neo4j-kafka-skill: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

Configure and operate the Neo4j Connector for Kafka (sink + source) and the

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Neo4j Kafka Skill

When to Use

Writing Kafka events into Neo4j (sink connector — Cypher, Pattern, CDC, CUD strategies)
Streaming Neo4j changes to Kafka topics (source connector — CDC or query-based)
Querying Neo4j change events natively without Kafka (db.cdc.query)
Configuring Confluent Cloud managed Neo4j sink connector
Setting up schema registry (Avro/JSON Schema) for typed Kafka messages
Enabling exactly-once semantics or dead-letter queue on sink

When NOT to Use

Cypher query authoring → neo4j-cypher-skill
Bulk CSV/JSON file import → neo4j-import-skill
GDS algorithms → neo4j-gds-skill
Live app write patterns → neo4j-cypher-skill

Decision Table — Which connector strategy?

Use case	Strategy
Custom transformation of Kafka payload → graph	Sink: Cypher
Mirror another Neo4j CDC source	Sink: CDC (schema or source-id sub-strategy)
Map Kafka JSON fields to graph nodes/rels with no code	Sink: Pattern
Consume pre-formatted CUD JSON messages	Sink: CUD
Stream all Neo4j changes to Kafka (real-time)	Source: CDC (Neo4j 5.13+ EE/Aura BC/VDC)
Stream specific query results on a schedule	Source: Query
Consume CDC events in-process, no Kafka	Native CDC API (`db.cdc.query`)

Prerequisites

Neo4j Connector for Kafka ≥ 5.5.2 (download from neo4j.com/labs/kafka or Confluent Hub) — 5.5.2 processes incoming messages per partition instead of per topic; 5.5.1 forces static labels in generated queries
Kafka Connect ≥ 3.x or Confluent Platform ≥ 7.x
For CDC source/sink: Neo4j 5.13+ Enterprise Edition, AuraDB Business Critical, or AuraDB VDC
For query source: any Neo4j edition
Java 11+

Core Connection Config (all connectors)

{
  "neo4j.uri": "neo4j+s://your-instance.databases.neo4j.io:7687",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "${file:/opt/secrets.properties:neo4j.password}",
  "neo4j.database": "neo4j"
}

Authentication types: BASIC | BEARER | KERBEROS | CUSTOM | NONE

Never hardcode passwords — use Kafka Connect secrets provider (${file:...} or ${env:...}).

Sink Connector

Strategy 1 — Cypher

Connector auto-prepends UNWIND $events AS __value — write query using __value:

{
  "connector.class": "org.neo4j.connectors.kafka.sink.Neo4jConnector",
  "topics": "person-creates,person-updates",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "secret",
  "neo4j.cypher.topic.person-creates":
    "MERGE (p:Person {id: __value.id}) SET p += __value.properties",
  "neo4j.cypher.topic.person-updates":
    "MATCH (p:Person {id: __value.id}) SET p += __value.properties",
  "neo4j.cypher.bind-value-as": "__value",
  "neo4j.cypher.bind-key-as": "__key",
  "neo4j.cypher.bind-header-as": "__header"
}

MERGE pattern — idempotent upsert:

MERGE (p:Person {id: __value.id})
ON CREATE SET p.createdAt = datetime(), p += __value.properties
ON MATCH  SET p.updatedAt = datetime(), p += __value.properties

Strategy 2 — Pattern

No Cypher needed — map message fields to graph via pattern syntax:

{
  "neo4j.pattern.topic.users": "(:User{!userId, name, email})",
  "neo4j.pattern.topic.friendships":
    "(:User{!userId: from.userId})-[:KNOWS{since}]->(:User{!userId: to.userId})"
}

Pattern rules:

!prop = key property (used for MERGE)
prop: field.path = map from nested message field
* = map all message fields
-prop = exclude property (cannot mix with inclusions)

Strategy 3 — CDC (mirror another Neo4j)

{
  "neo4j.cdc.schema.topics": "neo4j-cdc-events"
}

Or with source-id tracking (stores elementId as property):

{
  "neo4j.cdc.source-id.topics": "neo4j-cdc-events",
  "neo4j.cdc.source-id.label-name": "SourceEvent",
  "neo4j.cdc.source-id.property-name": "sourceId"
}

Exactly-Once Semantics (EOS)

Requires: connector ≥ 5.3.0 (Cypher/Pattern/CDC strategies), connector ≥ 5.3.1 (CUD strategy), Kafka broker EOS support, and a NODE KEY constraint.

