B F monte-carlo-push-ingestion
Expert guide for pushing metadata, lineage, and query logs to Monte Carlo from any data warehouse.
Expert guide for pushing metadata, lineage, and query logs to Monte Carlo from any data warehouse.
As a process F 50 /100 · Will not run — References files that are not bundled: scripts/templates/<warehouse>/
Technical rating
B 87/100
safety, quality, tests
Safety 60% 100
Quality 40% 67
Run on models none yet
Process rating
References files that are not bundled: scripts/templates/<warehouse>/
Tools and files w 18 0
Failures and branches w 10 0
Running it twice w 4 30 the three weakest of ten parameters ·
all ten
This is a copy of a skill from another catalog; the rating counts the canonical one:
monte-carlo-push-ingestion (sickn33/agentic-awesome-skills)
How to improve For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section. The text references files that are not there: add them or drop the references. For the model run — optional
Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite. A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 69. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec warning description-long-hermes description is 98 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)warning missing-ref reference to a missing file: scripts/templates/<warehouse>/note frontmatter-key unknown frontmatter key "risk"note frontmatter-key unknown frontmatter key "source"note frontmatter-key unknown frontmatter key "source_repo"note frontmatter-key unknown frontmatter key "source_type"note frontmatter-key unknown frontmatter key "date_added"note frontmatter-key unknown frontmatter key "tools"
Process rating: all ten parameters 50/100
Will not run. References files that are not bundled: scripts/templates/<warehouse>/
0 Tools and files. 1 referenced file(s) missing: scripts/templates/<warehouse>/0 Failures and branches. Linear process with no failure handling30 Running it twice. 94 mutating operations with no state check40 Result and completion. Does not say what the result is70 When it triggers. States when to use, but not when not to70 Inputs and preconditions. Inputs and preconditions are listed70 Execution cost. Instruction body is 4499 tokens85 Steps. 43 steps, 1 vague phrases100 Consistency. Name and required fields are in place100 Progress reporting. Reports progress
medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothinglow 18 top-level sections: this looks like several domains in one skilllow The response is described with custom markup (5 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals +5 Description has no quoted example phrases that should trigger the skill +4 Description does not say when NOT to use the skill (false activations) +3 Description length 98: 120–800 characters recommended +3 Output format is not stated: the model decides each time -3 3 of 3 scripts are never mentioned in SKILL.md +1 No license +2 Single-language instructions +4 Structure: 23 headings +3 Step-by-step instructions: 43 items +4 Has examples (4 code blocks) +4 Reference files are cited in the instructions (8 of 8) Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.
Tests
— no evals.json
— no spec.yaml
— not run on models
Files · 69 SKILL.md 18K references/anomaly-detection.md 4K references/custom-lineage.md 5K references/direct-http-api.md 5K references/prerequisites.md 4K references/push-lineage.md 5K references/push-metadata.md 5K references/push-query-logs.md 6K references/validation.md 5K scripts/sample_verify.py 14K scripts/templates/bigquery/_safe_paths.py 2K scripts/templates/bigquery/collect_and_push_lineage.py 3K scripts/templates/bigquery/collect_and_push_metadata.py 2K scripts/templates/bigquery/collect_and_push_query_logs.py 3K scripts/templates/bigquery/collect_lineage.py 8K scripts/templates/bigquery/collect_metadata.py 5K scripts/templates/bigquery/collect_query_logs.py 6K scripts/templates/bigquery/push_lineage.py 6K scripts/templates/bigquery/push_metadata.py 6K scripts/templates/bigquery/push_query_logs.py 7K scripts/templates/bigquery-iceberg/_safe_paths.py 2K scripts/templates/bigquery-iceberg/collect_and_push_metadata.py 3K scripts/templates/bigquery-iceberg/collect_and_push_query_logs.py 2K scripts/templates/bigquery-iceberg/collect_metadata.py 9K scripts/templates/bigquery-iceberg/collect_query_logs.py 5K scripts/templates/bigquery-iceberg/push_metadata.py 6K scripts/templates/bigquery-iceberg/push_query_logs.py 7K scripts/templates/databricks/_safe_paths.py 2K scripts/templates/databricks/collect_and_push_lineage.py 3K scripts/templates/databricks/collect_and_push_metadata.py 3K scripts/templates/databricks/collect_and_push_query_logs.py 4K scripts/templates/databricks/collect_lineage.py 9K scripts/templates/databricks/collect_metadata.py 8K scripts/templates/databricks/collect_query_logs.py 8K scripts/templates/databricks/push_lineage.py 7K scripts/templates/databricks/push_metadata.py 6K scripts/templates/databricks/push_query_logs.py 7K scripts/templates/hive/_safe_paths.py 2K scripts/templates/hive/collect_and_push_lineage.py 4K scripts/templates/hive/collect_and_push_metadata.py 4K scripts/templates/hive/collect_and_push_query_logs.py 4K scripts/templates/hive/collect_lineage.py 10K scripts/templates/hive/collect_metadata.py 13K scripts/templates/hive/collect_query_logs.py 9K scripts/templates/hive/push_lineage.py 11K scripts/templates/hive/push_metadata.py 8K scripts/templates/hive/push_query_logs.py 9K scripts/templates/redshift/_safe_paths.py 2K scripts/templates/redshift/collect_and_push_lineage.py 4K scripts/templates/redshift/collect_and_push_metadata.py 3K scripts/templates/redshift/collect_and_push_query_logs.py 5K scripts/templates/redshift/collect_lineage.py 10K scripts/templates/redshift/collect_metadata.py 10K scripts/templates/redshift/collect_query_logs.py 11K scripts/templates/redshift/push_lineage.py 6K scripts/templates/redshift/push_metadata.py 6K scripts/templates/redshift/push_query_logs.py 7K scripts/templates/snowflake/_safe_paths.py 2K scripts/templates/snowflake/collect_and_push_lineage.py 5K scripts/templates/snowflake/collect_and_push_metadata.py 5K
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 systems that power the pull model. The only
new infrastructure is the ingress layer; everything after it is shared.
Frontmatter 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","ingestion","monte-carlo","pycarlo","metadata"]
tools: ["claude","cursor","codex"]
Dates
In catalog since —
Updated in catalog 13 Sept 2026
First seen here 7 Sept 2026
Last analysis 12 Sept 2026, 16:46 UTC
Last seen in crawl 18 h ago
Score history Date Score Safety Qual. 10 Sept 2026 B 87 100 67 7 Sept 2026 B 86 100 64 7 Sept 2026 A 93 100 82