Chdb Datastore
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
A skill your agent uses when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x…
$ npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install neo4j-contrib/neo4j-skills neo4j-spark-skill --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/neo4j-spark-skill .claude/skills/neo4j-spark-skill && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "neo4j-spark-skill" agent skill from https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill into .claude/skills/neo4j-spark-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neo4j-spark-skill", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skillType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install neo4j-contrib/neo4j-skills neo4j-spark-skill --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/neo4j-spark-skill .agents/skills/neo4j-spark-skill && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "neo4j-spark-skill" agent skill from https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill into .agents/skills/neo4j-spark-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neo4j-spark-skill", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install neo4j-contrib/neo4j-skills neo4j-spark-skill --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/neo4j-spark-skill .cursor/skills/neo4j-spark-skill && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "neo4j-spark-skill" agent skill from https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill into .cursor/skills/neo4j-spark-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neo4j-spark-skill", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/neo4j-contrib/neo4j-skills.git --path neo4j-spark-skill--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install neo4j-contrib/neo4j-skills neo4j-spark-skill --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/neo4j-spark-skill .gemini/skills/neo4j-spark-skill && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "neo4j-spark-skill" agent skill from https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill into .gemini/skills/neo4j-spark-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neo4j-spark-skill", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install neo4j-contrib/neo4j-skills neo4j-spark-skillInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/neo4j-spark-skill .github/skills/neo4j-spark-skill && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "neo4j-spark-skill" agent skill from https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill into .github/skills/neo4j-spark-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neo4j-spark-skill", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install neo4j-contrib/neo4j-skills neo4j-spark-skill --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/neo4j-spark-skill .opencode/skills/neo4j-spark-skill && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "neo4j-spark-skill" agent skill from https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-spark-skill into .opencode/skills/neo4j-spark-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "neo4j-spark-skill", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
neo4j-spark-skillA skill your agent uses when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x…
Neo4j Spark Skill is an agent skill from neo4j-contrib/neo4j-skills. Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT…
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `README.md`, `references/read-patterns.md` and `references/write-patterns.md`).
It sits in Data & Analytics, covering DataFrames. It works with Neo4j, Apache Spark, Databricks and Python. The repository describes itself as: Neo4j Skills for Coding and other Agents including Cypher. The licence is MIT.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit bb30e1f. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
BashWebFetchFrom allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and scala).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Neo4j Spark Skill loads about 4.1k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 178 tokens; SKILL.md has 851 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Bash, WebFetchAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from neo4j-contrib/neo4j-skills at commit bb30e1f, republished under its MIT licence (© neo4j-contrib). 851 words, ~4,051 tokens.
.claude/skills/neo4j-spark-skill/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.neo4j-driver-python-skillneo4j-cypher-skillneo4j-gds-skillneo4j-spring-data-skill| Connector | Spark | Scala | Java | Databricks Runtime | Neo4j | Maven coordinate |
|---|---|---|---|---|---|---|
| 6.0.x | 4.0, 4.1 | 2.13 | 17+ | 17.3 LTS | 5.x, 2025.x, 2026.x | org.neo4j.connectors:spark:6.0.0-s_2.13 |
| 5.5.x / 5.4.x | 3.4, 3.5 | 2.12, 2.13 | 8+ | 14.3–16.4 LTS | 4.4, 5.x, 2025.x, 2026.x | org.neo4j:neo4j-connector-apache-spark_2.13:5.5.0_for_spark_3 |
Group ID changed in 6.0 — org.neo4j:neo4j-connector-apache-spark_<scala> is now a relocation POM pointing at org.neo4j.connectors:spark. On Spark 3.x stay on 5.5.x.
