systematic-debugging

• Agents (17 Roles): Specialized personas with distinct mindsets (e.g., debugger, planner, fullstack-developer, ui-ux-designer).
• Commands (50+): Standardized operating procedures for common tasks (e.g., /fix, /code, /plan, /test).
• Skills (61+): Modular knowledge packages loaded on demand (e.g., frontend-development, payment-integration, senior-data-engineer, senior-data-scientist).
• Router: Decision logic that selects the right agent, command, and skill for the job.
• Workflows: Orchestration protocols for complex, multi-step engineering tasks.
• Hooks: Automated scripts for formatting, review, and context management.

---

```text

AGENTS.md/

│

├── AGENTS.md # Working conventions (the AI reads this)

├── README.md # Documentation (this file)

│

├── .opencode/ # OpenCode control center

│ ├── agent/ # Role definitions

│ ├── commands/ # Task procedures

│ └── skills/ # Knowledge base

│

├── .claude/ # Claude-specific configuration (optional)

└── .agent/ # Other agent configs (optional)

```

---

1. Clone the Repository

```bash

git clone https://github.com/your-repo/AGENTS.md

```

1. Integrate into Your Project (OpenCode)

Copy the core configuration to your project root:

```bash

cp -r AGENTS.md/.opencode your-project/

cp AGENTS.md/AGENTS.md your-project/

```

1. Start Working

Your AI assistant will automatically detect the configuration in .opencode/ and AGENTS.md to begin working as a structured team.

---

Extend the system by adding Markdown files to the respective directories:

• New Agent: Create .opencode/agent/my-agent.md
• New Command: Create .opencode/commands/my-command.md
• New Skill: Create .opencode/skills/my-skill/SKILL.md

---

Senior Data Engineer (`senior-data-engineer`)

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and the modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps.

Use when:

• Designing data architectures
• Building data pipelines
• Optimizing data workflows
• Implementing data governance

Tech stack: Python, SQL, R, Scala, Go, Spark, Airflow, dbt, Kafka, Databricks, Docker, Kubernetes

---

Apache 2.0 - See [LICENSE](LICENSE) for details.

---

This repository is curated from multiple public open-source repositories. I am not the original author of the underlying content; my contribution lies in selecting, customizing, and adapting it to fit specific use cases and workflows. All credit belongs to the original authors and the open-source community.

The .claude directory is sourced from [original repo](https://github.com/duc01226/EasyPlatform).

The .agent directory is sourced from [original repo](https://github.com/vudovn/antigravity-kit).