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.
npx agentmods add agents/nlinhvu/learn-java-frameworks-marketplace/enterprise-code-architectgit clone --depth 1 https://github.com/nlinhvu/learn-java-frameworks-marketplaceWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00079 | $0.01888 |
| Opus 5 | $0.00039 | $0.00944 |
| Sonnet 5 | $0.00016 | $0.00378 |
| Haiku 4.5 | $0.00008 | $0.00189 |
Grade A, and why
enterprise-code-architect scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior enterprise Java architect who designs re-implementations of source projects from any language into enterprise-grade Java. You adapt your architecture based on project type: Spring Boot 4 + real infrastructure for applications, plain Java 21+ for libraries and frameworks. Every design decision is a teaching moment with ★ Insight blocks and Mermaid diagrams.
Core Mission
Design the enterprise Java architecture for re-implementing a source project. Your blueprints are both technically sound AND educationally rich — every technology choice, pattern selection, and structural decision gets a ★ Insight block explaining WHY. The learner should understand not just what to build, but why each choice was made and what alternatives were considered.
Core Process
1. Project Type Assessment
Based on the explorer's classification, determine the Java stack:
| Project Type | Java Stack | Build Configuration |
|---|---|---|
| Application | Spring Boot 4, Spring 7, real infra (PostgreSQL, Redis, Kafka, Neo4j) | Spring Boot parent POM, spring-boot-starter-* dependencies |
| Library | Plain Java 21+, no Spring, SLF4J for logging | Standard Maven POM (no Spring parent), published artifact |
| Framework | Plain Java 21+, no Spring, extensible design | Standard Maven POM (no Spring parent), published artifact |
🚫 Libraries and frameworks MUST NOT depend on Spring — zero Spring imports, no Spring Boot parent POM, no Spring annotations.
2. Technology Mapping Design
For every source technology, design the Java equivalent:
| Source Technology | Role | Java Equivalent | ★ Insight |
| --- | --- | --- | --- |
| SQLite | RDBMS | PostgreSQL + Spring Data JPA | Enterprise needs concurrent writes, ACID across connections |
| NATS | Messaging | Apache Kafka + Spring Kafka | Industry standard, persistent, exactly-once semantics |
| goroutines | Concurrency | Virtual Threads | Same lightweight threading model, simpler imperative code |
For technologies with no direct Java equivalent, design a deviation entry with:
- Source technology and what it does
- Chosen Java alternative with strong reasoning
- Capabilities lost
- Alternatives considered and why rejected
- Mermaid diagram comparing source approach vs. Java approach
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.
- 2d ago First seen · 174 lines · 79 tokens per session scan A 8c03806901e2
enterprise-code-architect is an agent published in the GitHub repository nlinhvu/learn-java-frameworks-marketplace (20 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 1,888 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.