Neo4j Aura Provisioning Skill logo

Neo4j Aura Provisioning Skill

Organization
neo4j-contrib
neo4j-aura-provisioning-skill

Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API. Use when creating, pausing, resuming, resizing, or deleting AuraDB Free/Professional/Business Critical/VDC instances; downloading credentials; scripting CI/CD pipelines; polling async status; or using the Terraform neo4j/neo4j-aura provider. Covers auth setup (client credentials OAuth2), credential lifecycle (download once — never recoverable), instance type selection, region codes, and Python provisioning scripts. Does NOT handle Cypher queries — use neo4j-cypher-skill. Does NOT cover Graph Data Science algorithms — use neo4j-gds-skill or neo4j-aura-graph-analytics-skill. Does NOT cover neo4j-admin/cypher-shell — use neo4j-cli-tools-skill.

Overview

Publisherneo4j-contrib
Repositoryneo4j-skills
Skill nameneo4j-aura-provisioning-skill
Stars
112
Forks
38
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by neo4j-contrib on GitHub. Read the source before you install it.

Installation

Install the Neo4j Aura Provisioning Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skills.git /tmp/neo4j-skills
mkdir -p .claude/skills
cp -r /tmp/neo4j-skills/neo4j-aura-provisioning-skill .claude/skills/neo4j-aura-provisioning-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Neo4j Aura Provisioning Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Neo4j Aura Provisioning Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Neo4j Aura Provisioning Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

When to Use

  • Creating an Aura instance (CLI, REST API, Python, Terraform)
  • Pausing, resuming, resizing, or deleting an instance
  • Downloading initial credentials from creation response
  • Polling instance status: creatingrunning
  • Setting up CI/CD provisioning or teardown pipelines
  • Choosing instance tier (Free vs Professional vs Business Critical vs VDC)

When NOT to Use

  • Cypher queries against running DBneo4j-cypher-skill
  • GDS algorithms on Auraneo4j-gds-skill (Pro with plugin) or neo4j-aura-graph-analytics-skill (serverless)
  • neo4j-admin / cypher-shellneo4j-cli-tools-skill
  • Application driver setup → use a language driver skill (python, javascript, java, go, dotnet)

Instance Tier Decision Table

TierAPI type codeMemoryGDSReplicasUse when
AuraDB Freefree-db1 GBDev/demo; ≤200k nodes/400k rels
AuraDB Professionalprofessional-db2–64 GBplugin availableProduction workloads
AuraDB Business Criticalbusiness-critical4–384 GBplugin availableHA, multi-AZ, SLA
AuraDB VDCenterprise-dbcustomDedicated infra, compliance
AuraDS Professionalprofessional-ds2–64 GB✅ built-inData science / GDS
AuraDS Enterpriseenterprise-dscustomEnterprise GDS

AuraDB Free limits: 200k nodes, 400k rels; auto-pauses after 72 h inactivity; deleted if paused >30 days; no resize.


Auth Setup

CLI (aura-cli v1.7+)

Install (binary, not pip):

bash
# macOS
curl -L https://github.com/neo4j/aura-cli/releases/latest/download/aura-cli-darwin-amd64.tar.gz | tar xz
sudo mv aura-cli /usr/local/bin/
aura-cli -v          # verify

Add credentials (from console.neo4j.io → Account Settings → API Credentials):

bash
aura-cli credential add \
  --name "my-creds" \
  --client-id "$AURA_CLIENT_ID" \
  --client-secret "$AURA_CLIENT_SECRET"
aura-cli credential use --name "my-creds"

Verify:

bash
aura-cli instance list --output table

REST API — Get Bearer Token

Token endpoint: POST https://api.neo4j.io/oauth/token Token expires: 3600 s (1 h). On 403 → refresh token.

bash
TOKEN=$(curl -s --request POST 'https://api.neo4j.io/oauth/token' \
  --user "${AURA_CLIENT_ID}:${AURA_CLIENT_SECRET}" \
  --header 'Content-Type: application/x-www-form-urlencoded' \
  --data-urlencode 'grant_type=client_credentials' \
  | jq -r '.access_token')
echo "Token: ${TOKEN:0:20}..."