Step 1 — Create constraint:

CREATE CONSTRAINT kafka_offset_key IF NOT EXISTS
FOR (n:__KafkaOffset)
REQUIRE (n.strategy, n.topic, n.partition) IS NODE KEY;

Step 2 — Add to connector config:

{
  "neo4j.eos-offset-label": "__KafkaOffset"
}

Without EOS: connector provides at-least-once — write idempotent Cypher (MERGE, not CREATE).

Error Handling / DLQ

{
  "errors.tolerance": "all",
  "errors.log.enable": "true",
  "errors.log.include.messages": "true",
  "errors.deadletterqueue.topic.name": "neo4j-dlq",
  "errors.deadletterqueue.context.headers.enable": "true",
  "errors.deadletterqueue.topic.replication.factor": "3"
}

errors.tolerance=none (default) — stops on first error. Use all + DLQ for production.

Source Connector

CDC-Based Source (recommended, Neo4j 5.13+)

{
  "connector.class": "org.neo4j.connectors.kafka.source.Neo4jConnector",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "secret",
  "neo4j.source-strategy": "CDC",
  "neo4j.start-from": "NOW",
  "neo4j.cdc.poll-interval": "1s",
  "neo4j.cdc.poll-duration": "5s",
  "neo4j.cdc.topic.person-creates.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-creates.patterns.0.operation": "CREATE",
  "neo4j.cdc.topic.person-updates.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-updates.patterns.0.operation": "UPDATE",
  "neo4j.cdc.topic.person-deletes.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-deletes.patterns.0.operation": "DELETE"
}

neo4j.start-from options: NOW | EARLIEST | a specific cursor string

Multiple patterns per topic — indexed 0, 1, 2...:

{
  "neo4j.cdc.topic.all-changes.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.all-changes.patterns.1.pattern": "(:Organization)"
}

Cursor warning: after DB restore from backup, CDC cursors are invalidated. Reconfigure neo4j.start-from.

Query-Based Source (legacy / any edition)

{
  "neo4j.source-strategy": "QUERY",
  "neo4j.query": "MATCH (p:Person) WHERE p.updatedAt > $lastCheck RETURN p.id AS id, p.name AS name, p.updatedAt AS updatedAt",
  "neo4j.query.streaming-property": "updatedAt",
  "neo4j.query.topic": "person-changes",
  "neo4j.query.polling-interval": "5s",
  "neo4j.query.polling-duration": "10s"
}

$lastCheck is auto-injected by connector. neo4j.query.streaming-property must be returned by the query and should be indexed.

Native CDC API (no Kafka required)

Requires: Neo4j 5.13+ Enterprise, AuraDB BC, or AuraDB VDC.

Enable CDC first (self-managed — set in neo4j.conf):

db.cdc.enabled=true

On Aura: enabled by default on eligible tiers.

Cursor Bootstrap

// Get cursor for "right now" — start tracking from this point forward
CALL db.cdc.current() YIELD id RETURN id AS cursor;

// Get earliest available cursor (replay from history start)
CALL db.cdc.earliest() YIELD id RETURN id AS cursor;

Cursors are exclusive: db.cdc.current() does NOT include the transaction it points to.

Query Changes

// All changes since cursor
CALL db.cdc.query($cursor, []) YIELD id, txId, seq, metadata, event
RETURN id, txId, seq, metadata, event
ORDER BY txId, seq;

Filtered — nodes with label Person, CREATE only:

CALL db.cdc.query($cursor, [
  {select: 'n', labels: ['Person'], operation: 'c'}
]) YIELD id, txId, seq, event
RETURN id, event.state.after.properties AS newProps
ORDER BY txId, seq;

Filtered — specific relationship type with property change tracking:

CALL db.cdc.query($cursor, [
  {select: 'r', type: 'KNOWS', changesTo: ['since', 'strength']}
]) YIELD id, txId, seq, event
RETURN id, event.state.before AS before, event.state.after AS after;

