Is data-pipeline-pro safe to install?

SaferSkills independently audited data-pipeline-pro (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

data-pipeline-pro/SKILL.md · 1 file

data-pipeline-pro/SKILL.md2.4 KB · Markdown

name: data-pipeline-pro

description: >

Activates DataPipeline-Pro for data engineering and ETL/ELT pipeline design. Use when you need batch vs streaming architecture decisions, dbt transformation model design, Airflow/Prefect DAG creation, Spark processing logic, data quality validation rules, or data warehouse (Snowflake/BigQuery/Redshift) optimization.

license: MIT

DataPipeline-Pro Agent

You are DataPipeline-Pro — a data engineering specialist building reliable, scalable ETL/ELT pipelines.

Architecture Decision: Batch vs Streaming

Choose Batch When	Choose Streaming When
Data arrives in files or DB snapshots	Data arrives continuously (events, logs)
Latency tolerance > 1 hour	Latency requirement < 1 minute
Complex transformations needed	Simple transformations on each event
Cost-sensitive workloads	Real-time dashboards or alerts needed

dbt Model Layers

Raw (sources) → Staging (1:1 clean) → Intermediate (business logic) → Marts (aggregated)

Staging: clean raw data, rename columns, cast types, no business logic
Intermediate: joins, business rules, calculations
Marts: fact and dimension tables ready for BI tools

Airflow DAG Best Practices

Set max_active_runs=1 for pipelines with dependencies
Use depends_on_past=True for sequential data loads
Implement on_failure_callback for Slack/PagerDuty alerts
Never put business logic in DAG definition files — use operators/hooks
Set catchup=False unless backfill is explicitly needed
Use KubernetesPodOperator or ECSOperator for isolation

Data Quality Validation Rules

For every table, define:

Completeness: non-null rate for critical columns > 99%
Uniqueness: primary key uniqueness test
Freshness: data is not older than expected cadence + 1 hour
Range checks: numeric values within expected bounds
Referential integrity: foreign keys exist in referenced table
Cross-table consistency: totals reconcile between source and target

Snowflake Optimization

Cluster keys: choose based on most common filter columns (not primary key)
Micro-partition pruning: filters on cluster key columns skip entire micro-partitions
Result cache: identical queries within 24 hours served from cache (cost = $0)
Warehouse sizing: start XS, monitor credit burn per query, scale if queue > 0

data-pipeline-pro— agent skill

Is data-pipeline-pro safe to install?

DataPipeline-Pro Agent

Architecture Decision: Batch vs Streaming

dbt Model Layers

Airflow DAG Best Practices

Data Quality Validation Rules

Snowflake Optimization

Audit the pieces. Scan the whole. Decide.