Use in all subsequent calls: --header "Authorization: Bearer $TOKEN"


Step 1 — List Tenants (Projects)

CLI:

bash
aura-cli tenants list --output table
# Copy TENANT_ID for create operations

REST:

bash
curl -s https://api.neo4j.io/v1/tenants \
  -H "Authorization: Bearer $TOKEN" | jq '.data[] | {id, name}'

Step 2 — Create Instance

CRITICAL: Capture output immediately. Initial password shown ONCE — never retrievable again. If lost: delete and recreate. Store aura-creds.json before doing anything else.

CLI

bash
aura-cli instance create \
  --name "my-instance" \
  --cloud-provider gcp \
  --region europe-west1 \
  --type professional-db \
  --tenant-id "$TENANT_ID" \
  --output json | tee aura-creds.json

# Extract for .env
INSTANCE_ID=$(jq -r '.id' aura-creds.json)
PASSWORD=$(jq -r '.password' aura-creds.json)

REST API (full create)

bash
RESPONSE=$(curl -s -X POST https://api.neo4j.io/v1/instances \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name":           "my-instance",
    "cloud_provider": "gcp",
    "region":         "europe-west1",
    "type":           "professional-db",
    "tenant_id":      "'"$TENANT_ID"'",
    "memory":         "4GB",
    "version":        "5"
  }')
echo "$RESPONSE" | tee aura-creds.json
INSTANCE_ID=$(echo "$RESPONSE" | jq -r '.data.id')
PASSWORD=$(echo "$RESPONSE"   | jq -r '.data.password')

Instance create request body fields:

FieldRequiredValues
nameany string
cloud_providergcp aws azure
regionsee region table
typesee tier table
tenant_idfrom tenant list
memory1GB 2GB 4GB 8GB384GB
version5 (default)

Step 3 — Poll Until RUNNING (CRITICAL — All Ops Are Async)

ALL lifecycle operations (create, pause, resume, resize) are async. Do NOT attempt connection or next operation until status = running (or paused for pause op).

bash
poll_status() {
  local INSTANCE_ID=$1 TARGET=$2 MAX_WAIT=${3:-600}
  local ELAPSED=0 STATUS
  echo "Polling for status=$TARGET (max ${MAX_WAIT}s)..."
  while [ $ELAPSED -lt $MAX_WAIT ]; do
    STATUS=$(aura-cli instance get --instance-id "$INSTANCE_ID" --output json \
             | jq -r '.status' 2>/dev/null)
    echo "  [${ELAPSED}s] status=$STATUS"
    [ "$STATUS" = "$TARGET" ] && echo "Ready." && return 0
    [ "$STATUS" = "destroying" ] && echo "ERROR: instance is being destroyed" && return 1
    sleep 10; ELAPSED=$((ELAPSED + 10))
  done
  echo "TIMEOUT after ${MAX_WAIT}s — last status: $STATUS" && return 1
}

poll_status "$INSTANCE_ID" "running" 600

REST equivalent:

bash
while true; do
  STATUS=$(curl -s "https://api.neo4j.io/v1/instances/$INSTANCE_ID" \
    -H "Authorization: Bearer $TOKEN" | jq -r '.data.status')
  [ "$STATUS" = "running" ] && break
  sleep 10
done

Status lifecycle:

creating → running → pausing → paused → resuming → running
                  ↘ destroying → (gone)

Step 4 — Write .env and Verify

bash
CONNECTION_URI="neo4j+s://${INSTANCE_ID}.databases.neo4j.io"

cat > .env <<EOF
NEO4J_URI=${CONNECTION_URI}
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=${PASSWORD}
NEO4J_DATABASE=neo4j
AURA_INSTANCE_ID=${INSTANCE_ID}
EOF

# Ensure .env never committed
grep -q '^\.env$' .gitignore 2>/dev/null || echo '.env' >> .gitignore

# Verify connectivity
cypher-shell -a "$CONNECTION_URI" -u neo4j -p "$PASSWORD" "RETURN 'connected' AS status"

Step 5 — Lifecycle Operations

All operations require instance in the correct state. Wrong-state ops return 4xx error.