| Change | Migration |
|---|---|
| Spark baseline 3.5 → 4.0/4.1; Scala 2.12 and Java 8–11 dropped | Upgrade to 5.5.0 first, then Spark 4.x + Scala 2.13 + Java 17 |
Maven coordinate org.neo4j.connectors:spark:<version>-s_2.13 | Replace old _for_spark_3 coordinate |
schema.optimization.type removed | schema.optimization.node.keys, schema.optimization.relationship.keys, schema.optimization |
$stream.offset in partitioned reads removed | Use partitions + query.count |
;-separated multi-statement script removed | script.1, script.2, … script.N — executed in numbered order |
relationship.save.strategy default native → keys | Set .option("relationship.save.strategy", "native") explicitly to keep old behaviour |
query option rewritten for Data Source V2 predicate push-down | No action; verify plans on upgrade |
from pyspark.sql import SparkSession
spark = (SparkSession.builder
.appName("neo4j-app")
.config("spark.jars.packages",
"org.neo4j.connectors:spark:6.0.0-s_2.13") # Spark 3.x: org.neo4j:neo4j-connector-apache-spark_2.13:5.5.0_for_spark_3
.config("neo4j.url", "neo4j+s://xxxx.databases.neo4j.io")
.config("neo4j.authentication.type", "basic")
.config("neo4j.authentication.basic.username", "neo4j")
.config("neo4j.authentication.basic.password", "password")
.getOrCreate())val spark = SparkSession.builder
.appName("neo4j-app")
.config("spark.jars.packages",
"org.neo4j.connectors:spark:6.0.0-s_2.13")
.config("neo4j.url", "neo4j+s://xxxx.databases.neo4j.io")
.config("neo4j.authentication.type", "basic")
.config("neo4j.authentication.basic.username", "neo4j")
.config("neo4j.authentication.basic.password", "password")
.getOrCreate()org.neo4j.connectors:spark:6.0.0-s_2.13 on DBR 17.3 LTS; org.neo4j:neo4j-connector-apache-spark_2.13:5.5.0_for_spark_3 on DBR 14.3–16.4 LTSneo4j.url neo4j+s://xxxx.databases.neo4j.io
neo4j.authentication.type basic
neo4j.authentication.basic.username {{secrets/neo4j/username}}
neo4j.authentication.basic.password {{secrets/neo4j/password}}# Store credentials once:
# databricks secrets create-scope --scope neo4j
# databricks secrets put --scope neo4j --key url
# databricks secrets put --scope neo4j --key username
# databricks secrets put --scope neo4j --key password
neo4j_url = dbutils.secrets.get(scope="neo4j", key="url")
neo4j_user = dbutils.secrets.get(scope="neo4j", key="username")
neo4j_pass = dbutils.secrets.get(scope="neo4j", key="password")
spark.conf.set("neo4j.url", neo4j_url)
spark.conf.set("neo4j.authentication.type", "basic")
spark.conf.set("neo4j.authentication.basic.username", neo4j_user)
spark.conf.set("neo4j.authentication.basic.password", neo4j_pass)| Option | Description | Default |
|---|---|---|
neo4j.url | Bolt/Neo4j URI | — (required) |
neo4j.authentication.type | none, basic, kerberos, bearer | basic |
neo4j.authentication.basic.username | Username | driver default |
neo4j.authentication.basic.password | Password | driver default |
neo4j.authentication.bearer.token | Bearer token | — |
neo4j.database | Target database | driver default |
neo4j.access.mode | read or write | read |
neo4j.encryption.enabled | TLS (ignored with +s/+ssc URI) | false |
neo4j.db.transaction.timeout | Transaction timeout (ms) | driver default |
neo4j.db.transaction.metadata.<key> | Custom transaction metadata surfaced in query log [6.0] | empty |
neo4j.authentication.type = supplier name | Custom AuthenticationTokenSupplierFactory (e.g. keycloak via org.neo4j.connectors:commons-authn-keycloak) for expiring OAuth/OIDC tokens | — |
| Option | Effect |
|---|---|
cypher.version | Cypher language version — 5 (default) or 25 |
cypher.tuning.<param> | Emits CYPHER <param>=<value> preamble on every generated query |
Valid with labels, relationship, query on reads and writes; rejected with gds.
df = (spark.read.format("org.neo4j.spark.DataSource")
.option("query", "MATCH (o:Object) RETURN o.id AS id, o.name AS name")
.option("cypher.version", "25")
.option("cypher.tuning.runtime", "parallel") # CYPHER 25 runtime=parallel
.option("db.transaction.metadata.app", "spark-etl") # tags transactions in query.log
.load())Three mutually exclusive read modes — use exactly one per .read() call.