Selector Reference

Field	Values	Applies to
`select`	`'e'` (all), `'n'` (nodes), `'r'` (rels)	both
`operation`	`'c'` (create), `'u'` (update), `'d'` (delete)	both
`labels`	`['Label1','Label2']` (node must have ALL)	nodes
`type`	`'REL_TYPE'`	relationships
`elementId`	specific element ID string	both
`key`	`{propName: value}` (requires key constraint)	both
`changesTo`	`['prop1','prop2']` (AND — all must change)	both
`authenticatedUser`	username string	both
`executingUser`	username string	both
`txMetadata`	`{key: value}`	both

Event Structure

{
  id:       STRING,           // cursor for this event (use as next $cursor)
  txId:     INTEGER,          // transaction ID
  seq:      INTEGER,          // ordering within transaction
  metadata: {
    executingUser:     STRING,
    authenticatedUser: STRING,
    captureMode:       STRING,  // "DIFF" or "FULL"
    txStartTime:       DATETIME,
    txCommitTime:      DATETIME,
    txMetadata:        MAP
  },
  event: {
    elementId:  STRING,
    eventType:  STRING,         // "n" or "r"
    operation:  STRING,         // "c", "u", "d"
    labels:     [STRING],       // nodes only
    type:       STRING,         // relationships only
    keys:       MAP,
    state: {
      before: { properties: MAP },  // null on CREATE
      after:  { properties: MAP }   // null on DELETE
    }
  }
}

Cursor-Loop Pattern (Python)

from neo4j import GraphDatabase

driver = GraphDatabase.driver("neo4j+s://...", auth=("neo4j", "password"))

def poll_changes(cursor: str, selectors: list) -> tuple[list, str]:
    records, _, _ = driver.execute_query(
        "CALL db.cdc.query($cursor, $selectors) YIELD id, txId, seq, event "
        "RETURN id, txId, seq, event ORDER BY txId, seq",
        cursor=cursor, selectors=selectors,
        database_="neo4j"
    )
    events = [r.data() for r in records]
    # Advance cursor to last event id; keep current if no events
    next_cursor = events[-1]["id"] if events else cursor
    return events, next_cursor

# Bootstrap
with driver.session(database="neo4j") as s:
    cursor = s.run("CALL db.cdc.current() YIELD id RETURN id").single()["id"]

selectors = [{"select": "n", "labels": ["Person"]}]

import time
while True:
    events, cursor = poll_changes(cursor, selectors)
    for e in events:
        print(e["event"]["operation"], e["event"]["elementId"])
    time.sleep(1)

Confluent Cloud Managed Connector

Confluent Cloud hosts the Neo4j Sink connector as a fully managed service (no JAR upload needed).

Config differences vs self-managed:

No connector.class field — selected in UI/API
Credentials via Confluent Cloud secret manager or direct JSON
Private endpoints supported (AWS PrivateLink, Azure Private Link, GCP PSC)
Managed upgrades — pin connector version explicitly if needed

Required Confluent Cloud fields:

{
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "...",
  "kaf

View source ↗

File metadata

name: neo4j-kafka-skill
description: Configure and operate the Neo4j Connector for Kafka (sink + source) and the
  native Neo4j CDC API. Covers Cypher/Pattern/CUD sink strategies, CDC-based and query-based
  source, exactly-once semantics, DLQ error handling, Confluent Cloud managed connector,
  schema registry (Avro/JSON), and native db.cdc.query cursor-loop patterns (Neo4j 5.13+
  Enterprise/Aura BC/VDC). Use when streaming Kafka events into Neo4j, streaming Neo4j
  changes to Kafka, or querying Neo4j change events without Kafka. Does NOT handle Cypher
  query authoring — use neo4j-cypher-skill. Does NOT handle bulk CSV/file import — use
  neo4j-import-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
allowed-tools: Bash WebFetch
version: 1.0.4

View original text

---
name: neo4j-kafka-skill
description: Configure and operate the Neo4j Connector for Kafka (sink + source) and the
  native Neo4j CDC API. Covers Cypher/Pattern/CUD sink strategies, CDC-based and query-based
  source, exactly-once semantics, DLQ error handling, Confluent Cloud managed connector,
  schema registry (Avro/JSON), and native db.cdc.query cursor-loop patterns (Neo4j 5.13+
  Enterprise/Aura BC/VDC). Use when streaming Kafka events into Neo4j, streaming Neo4j
  changes to Kafka, or querying Neo4j change events without Kafka. Does NOT handle Cypher
  query authoring — use neo4j-cypher-skill. Does NOT handle bulk CSV/file import — use
  neo4j-import-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
allowed-tools: Bash WebFetch
version: 1.0.4
---