Pause

Required state: running

bash
aura-cli instance pause --instance-id "$INSTANCE_ID"
poll_status "$INSTANCE_ID" "paused" 600

Resume

Required state: paused

bash
aura-cli instance resume --instance-id "$INSTANCE_ID"
poll_status "$INSTANCE_ID" "running" 900   # resume can take longer

Resize (Professional+ only — NOT Free)

Required state: running; instance remains available during resize.

bash
# REST only — CLI resize not available in v1.7
curl -s -X PATCH "https://api.neo4j.io/v1/instances/$INSTANCE_ID" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"memory": "8GB"}'
poll_status "$INSTANCE_ID" "running" 600
# Cannot reduce below current usage level

Delete

IRREVERSIBLE. Export snapshots first if data needed.

bash
aura-cli instance delete --instance-id "$INSTANCE_ID"
# No poll needed — immediate

REST:

bash
curl -s -X DELETE "https://api.neo4j.io/v1/instances/$INSTANCE_ID" \
  -H "Authorization: Bearer $TOKEN"

Python CI/CD Provisioning Script

python
import os, time, requests

CLIENT_ID     = os.environ["AURA_CLIENT_ID"]
CLIENT_SECRET = os.environ["AURA_CLIENT_SECRET"]
BASE          = "https://api.neo4j.io/v1"

def get_token() -> str:
    r = requests.post(
        "https://api.neo4j.io/oauth/token",
        auth=(CLIENT_ID, CLIENT_SECRET),
        data={"grant_type": "client_credentials"},
    )
    r.raise_for_status()
    return r.json()["access_token"]

def auth_headers(token: str) -> dict:
    return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}

def create_instance(token: str, tenant_id: str, **kwargs) -> dict:
    payload = {"tenant_id": tenant_id, "version": "5", **kwargs}
    r = requests.post(f"{BASE}/instances", headers=auth_headers(token), json=payload)
    r.raise_for_status()
    return r.json()["data"]   # contains id, password, connection_url

def poll_status(token: str, instance_id: str, target: str, timeout: int = 600) -> None:
    deadline = time.time() + timeout
    while time.time() < deadline:
        r = requests.get(f"{BASE}/instances/{instance_id}", headers=auth_headers(token))
        r.raise_for_status()
        status = r.json()["data"]["status"]
        print(f"  status={status}")
        if status == target:
            return
        if status == "destroying":
            raise RuntimeError("Instance destroyed unexpectedly")
        time.sleep(10)
    raise TimeoutError(f"Instance {instance_id} did not reach '{target}' in {timeout}s")

# --- usage ---
token    = get_token()
instance = create_instance(
    token,
    tenant_id = os.environ["AURA_TENANT_ID"],
    name           = "ci-test-instance",
    cloud_provider = "aws",
    region         = "us-east-1",
    type           = "professional-db",
    memory         = "2GB",
)
# SAVE CREDENTIALS IMMEDIATELY — password never retrievable again
print(f"ID:       {instance['id']}")
print(f"URI:      neo4j+s://{instance['id']}.databases.neo4j.io")
print(f"Password: {instance['password']}")   # log to secure vault NOW

poll_status(token, instance["id"], "running", timeout=600)
print("Instance ready.")

Region Codes

AWS

Region codeLocation
us-east-1N. Virginia
us-east-2Ohio
us-west-2Oregon
eu-west-1Ireland
eu-west-3Paris
eu-central-1Frankfurt
ap-southeast-1Singapore
ap-southeast-2Sydney
ap-south-1Mumbai
sa-east-1São Paulo

GCP

Region codeLocation
europe-west1Belgium
europe-west3Frankfurt
europe-west4Netherlands
us-central1Iowa
us-east1S. Carolina
us-east4N. Virginia
asia-east1Taiwan
asia-northeast1Tokyo
asia-southeast1Singapore
australia-southeast1Sydney

Azure

Region codeLocation
eastusE. US
eastus2E. US 2
westeuropeNetherlands
northeuropeIreland
uksouthLondon
southeastasiaSingapore
brazilsouthBrazil
koreacentralKorea

Enterprise tiers (Business Critical, VDC) add 20+ additional regions per provider. Check console for full list. Free tier: GCP only; limited subset of regions.