# PySpark
df = (spark.read.format("org.neo4j.spark.DataSource")
.option("labels", ":Person")
.load())
df.printSchema()
df.show()// Scala
val df = spark.read
.format("org.neo4j.spark.DataSource")
.option("labels", ":Person")
.load()Multi-label filter (AND): .option("labels", ":Person:Employee")
Result includes <id> (internal Neo4j id) and <labels> columns.
df = (spark.read.format("org.neo4j.spark.DataSource")
.option("query", "MATCH (p:Person)-[:ACTED_IN]->(m:Movie) RETURN p.name AS actor, m.title AS movie, m.year AS year")
.load())Use explicit RETURN aliases — they become DataFrame column names. No SKIP/LIMIT in query (connector handles pagination).
df = (spark.read.format("org.neo4j.spark.DataSource")
.option("relationship", "BOUGHT")
.option("relationship.source.labels", ":Customer")
.option("relationship.target.labels", ":Product")
.load())Result columns: <rel.id>, <rel.type>, <source.*>, <target.*>, plus relationship properties.
df = (spark.read.format("org.neo4j.spark.DataSource")
.option("labels", ":Transaction")
.option("partitions", "10") # parallel partitions (default: 1)
.option("batch.size", "5000") # rows per partition batch (default: 5000)
.option("schema.flatten.limit", "100") # rows sampled for schema inference
.load())Full read options reference: references/read-patterns.md
| SaveMode | Cypher | Requires |
|---|---|---|
Append | CREATE | nothing extra |
Overwrite | MERGE | node.keys (nodes) or *.node.keys (rels) |
ErrorIfExists | CREATE + error if exists | — |
Always create uniqueness constraints on node.keys properties before writing in Overwrite mode.
from pyspark.sql import Row
people = spark.createDataFrame([
{"name": "Alice", "age": 30},
{"name": "Bob", "age": 25},
])
(people.write.format("org.neo4j.spark.DataSource")
.mode("Append")
.option("labels", ":Person")
.save())(people.write.format("org.neo4j.spark.DataSource")
.mode("Overwrite")
.option("labels", ":Person")
.option("node.keys", "name") # comma-separated; df_col:node_prop if names differ
.save())node.keys with rename: .option("node.keys", "df_col:node_property,id:personId")
import org.apache.spark.sql.SaveMode
peopleDF.write
.format("org.neo4j.spark.DataSource")
.mode(SaveMode.Overwrite)
.option("labels", ":Person")
.option("node.keys", "name")
.save()Use coalesce(1) before relationship writes to avoid deadlocks.
rel_df = spark.createDataFrame([
{"cust_id": "C1", "prod_id": "P1", "qty": 3},
{"cust_id": "C2", "prod_id": "P2", "qty": 1},
])
(rel_df.coalesce(1)
.write.format("org.neo4j.spark.DataSource")
.mode("Append")
.option("relationship", "BOUGHT")
.option("relationship.save.strategy", "keys")
.option("relationship.source.labels", ":Customer")
.option("relationship.source.save.mode", "Match") # require existing nodes
.option("relationship.source.node.keys", "cust_id:id")
.option("relationship.target.labels", ":Product")
.option("relationship.target.save.mode", "Match")
.option("relationship.target.node.keys", "prod_id:id")
.option("relationship.properties", "qty:quantity")
.save())relationship.source.save.mode / relationship.target.save.mode:
Match — find existing nodes (fail if missing)Append — always CREATE new nodesOverwrite — MERGE nodesscript.N runs Cypher once before write operations, in numbered order. Required for index/constraint setup when using query mode (schema.optimization.* rejected there).
