Agent skill · Data & Analytics

monte-carlo-push-ingestion

Expert guide for pushing metadata, lineage, and query logs to Monte Carlo from any data warehouse.

Nick44,086★ · +407/wk · 1 repos on radarProfile →
claude-codecodexcursorships scriptsMIT
Install
npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-push-ingestion --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 69
SKILL.md size: 18 KB
Bundled scripts: yes
Declared author: monte-carlo-data
Path: skills/monte-carlo-push-ingestion/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 44,414 · +328 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Monte Carlo Push Ingestion You are an agent that helps customers collect metadata, lineage, and query logs from their data warehouses and push that data to Monte Carlo via the push ingestion API. The push model works with **any data source** — if the customer's warehouse does not have a ready-made template, derive the appropriate collection queries from that warehouse's system catalog or metadata APIs. The push format and pycarlo SDK calls are the same regardless of source. Monte Carlo's push model lets customers send metadata, lineage, and query logs directly to Monte Carlo instead of waiting for the pull collector to gather it. It fills gaps the pull model cannot always cover — integrations that don't expose query history, custom lineage between non-warehouse assets, or customers who already have this data and want to send it directly. ## When to Use Use this skill when the user needs to collect metadata, lineage, freshness, volume, or query-log data from a warehouse or adjacent system and push it into Monte Carlo through the push-ingestion API. Push data travels through the integration gateway → dedicated Kinesis streams → thin adapter/normalizer code → the same downstream sys

What's inside
Steps it walks through
  1. When to Use
  2. MANDATORY — Always start from templates
  3. Canonical pycarlo API — authoritative reference
  4. Imports and client setup
  5. Method signatures
  6. RelationalAsset structure (nested, NOT flat)
  7. Environment variable conventions
  8. What this skill can build for you
  9. Ready-to-run examples
  10. Reference docs — when to load
  11. Prerequisites — read this first
  12. What you can push
  13. Step 1 — Generate your collection scripts
  14. Step 2 — Validate pushed data
Ships with 24 files
  • references/anomaly-detection.md
  • references/custom-lineage.md
  • references/direct-http-api.md
  • references/prerequisites.md
  • references/push-lineage.md
  • references/push-metadata.md
  • references/push-query-logs.md
  • references/validation.md
  • scripts/sample_verify.py
  • scripts/templates/bigquery-iceberg/_safe_paths.py
  • scripts/templates/bigquery-iceberg/collect_and_push_metadata.py
  • scripts/templates/bigquery-iceberg/collect_and_push_query_logs.py
  • scripts/templates/bigquery-iceberg/collect_metadata.py
  • scripts/templates/bigquery-iceberg/collect_query_logs.py
  • scripts/templates/bigquery-iceberg/push_metadata.py
  • scripts/templates/bigquery-iceberg/push_query_logs.py
  • scripts/templates/bigquery/_safe_paths.py
  • scripts/templates/bigquery/collect_and_push_lineage.py
  • scripts/templates/bigquery/collect_and_push_metadata.py
  • scripts/templates/bigquery/collect_and_push_query_logs.py
  • scripts/templates/bigquery/collect_lineage.py
  • scripts/templates/bigquery/collect_metadata.py
  • scripts/templates/bigquery/collect_query_logs.py
  • scripts/templates/bigquery/push_lineage.py
first 24 of 69
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About this skill
What does the monte-carlo-push-ingestion skill do?

Expert guide for pushing metadata, lineage, and query logs to Monte Carlo from any data warehouse.

How do I install it?

Run `npx skills add sickn33/agentic-awesome-skills --skill monte-carlo-push-ingestion --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From sickn33/agentic-awesome-skills, a repository with 44,414 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going