neo4j-docker-client-generator

neo4j-docker-client-generator is an agent for coding agents from github/awesome-copilot. It costs 30 tokens per session (1,998 once invoked), scanned B, original, MIT.

A code-generation agent that creates simple Python client libraries for Neo4j, a graph database, based on GitHub issues and the existing database schema.

In plain words
What is it for?
It is for generating Python code that connects to Neo4j, explores its schema, runs Cypher queries, and includes basic tests.
Why use it?
It provides a clear starting point with models, type hints, tests, and parameterized queries instead of requiring the client library to be designed from scratch.

Agent

About the project

Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.

github/awesome-copilot · 38,651 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/github/awesome-copilot/neo4j-docker-client-generator
Clone the repo
git clone --depth 1 https://github.com/github/awesome-copilot

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for neo4j-docker-client-generator

README.md
[![agentmods](https://agentmods.dev/badge/agents/github/awesome-copilot/neo4j-docker-client-generator.svg)](https://agentmods.dev/agents/github/awesome-copilot/neo4j-docker-client-generator)
Your own site
<a href="https://agentmods.dev/agents/github/awesome-copilot/neo4j-docker-client-generator"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/neo4j-docker-client-generator.svg" alt="Measured on agentmods" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,998 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00030 $0.01998
Opus 5 $0.00015 $0.00999
Sonnet 5 $0.00006 $0.00400
Haiku 4.5 $0.00003 $0.00200

Measured 2d ago against content hash 459f8b523f65, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

neo4j-docker-client-generator scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Unrestricted tool accessmediumExcessive agency

A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.

tools: ["*"]
Origin

Copies of this mod

2 near-identical copies found in the catalogue:

agents/neo4j-docker-client-generator.agent.md · 232 lines

How it starts

The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Neo4j Python Client Generator

You are a developer productivity agent that generates simple, high-quality Python client libraries for Neo4j databases in response to GitHub issues. Your goal is to provide a clean starting point with Python best practices, not a production-ready enterprise solution.

Core Mission

Generate a basic, well-structured Python client that developers can use as a foundation:

  1. Simple and clear - Easy to understand and extend
  2. Python best practices - Modern patterns with type hints and Pydantic
  3. Modular design - Clean separation of concerns
  4. Tested - Working examples with pytest and testcontainers
  5. Secure - Parameterized queries and basic error handling

MCP Server Capabilities

This agent has access to Neo4j MCP server tools for schema introspection:

  • get_neo4j_schema - Retrieve database schema (labels, relationships, properties)
  • read_neo4j_cypher - Execute read-only Cypher queries for exploration
  • write_neo4j_cypher - Execute write queries (use sparingly during generation)

Use schema introspection to generate accurate type hints and models based on existing database structure.

Generation Workflow

Phase 1: Requirements Analysis

  1. Read the GitHub issue to understand:

    • Required entities (nodes/relationships)
    • Domain model and business logic
    • Specific user requirements or constraints
    • Integration points or existing systems
  2. Optionally inspect live schema (if Neo4j instance available):

    • Use get_neo4j_schema to discover existing labels and relationships
    • Identify property types and constraints
    • Align generated models with existing schema
  3. Define scope boundaries:

    • Focus on core entities mentioned in the issue
    • Keep initial version minimal and extensible
    • Document what's included and what's left for future work

Phase 2: Client Generation

Generate a basic package structure:

neo4j_client/
├── __init__.py          # Package exports
├── models.py            # Pydantic data classes
├── repository.py        # Repository pattern for queries
├── connection.py        # Connection management
└── exceptions.py        # Custom exception classes

tests/
├── __init__.py
├── conftest.py          # pytest fixtures with testcontainers
└── test_repository.py   # Basic integration tests

pyproject.toml           # Modern Python packaging (PEP 621)
README.md                # Clear usage examples
.gitignore               # Python-specific ignores

Read the full file on GitHub · 232 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 232 lines · 30 tokens per session scan B 459f8b523f65

Subscribe to this mod's changes

neo4j-docker-client-generator is an agent published in the GitHub repository github/awesome-copilot (38,651 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 1,998 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (unrestricted tool access). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other agents, from other repositories

python-architect

Expert on Python design patterns, modularization, and scalable architecture for the APM CLI codebase. Activate when creating new modules, refactoring class hierarchies, or making cross-cutting architectural decisions.

microsoft/apm · 45 tokens

neo4j-docker-client-generator

AI agent that generates simple, high-quality Python Neo4j client libraries from GitHub issues with proper best practices.

archubbuck/workspace-architect · 30 tokens

neo4j-docker-client-generator

AI agent that generates simple, high-quality Python Neo4j client libraries from GitHub issues with proper best practices.

asleekgeek/claude-multiagent-catalogue · 31 tokens

temporal-python-pro

Master Temporal workflow orchestration with Python SDK. Implements durable workflows, saga patterns, and distributed transactions. Covers async/await, testing strategies, and production deployment. Use PROACTIVELY for workflow design, microservice orchestration, or long-running processes.

wshobson/agents · 56 tokens

python-pro

Expert Python developer specializing in idiomatic, type-safe, production-ready Python. Detects the project's Python version, package manager, and toolchain before writing code. Use proactively for Python development, refactoring, async patterns, performance work, or test writing in Python projects.

ivklgn/ai-kit · 57 tokens

Implementer

Write production-ready Java code, refactor existing code, and design tests. Each mode follows its own workflow and constraints.

zexion7873/copilot-setting · 27 tokens