(df.write.format("org.neo4j.spark.DataSource")
.mode("Overwrite")
.option("query", "MERGE (p:Person {email: event.email}) SET p.name = event.name")
.option("script.1", "CREATE CONSTRAINT person_email IF NOT EXISTS FOR (p:Person) REQUIRE p.email IS UNIQUE")
.option("script.2", "CREATE INDEX person_name IF NOT EXISTS FOR (p:Person) ON (p.name)")
.option("index.await.timeout", "300") # db.awaitIndexes seconds; 0 disables
.save())script (single statement) and script.N are mutually exclusive. Semicolon-separated statements inside one script fail on 6.0.
Full write options reference: references/write-patterns.md
# Read from Delta table (Unity Catalog or DBFS)
delta_df = spark.read.format("delta").table("catalog.schema.customers")
# Optional: filter/transform in Spark before writing
filtered = delta_df.filter("active = true").select("customer_id", "name", "region")
# Write to Neo4j
(filtered.write.format("org.neo4j.spark.DataSource")
.mode("Overwrite")
.option("labels", ":Customer")
.option("node.keys", "customer_id")
.option("batch.size", "20000")
.save())Pipeline pattern for relationships — load both node sets first, then write edges:
# Step 1: ensure nodes exist
customers_df.write.format("org.neo4j.spark.DataSource").mode("Overwrite") \
.option("labels", ":Customer").option("node.keys", "customer_id").save()
products_df.write.format("org.neo4j.spark.DataSource").mode("Overwrite") \
.option("labels", ":Product").option("node.keys", "product_id").save()
# Step 2: write relationships (single partition)
orders_df.coalesce(1).write.format("org.neo4j.spark.DataSource").mode("Append") \
.option("relationship", "ORDERED") \
.option("relationship.save.strategy", "keys") \
.option("relationship.source.labels", ":Customer") \
.option("relationship.source.save.mode", "Match") \
.option("relationship.source.node.keys", "customer_id:customer_id") \
.option("relationship.target.labels", ":Product") \
.option("relationship.target.save.mode", "Match") \
.option("relationship.target.node.keys", "product_id:product_id") \
.save()| Scenario | Recommendation |
|---|---|
| Node writes (no lock contention) | repartition(N) where N ≤ Neo4j CPU cores |
| Relationship writes (lock risk) | coalesce(1) — single partition |
| Large datasets | batch.size 10000–20000 (adjust to heap) |
| MERGE-heavy loads | Add uniqueness constraint on node.keys properties first |
# Aggressive batch — monitor Neo4j heap; OOM risk above 50k
(big_df.repartition(8)
.write.format("org.neo4j.spark.DataSource")
.mode("Overwrite")
.option("labels", ":Event")
.option("node.keys", "event_id")
.option("batch.size", "20000")
.save())| Error | Cause | Fix |
|---|---|---|
ClassNotFoundException: org.neo4j.spark.DataSource | JAR not on classpath | Add spark.jars.packages or attach library |
| Deadlock on relationship write | Multiple partitions locking nodes | coalesce(1) before write |
| Duplicate nodes on Overwrite | No uniqueness constraint on keys | CREATE CONSTRAINT ON (n:Label) ASSERT n.prop IS UNIQUE |
| OOM on Neo4j side | batch.size too large | Reduce to 5000–10000; check heap |
Schema all string columns | No APOC, schema not sampled | Set schema.flatten.limit higher; or use query mode with explicit types |
Access mode is read error on write | Session opened in read mode | Remove neo4j.access.mode or set to write |
| Databricks Shared cluster fails | Unity Catalog shared mode unsupported | Switch to Single User access mode |
NoSuchMethodError / IncompatibleClassChangeError on Spark 4 | 5.x connector on a Spark 4 runtime | Use org.neo4j.connectors:spark:6.0.0-s_2.13 |
Relationship write ignores rel.* / source.* columns after upgrade | 6.0 default strategy is keys, not native | .option("relationship.save.strategy", "native") |
script option rejected with multiple statements | 6.0 removed ;-separated scripts | Split into script.1, script.2, … |
org.neo4j.connectors:spark:*-s_2.13 for Spark 4.x, org.neo4j:neo4j-connector-apache-spark_<scala>:*_for_spark_3 for Spark 3.xnode.keys set when using Overwrite modenode.keys properties before MERGE writescoalesce(1) applied before relationship writesbatch.size sized to Neo4j heap (start 5000, tune up)query mode: no SKIP/LIMIT in Cypher (connector paginates internally)© neo4j-contrib, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (references) in neo4j-spark-skill of neo4j-contrib/neo4j-skills.