# Neo4j Kafka Skill

## When to Use

- Writing Kafka events into Neo4j (sink connector — Cypher, Pattern, CDC, CUD strategies)
- Streaming Neo4j changes to Kafka topics (source connector — CDC or query-based)
- Querying Neo4j change events natively without Kafka (`db.cdc.query`)
- Configuring Confluent Cloud managed Neo4j sink connector
- Setting up schema registry (Avro/JSON Schema) for typed Kafka messages
- Enabling exactly-once semantics or dead-letter queue on sink

## When NOT to Use

- **Cypher query authoring** → `neo4j-cypher-skill`
- **Bulk CSV/JSON file import** → `neo4j-import-skill`
- **GDS algorithms** → `neo4j-gds-skill`
- **Live app write patterns** → `neo4j-cypher-skill`

---

## Decision Table — Which connector strategy?

| Use case | Strategy |
|---|---|
| Custom transformation of Kafka payload → graph | Sink: **Cypher** |
| Mirror another Neo4j CDC source | Sink: **CDC** (schema or source-id sub-strategy) |
| Map Kafka JSON fields to graph nodes/rels with no code | Sink: **Pattern** |
| Consume pre-formatted CUD JSON messages | Sink: **CUD** |
| Stream all Neo4j changes to Kafka (real-time) | Source: **CDC** (Neo4j 5.13+ EE/Aura BC/VDC) |
| Stream specific query results on a schedule | Source: **Query** |
| Consume CDC events in-process, no Kafka | **Native CDC API** (`db.cdc.query`) |

---

## Prerequisites

- Neo4j Connector for Kafka ≥ 5.5.2 (download from [neo4j.com/labs/kafka](https://neo4j.com/labs/kafka/) or Confluent Hub) — 5.5.2 processes incoming messages per partition instead of per topic; 5.5.1 forces static labels in generated queries
- Kafka Connect ≥ 3.x or Confluent Platform ≥ 7.x
- For CDC source/sink: Neo4j 5.13+ Enterprise Edition, AuraDB Business Critical, or AuraDB VDC
- For query source: any Neo4j edition
- Java 11+

---

## Core Connection Config (all connectors)

```json
{
  "neo4j.uri": "neo4j+s://your-instance.databases.neo4j.io:7687",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "${file:/opt/secrets.properties:neo4j.password}",
  "neo4j.database": "neo4j"
}
```

Authentication types: `BASIC` | `BEARER` | `KERBEROS` | `CUSTOM` | `NONE`

Never hardcode passwords — use Kafka Connect secrets provider (`${file:...}` or `${env:...}`).

---

## Sink Connector

### Strategy 1 — Cypher

Connector auto-prepends `UNWIND $events AS __value` — write query using `__value`:

```json
{
  "connector.class": "org.neo4j.connectors.kafka.sink.Neo4jConnector",
  "topics": "person-creates,person-updates",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "secret",
  "neo4j.cypher.topic.person-creates":
    "MERGE (p:Person {id: __value.id}) SET p += __value.properties",
  "neo4j.cypher.topic.person-updates":
    "MATCH (p:Person {id: __value.id}) SET p += __value.properties",
  "neo4j.cypher.bind-value-as": "__value",
  "neo4j.cypher.bind-key-as": "__key",
  "neo4j.cypher.bind-header-as": "__header"
}
```

MERGE pattern — idempotent upsert:
```cypher
MERGE (p:Person {id: __value.id})
ON CREATE SET p.createdAt = datetime(), p += __value.properties
ON MATCH  SET p.updatedAt = datetime(), p += __value.properties
```

### Strategy 2 — Pattern

No Cypher needed — map message fields to graph via pattern syntax:

```json
{
  "neo4j.pattern.topic.users": "(:User{!userId, name, email})",
  "neo4j.pattern.topic.friendships":
    "(:User{!userId: from.userId})-[:KNOWS{since}]->(:User{!userId: to.userId})"
}
```

Pattern rules:
- `!prop` = key property (used for MERGE)
- `prop: field.path` = map from nested message field
- `*` = map all message fields
- `-prop` = exclude property (cannot mix with inclusions)

### Strategy 3 — CDC (mirror another Neo4j)

```json
{
  "neo4j.cdc.schema.topics": "neo4j-cdc-events"
}
```

Or with source-id tracking (stores elementId as property):
```json
{
  "neo4j.cdc.source-id.topics": "neo4j-cdc-events",
  "neo4j.cdc.source-id.label-name": "SourceEvent",
  "neo4j.cdc.source-id.property-name": "sourceId"
}
```

### Exactly-Once Semantics (EOS)

Requires: connector ≥ 5.3.0 (Cypher/Pattern/CDC strategies), connector ≥ 5.3.1 (CUD strategy), Kafka broker EOS support, and a NODE KEY constraint.

