Document current coding-workflow stack state
Snapshot of where opencode + Qwen3-Coder + MCPs + Kimi-Linear + voice + Phoenix tracing land today, plus in-flight (oc-tree, kimi-linear context ramp) and next (ComfyUI) items with pointers to per-project NEXT_STEPS.md guides.
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gitea-opencode-agent-project/README.md
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# Gitea OpenCode Agent Project
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This is a default project structure for an agentic OpenCode development environment using the qwen3-coder model (30b).
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## Project Structure
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```
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gitea-opencode-agent-project/
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├── docs/ # Documentation
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├── agents/ # Agent definition files
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├── scripts/ # Automation scripts
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├── config/ # Configuration files
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├── src/ # Source code
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├── README.md # Project documentation
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├── AGENTS.md # OpenCode agent definitions
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└── .gitignore # Git ignore patterns
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```
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## Agent Definitions
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Agent files are stored in the `agents/` directory with the following naming convention:
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- `agent-<role>.sh` for bash agents
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- `agent-<role>.py` for Python agents
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- `agent-<role>.json` for configuration files
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## Documentation
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The `docs/` directory contains the project documentation, including:
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- Agent documentation
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- Implementation guides
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- Usage examples
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- API documentation
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## Setup Instructions
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1. Initialize the project with `init`
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2. Configure the qwen3-coder model
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3. Deploy agents using the standard OpenCode Task Master system
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4. Run the environment with `docker compose up -d`
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## Configuration
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Configuration files are stored in `config/` and include:
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- Agent parameters
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- Environment settings
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- Model specifications
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- Deployment configurations
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## OpenCode Integration
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This project is designed to work with OpenCode's agent system directly without requiring dmux. The agents can be invoked as specialized tools using OpenCode's task master functionality:
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```bash
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# Example OpenCode task master commands
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/opencode agent documentation-agent
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/opencode agent scripting-agent
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/opencode agent deployment-agent
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```
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The qwen3-coder (30b) model integration is handled through OpenCode's provider configuration, allowing each agent to leverage the advanced reasoning capabilities of the model for:
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- Complex problem identification in infrastructure documentation
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- Multi-step configuration automation
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- Intelligent deployment strategy recommendations
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- Analysis of complex dependency chains in network diagrams
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