# Reads: aura_client_id, aura_client_secret, aura_project_id (optional) Human Guide

## What This Is For
Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, It gives the agent a clearer input/output frame for marketing analytics: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Reads: aura_client_id, aura_client_secret, aura_project_id (optional) agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for reads: aura_client_id, aura_client_secret, aura_project_id (optional).
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Reads: aura_client_id, aura_client_secret, aura_project_id (optional) guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Running GDS algorithms in Aura Graph Analytics GDS Sessions
- Creating `GdsSessions` or using `AuraGraphDataScience`
- Remote projecting connected Neo4j data with `gds.graph.project.remote(...)`
- Using AuraDB Cypher API projection with `{ memory: ... }` or `{ sessionId: ... }`
- Processing graph data from non-Neo4j sources (Pandas, Spark, CSV)
- On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
- Full isolation from the live database during analytics
- **Aura Pro with embedded GDS plugin** → `neo4j-gds-skill`
- **Self-managed Neo4j with embedded GDS plugin** → `neo4j-gds-skill`
- **Writing Cypher queries** → `neo4j-cypher-skill`
- **Snowflake Graph Analytics** → `neo4j-snowflake-graph-analytics-skill`
- `graphdatascience >= 1.15` required; `>= 1.18` for Spark

## Decision Points And Nuance
The original skill emphasizes: When to Use, When NOT to Use, Deployment Decision Table, Defaults, Installation, graphdatascience 2.0 (alpha), Key Patterns, Step 1 — Authenticate, Step 2 — Estimate Memory, Step 3 — Create Session.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- `graphdatascience >= 1.15` required; `>= 1.18` for Spark
- Use snake_case parameters end-to-end; never mix v2 with camelCase params
- Connected sessions: call `gds.v2.verify_db_connectivity()` when source DB access required
- Use `AuraAPICredentials.from_env()` — never hardcode credentials
- `CALL () { ... }` required for multi-pattern MATCH. Use `UNION` inside `CALL` for multiple labels/rel types.
- Required columns — nodes: `nodeId` (int), `labels` (str). Relationships: `sourceNodeId`, `targetNodeId`, `relationshipType`. Drop string node properties before `construct()`.
- [ ] Connected sessions call `gds.v2.verify_db_connectivity()` when source DB access required
- [ ] TTL set to avoid unexpected costs on idle sessions

## Copy-And-Paste Prompt
```text
Use the Reads: aura_client_id, aura_client_secret, aura_project_id (optional) human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the neo4j-contrib/neo4j-skills skill entry for `neo4j-aura-graph-analytics-skill`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

## When to Use
- Running GDS algorithms in Aura Graph Analytics GDS Sessions
- Creating `GdsSessions` or using `AuraGraphDataScience`
- Remote projecting connected Neo4j data with `gds.graph.project.remote(...)`
- Using AuraDB Cypher API projection with `{ memory: ... }` or `{ sessionId: ... }`
- Processing graph data from non-Neo4j sources (Pandas, Spark, CSV)
- On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
- Full isolation from the live database during analytics

## When NOT to Use
- **Aura Pro with embedded GDS plugin** → `neo4j-gds-skill`
- **Self-managed Neo4j with embedded GDS plugin** → `neo4j-gds-skill`
- **Writing Cypher queries** → `neo4j-cypher-skill`
- **Snowflake Graph Analytics** → `neo4j-snowflake-graph-analytics-skill`

---

## Deployment Decision Table

| Deployment | Use |
|---|---|
| AuraDB Free | **this skill** — max `m_2GB`, 1 concurrent session, unbilled |
| Aura Pro + Graph Analytics plugin enabled (lightweight exploration, shared resources) | `neo4j-gds-skill` |
| Aura Pro / Pro Trial + session (isolated compute) | **this skill** — up to 128 GB (Pro) / 8 GB (Pro Trial), 100 / 3 concurrent sessions |
| AuraDB + Python client sessions | **this skill** |
| AuraDB + Cypher API | **this skill** for AGA-specific projection/session notes; `neo4j-cypher-skill` for query authoring |
| Self-managed Neo4j + AGA session | **this skill** |
| Self-managed Neo4j + embedded plugin | `neo4j-gds-skill` |
| Non-Neo4j data (Pandas, Spark) | **this skill** (standalone mode) |

---

## Defaults

- `graphdatascience >= 1.15` required; `>= 1.18` for Spark
- Prefer v2 endpoints: `gds.v2.graph.project(...)`, `gds.v2.page_rank.*`, `gds.v2.graph.node_properties.*`
- Use snake_case parameters end-to-end; never mix v2 with camelCase params
- Use v1 if v2 endpoint missing/incompatible; label fallback
- Call `gds.v2.verify_session_connectivity()` after session creation
- Connected sessions: call `gds.v2.verify_db_connectivity()` when source DB access required
- Estimate memory before large sessions
- Set TTL; default 1h idle, max 7d
- Close session when done: `gds.delete()` or `sessions.delete(name)` stops billing
- Use `AuraAPICredentials.from_env()` — never hardcode credentials

---

## Installation

```bash
pip install "graphdatascience>=1.15,<2"    # 1.22 is the current stable release
```

### graphdatascience 2.0 (alpha)

`2.0aN` is pre-release — pin `<2` for production. Rename map for when 2.0 ships:

| 1.x | 2.0 |
|---|---|
| `gds.v2.<endpoint>` | `gds.<endpoint>` — `gds.v2` prefix gone; untyped 1.x endpoints removed |
| `gds.graph.project(...)` (AGA) | `gds.graph.project.cypher(...)` |
| `gds.graph.project_native(...)` (AGA) | `gds.graph.project.native(...)` |
| `GraphV2` / `ModelV2` | `Graph` / `Model` — `from graphdatascience import Graph` |
| `Graph.drop(failIfMissing=)` / `Model.drop(failIfMissing=)` | `fail_if_missing=` |
| `run_cypher(..., retryable=)` | removed — always retries |
| `ArrowEndpointVersion.from_arrow_info` | `check_version_compatibility` |
| `ServerVersion`, `SemanticVersion` from top level | `graphdatascience.versions` |
| `gds.graph.node_labels.mutate(write_concurrency=, job_id=)` | parameters removed |

2.0 minimums: GDS server 2.13, `neo4j` driver 5.26, pandas 2.x–3.x, pyarrow 21–25, numpy <3.

2.0 additions: `GdsSessions.estimate(algorithms=[...])` for per-algorithm memory; `GdsSessions.get_or_create(show_progress=...)`; `gds.pipeline.get`; `overwrite=True` on `gds.graph.project` / `generate` / `construct` / `filter` / `sample` to drop a same-named graph first; `GdsSessions.delete(session_id=...)` returns `False` when nothing was deleted.

---

## Key Patterns

### Step 1 — Authenticate

```python