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Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases.
Passed 23 of 23 test cases with the skill, and 12 of 23 without it.
and 9 more in the full report
11 fixed12 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which helper script in the project codebase provides automated validation of incoming CSV files against an expected schema before ingestion?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies scripts/validate_csv_schema.py | Missed | Passed |
The instructions your model reads when the skill loads.
This skill provides patterns for getting data INTO systems from external sources.
What is your data source?
├── Cloud Storage (S3, GCS, Azure) → See cloud-storage.md
├── Files (CSV, JSON, Parquet) → See file-formats.md
├── REST/GraphQL APIs → See api-feeds.md
├── Streaming (Kafka, Kinesis) → See streaming-sources.md
├── Legacy Database → See database-migration.md
└── Need full ETL framework → See etl-tools.md
dlt (data load tool) - Modern Python ETL:
import dlt
# Define a source
@dlt.source
def github_source(repo: str):
@dlt.resource(write_disposition="merge", primary_key="id")
def issues():
response = requests.get(f"https://api.github.com/repos/{repo}/issues")
yield response.json()
test cases passed, vs 12 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.