Step 1 — Create constraint:
```cypher
CREATE CONSTRAINT kafka_offset_key IF NOT EXISTS
FOR (n:__KafkaOffset)
REQUIRE (n.strategy, n.topic, n.partition) IS NODE KEY;
```

Step 2 — Add to connector config:
```json
{
  "neo4j.eos-offset-label": "__KafkaOffset"
}
```

Without EOS: connector provides at-least-once — write idempotent Cypher (MERGE, not CREATE).

### Error Handling / DLQ

```json
{
  "errors.tolerance": "all",
  "errors.log.enable": "true",
  "errors.log.include.messages": "true",
  "errors.deadletterqueue.topic.name": "neo4j-dlq",
  "errors.deadletterqueue.context.headers.enable": "true",
  "errors.deadletterqueue.topic.replication.factor": "3"
}
```

`errors.tolerance=none` (default) — stops on first error. Use `all` + DLQ for production.

---

## Source Connector

### CDC-Based Source (recommended, Neo4j 5.13+)

```json
{
  "connector.class": "org.neo4j.connectors.kafka.source.Neo4jConnector",
  "neo4j.uri": "neo4j+s://...",
  "neo4j.authentication.type": "BASIC",
  "neo4j.authentication.basic.username": "neo4j",
  "neo4j.authentication.basic.password": "secret",
  "neo4j.source-strategy": "CDC",
  "neo4j.start-from": "NOW",
  "neo4j.cdc.poll-interval": "1s",
  "neo4j.cdc.poll-duration": "5s",
  "neo4j.cdc.topic.person-creates.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-creates.patterns.0.operation": "CREATE",
  "neo4j.cdc.topic.person-updates.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-updates.patterns.0.operation": "UPDATE",
  "neo4j.cdc.topic.person-deletes.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.person-deletes.patterns.0.operation": "DELETE"
}
```

`neo4j.start-from` options: `NOW` | `EARLIEST` | a specific cursor string

Multiple patterns per topic — indexed 0, 1, 2...:
```json
{
  "neo4j.cdc.topic.all-changes.patterns.0.pattern": "(:Person)",
  "neo4j.cdc.topic.all-changes.patterns.1.pattern": "(:Organization)"
}
```

Cursor warning: after DB restore from backup, CDC cursors are invalidated. Reconfigure `neo4j.start-from`.

### Query-Based Source (legacy / any edition)

```json
{
  "neo4j.source-strategy": "QUERY",
  "neo4j.query": "MATCH (p:Person) WHERE p.updatedAt > $lastCheck RETURN p.id AS id, p.name AS name, p.updatedAt AS updatedAt",
  "neo4j.query.streaming-property": "updatedAt",
  "neo4j.query.topic": "person-changes",
  "neo4j.query.polling-interval": "5s",
  "neo4j.query.polling-duration": "10s"
}
```

`$lastCheck` is auto-injected by connector. `neo4j.query.streaming-property` must be returned by the query and should be indexed.

---

## Native CDC API (no Kafka required)

Requires: Neo4j 5.13+ Enterprise, AuraDB BC, or AuraDB VDC.

Enable CDC first (self-managed — set in neo4j.conf):
```
db.cdc.enabled=true
```
On Aura: enabled by default on eligible tiers.

### Cursor Bootstrap

```cypher
// Get cursor for "right now" — start tracking from this point forward
CALL db.cdc.current() YIELD id RETURN id AS cursor;

// Get earliest available cursor (replay from history start)
CALL db.cdc.earliest() YIELD id RETURN id AS cursor;
```

Cursors are exclusive: `db.cdc.current()` does NOT include the transaction it points to.

