This page walks through the first useful session. The goal is not to configure every advanced backend immediately; the goal is to make one small, inspected, verified change.

The scaffold bootstrap does not need one. coding-scaffold wizard, probe, credentials, adapt, and select-model run locally in Python. select-model classifies the text and recommends a route; it does not send the prompt to an LLM.

The first LLM call happens when the coding adapter starts doing agent work, for example when OpenCode runs /first-session. Before that step, OpenCode needs access to a model through a local runtime, an authenticated CLI, or a cloud/API provider.

git clone https://github.com/JRS1986/CodingScaffold.git
cd CodingScaffold
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"

On Windows PowerShell outside WSL:

.venv\Scripts\Activate.ps1

coding-scaffold wizard --target ~/dev/my-project
cd ~/dev/my-project

The wizard writes .coding-scaffold/ with project facts, provider hints, routing guidance, and first-session documentation.

If you need project-local credentials, create an ignored template and fill only the providers you intend to use:

coding-scaffold credentials --target . --format env

OpenCode is the recommended default adapter for the first team rollout.

curl -fsSL https://opencode.ai/install | bash
coding-scaffold adapt --target . --tool opencode
opencode

Inside OpenCode:

/first-session

Expected result: the agent inspects before editing, identifies run/test commands, maps key files, and proposes one safe improvement. This command is where a working LLM connection becomes required.

Inside OpenCode:

/agentic-change

Expected result:

• explorer maps relevant files
• implementer makes a bounded change
• verification runs
• reviewer challenges the result
• you receive changed files, checks, findings, and follow-up

If the loop helped, create a reusable skill:

coding-scaffold skill --target . --adapter opencode --name "Small Safe Improvement"

Then create a knowledge base to preserve decisions and useful patterns:

coding-scaffold knowledge --target . --backend obsidian