CDC Pattern Implementer
Implements Change Data Capture patterns for real-time data integration
Best use case
CDC Pattern Implementer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Implements Change Data Capture patterns for real-time data integration
Teams using CDC Pattern Implementer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cdc-pattern-implementer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How CDC Pattern Implementer Compares
| Feature / Agent | CDC Pattern Implementer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implements Change Data Capture patterns for real-time data integration
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```json
{
"sourceDatabase": {
"type": "postgres|mysql|oracle|sqlserver",
"connection": "object"
},
"tables": ["string"],
"targetSystem": "kafka|kinesis|pubsub",
"requirements": {
"latencyMs": "number",
"exactlyOnce": "boolean"
}
}
```
## Output Schema
```json
{
"connectorConfig": "object",
"snapshotStrategy": "object",
"schemaConfig": "object",
"monitoringConfig": "object",
"documentation": "string"
}
```
## Target Processes
- ETL/ELT Pipeline
- Streaming Pipeline
- Data Warehouse Setup
## Usage Guidelines
1. Identify source database and tables for CDC
2. Define target streaming system
3. Specify latency and delivery guarantees
4. 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 productionRelated Skills
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