### Query Changes

```cypher
// All changes since cursor
CALL db.cdc.query($cursor, []) YIELD id, txId, seq, metadata, event
RETURN id, txId, seq, metadata, event
ORDER BY txId, seq;
```

Filtered — nodes with label Person, CREATE only:
```cypher
CALL db.cdc.query($cursor, [
  {select: 'n', labels: ['Person'], operation: 'c'}
]) YIELD id, txId, seq, event
RETURN id, event.state.after.properties AS newProps
ORDER BY txId, seq;
```

Filtered — specific relationship type with property change tracking:
```cypher
CALL db.cdc.query($cursor, [
  {select: 'r', type: 'KNOWS', changesTo: ['since', 'strength']}
]) YIELD id, txId, seq, event
RETURN id, event.state.before AS before, event.state.after AS after;
```

### Selector Reference

| Field | Values | Applies to |
|---|---|---|
| `select` | `'e'` (all), `'n'` (nodes), `'r'` (rels) | both |
| `operation` | `'c'` (create), `'u'` (update), `'d'` (delete) | both |
| `labels` | `['Label1','Label2']` (node must have ALL) | nodes |
| `type` | `'REL_TYPE'` | relationships |
| `elementId` | specific element ID string | both |
| `key` | `{propName: value}` (requires key constraint) | both |
| `changesTo` | `['prop1','prop2']` (AND — all must change) | both |
| `authenticatedUser` | username string | both |
| `executingUser` | username string | both |
| `txMetadata` | `{key: value}` | both |

### Event Structure

```
{
  id:       STRING,           // cursor for this event (use as next $cursor)
  txId:     INTEGER,          // transaction ID
  seq:      INTEGER,          // ordering within transaction
  metadata: {
    executingUser:     STRING,
    authenticatedUser: STRING,
    captureMode:       STRING,  // "DIFF" or "FULL"
    txStartTime:       DATETIME,
    txCommitTime:      DATETIME,
    txMetadata:        MAP
  },
  event: {
    elementId:  STRING,
    eventType:  STRING,         // "n" or "r"
    operation:  STRING,         // "c", "u", "d"
    labels:     [STRING],       // nodes only
    type:       STRING,         // relationships only
    keys:       MAP,
    state: {
      before: { properties: MAP },  // null on CREATE
      after:  { properties: MAP }   // null on DELETE
    }
  }
}
```

### Cursor-Loop Pattern (Python)

```python
from neo4j import GraphDatabase

driver = GraphDatabase.driver("neo4j+s://...", auth=("neo4j", "password"))

def poll_changes(cursor: str, selectors: list) -> tuple[list, str]:
    records, _, _ = driver.execute_query(
        "CALL db.cdc.query($cursor, $selectors) YIELD id, txId, seq, event "
        "RETURN id, txId, seq, event ORDER BY txId, seq",
        cursor=cursor, selectors=selectors,
        database_="neo4j"
    )
    events = [r.data() for r in records]
    # Advance cursor to last event id; keep current if no events
    next_cursor = events[-1]["id"] if events else cursor
    return events, next_cursor

# Bootstrap
with driver.session(database="neo4j") as s:
    cursor = s.run("CALL db.cdc.current() YIELD id RETURN id").single()["id"]

selectors = [{"select": "n", "labels": ["Person"]}]

import time
while True:
    events, cursor = poll_changes(cursor, selectors)
    for e in events:
        print(e["event"]["operation"], e["event"]["elementId"])
    time.sleep(1)
```

---

## Confluent Cloud Managed Connector

Confluent Cloud hosts the Neo4j Sink connector as a fully managed service (no JAR upload needed).

Config differences vs self-managed:
- No `connector.class` field — selected in UI/API
- Credentials via Confluent Cloud secret manager or direct JSON
- Private endpoints supported (AWS PrivateLink, Azure Private Link, GCP PSC)
- Managed upgrades — pin connector version explicitly if needed

Required Confluent Cloud fields:
```json
{
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "...",
  "kaf

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & trust

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

License: MIT
Version: 1.0.4
Last GitHub push: Aug 31, 2026
Registry updated: Sep 7, 2026
Instruction path: neo4j-kafka-skill/SKILL.md @ a9a5e783c506

Version reported in registry metadata; check source releases before relying on it.

Quality

67/100

Promising

Trust

61/100

Sandbox only

Audit

76/100

Needs review

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More details

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    "Dependency or permission surface needs review",
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}

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Creator: neo4j-contrib
Source: neo4j-contrib/neo4j-skills
Indexed by: OpenAgentSkill community index

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