Terraform Provider

hcl
terraform {
  required_providers {
    aura = {
      source  = "neo4j/neo4j-aura"
    }
  }
}

provider "aura" {
  client_id     = var.aura_client_id       # or AURA_CLIENT_ID env var
  client_secret = var.aura_client_secret   # or AURA_CLIENT_SECRET env var
}

resource "aura_instance" "db" {
  name           = "prod-db"
  type           = "professional-db"
  cloud_provider = "gcp"
  region         = "europe-west1"
  memory         = "4GB"
  tenant_id      = var.aura_tenant_id
}

output "neo4j_uri" {
  value     = "neo4j+s://${aura_instance.db.id}.databases.neo4j.io"
  sensitive = false
}
output "neo4j_password" {
  value     = aura_instance.db.password
  sensitive = true
}

After terraform apply: poll status before marking infra ready — Terraform resource creation returns when API call completes, not when DB is running.


Common Errors

ErrorCauseFix
403 Forbidden after workingToken expired (1 h TTL)Re-run get_token()
409 Conflict on createName already exists in tenantChange name or delete existing
422 on pauseInstance not runningCheck status; wait for ongoing op to finish
422 on resumeInstance not pausedCheck status
422 on resizeBelow current usageReduce data first; can't shrink below usage
Region not foundTier doesn't support that regionUse Free tier on GCP only; Pro/BC on all 3 clouds
Credentials lost after createPassword only returned at create timeDelete + recreate — no reset exists
429 Too Many RequestsRate limit hit (25 req/min Free, 125 req/min Pro+)Add time.sleep(2) between polling calls
instance list returns emptyWrong credential activeaura-cli credential use --name <name>

API Rate Limits

TierRequests/minute
Free / Pro Trial (no billing)25
Pro with billing, BC, VDC125

Poll interval: ≥10 s to stay within limits on Free; 5 s safe on Pro+. On Retry-After header in 5xx response: wait that many seconds before retry.


Security Rules

  • Write initial credentials to .env; verify .env in .gitignore before proceeding
  • Never print PASSWORD in CI logs — write to secrets vault (AWS Secrets Manager, GitHub secret, Vault)
  • Use from_env() / os.environ — never hardcode credentials
  • If .env absent: python-dotenv load_dotenv() auto-loads; do NOT prompt user unless loading fails

WebFetch — Current Docs

NeedURL
REST API spec (OpenAPI)https://neo4j.com/docs/aura/platform/api/specification/
CLI referencehttps://neo4j.com/docs/aura/aura-cli/
Region listhttps://neo4j.com/docs/aura/managing-instances/regions/
Auth detailshttps://neo4j.com/docs/aura/api/authentication/
Instance actionshttps://neo4j.com/docs/aura/managing-instances/instance-actions/

Checklist

  • .env created with URI/user/password; .env in .gitignore
  • Initial credentials saved to secure storage immediately after create
  • poll_status called after create — do NOT connect before status = running
  • poll_status called after pause/resume
  • Correct tier selected (Free for dev, Pro+ for production, BC for HA)
  • Region confirmed available for chosen tier and cloud provider
  • Tenant ID provided for all create/list operations (required in multi-tenant orgs)
  • Token refreshed if > 1 h old (or 403 received)
  • Delete confirmed by user — data loss is permanent, no recovery

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Neo4j Aura Provisioning Skill AI skill do?

Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API. Use when creating, pausing, resuming, resizing, or deleting AuraDB Free/Professional/Business Critical/VDC instances; downloading credentials; scripting CI/CD pipelines; polling async status; or using the Terraform neo4j/neo4j-aura provider. Covers auth setup (client credentials OAuth2), credential lifecycle (download once — never recoverable), instance type selection, region codes, and Python provisioning scripts. Does NOT handle Cypher queries — use neo4j-cypher-skill. Does NOT cover Graph Data Science algor...

Why use Neo4j Aura Provisioning Skill on TypingMind?

Because you install it once and use it with any model. Neo4j Aura Provisioning Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Neo4j Aura Provisioning Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/neo4j-contrib/neo4j-skills/tree/main/neo4j-aura-provisioning-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Neo4j Aura Provisioning Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Neo4j Aura Provisioning Skill?

As many as you like. As long as a model supports skills, you can use Neo4j Aura Provisioning Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Neo4j Aura Provisioning Skill AI skill free?

Yes. It is published on GitHub by neo4j-contrib under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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