authors or extends a skill in mc-agent-toolkit. Gates for forbidden buckets and name collisions, applies CONTRIBUTING's extend-or-split rules, then edits a peer skill or hands off to Anthropic's skill-creator and walks the registration checklist.

Analyze a Monte Carlo monitor and recommend configuration changes to reduce alert noise. Supports metric, custom SQL, and validation monitors. Fetches the report, identifies patterns, and suggests tuning.