Open the folder on GitHubat commit bb30e1f
Neo4j Spark Skill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Neo4j Spark Skill this skillneo4j-contrib/neo4j-skills | 114 | — | ~4.1k | Automated safety check: Notes | MIT | |
| Chdb Datastorevemetric/vemetric | 395 | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Datafusion Pythonapache/datafusion-python | 607 | — | ~7.8k | Automated safety check: Pass | Apache-2.0 | |
| Spark Python Data Sourcedatabricks/databricks-agent-skills | 345 | — | ~2.1k | Automated safety check: Pass | Custom licence | |
| Spark Version UpgradeOpenHands/extensions | 163 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Polar Python SDKpolarsource/polar | 10k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 |
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
apache/datafusion-python
A skill your agent uses when the user is writing datafusion-python (Apache DataFusion Python bindings) DataFrame or SQL code.
databricks/databricks-agent-skills
Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems.
OpenHands/extensions
Upgrade Apache Spark applications between major versions (2.x→3.x, 3.x→4.x).
polarsource/polar
Integrate Polar billing in server-side Python applications using the versioned Polar and PolarAsync clients.
coffeefuelbump/csv-data-summarizer-claude-skill
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
neo4j-contrib/neo4j-skills
Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance.
neo4j-contrib/neo4j-skills
Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x.
neo4j-contrib/neo4j-skills
Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, AuraGraphDataScience, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, getorcreate, remote graph projection with…
neo4j-contrib/neo4j-skills
Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build.
neo4j-contrib/neo4j-skills
Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API.
neo4j-contrib/neo4j-skills
Neo4j .NET Driver v6 — IDriver lifecycle, DI registration (singleton), ExecutableQuery fluent API, ExecuteReadAsync/ExecuteWriteAsync managed transactions, IResultCursor (FetchAsync/ ToListAsync)…
Works with
Categories
A skill your agent uses when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x…. Neo4j Spark Skill is an agent skill from neo4j-contrib/neo4j-skills.neo4j:neo4j-connector-apache-spark).
Neo4j Spark Skill fits situations like: writing to Neo4j with Apache Spark; databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark); 5.x (org.neo4j:neo4j-connector-apache-spark).
Run `npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a claude-code`. Or copy the skill folder (neo4j-spark-skill in neo4j-contrib/neo4j-skills) into .claude/skills/neo4j-spark-skill in your project. Claude Code loads it when a task matches its description.
Run `npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a codex`. Or copy the skill folder (neo4j-spark-skill in neo4j-contrib/neo4j-skills) into .agents/skills/neo4j-spark-skill in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add neo4j-contrib/neo4j-skills --skill neo4j-spark-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/neo4j-spark-skill, .gemini/skills/neo4j-spark-skill, .github/skills/neo4j-spark-skill and .opencode/skills/neo4j-spark-skill in your project.
SKILL.md names no scripts, command-line tools or credentials: Neo4j Spark Skill is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, WebFetch.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Neo4j Spark Skill is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Neo4j Spark Skill: Chdb Datastore (vemetric/vemetric, 395 stars), Datafusion Python (apache/datafusion-python, 607 stars), Spark Python Data Source (databricks/databricks-agent-skills, 345 stars) and Spark Version Upgrade (OpenHands/extensions, 163 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
neo4j-contrib (a GitHub organization) maintains it in neo4j-contrib/neo4j-skills, which has 114 GitHub stars. The repository holds 28 skills in this directory. The repository was last updated on October 9, 2026.
Source: neo4j-contrib/neo4j-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.