Babysitter CDC Pattern Implementer
Implements Change Data Capture patterns for real-time data integration
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/data-engineering-analytics/skills/cdc-pattern-implementer" ~/.claude/skills/a5c-ai-babysitter-cdc-pattern-implementer && rm -rf "$T"
manifest:
library/specializations/data-engineering-analytics/skills/cdc-pattern-implementer/SKILL.mdsource content
CDC Pattern Implementer
Overview
Implements Change Data Capture patterns for real-time data integration. This skill provides expertise in CDC configuration and implementation across various database and streaming platforms.
Capabilities
- Debezium connector configuration
- CDC pattern selection (log-based, trigger-based, timestamp-based)
- Initial snapshot strategy
- Schema change handling
- Exactly-once delivery configuration
- Sink connector setup
- Tombstone handling
- CDC monitoring setup
Input Schema
{ "sourceDatabase": { "type": "postgres|mysql|oracle|sqlserver", "connection": "object" }, "tables": ["string"], "targetSystem": "kafka|kinesis|pubsub", "requirements": { "latencyMs": "number", "exactlyOnce": "boolean" } }
Output Schema
{ "connectorConfig": "object", "snapshotStrategy": "object", "schemaConfig": "object", "monitoringConfig": "object", "documentation": "string" }
Target Processes
- ETL/ELT Pipeline
- Streaming Pipeline
- Data Warehouse Setup
Usage Guidelines
- Identify source database and tables for CDC
- Define target streaming system
- Specify latency and delivery guarantees
- Configure appropriate snapshot strategy for initial load
Best Practices
- Use log-based CDC when possible for minimal source impact
- Plan initial snapshot strategy carefully for large tables
- Implement proper error handling and dead letter queues
- Monitor replication lag and connector health
- Test schema evolution handling before production