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. It also asks which coding environment to use:

• opencode: default, recommended for the first rollout.
• openclaude: experimental option for teams tracking that workflow.
• both: generate both sets of guidance.
• manual: skip tool adapter generation and wire your own environment.

If the selected tool is missing and stdin is interactive, the wizard asks before installing it. Nothing is installed silently. The wizard can also configure the knowledge backend and shared Git remote during this setup phase.

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

Configure shared knowledge during setup:

coding-scaffold setup-knowledge --target . \
 --backend obsidian \
 --shared-remote https://github.com/acme/team-ai-knowledge.git

If you are joining an experienced team, prefer the team manifest:

coding-scaffold team connect --target . \
 --manifest https://github.com/acme/platform-ai-onboarding.git
coding-scaffold team doctor --target .

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

coding-scaffold setup-tool --tool opencode
coding-scaffold adapt --target . --tool opencode
opencode

Use coding-scaffold setup-tool --tool opencode --install when you want the CLI to install a missing tool without a second prompt, for example in a prepared dev container.

Use the same validate-or-install flow for optional pieces:

coding-scaffold setup-addon --target . --addon llmfit
coding-scaffold setup-addon --target . --addon obsidian
coding-scaffold setup-addon --target . --addon routellm
coding-scaffold setup-addon --target . --addon open-multi-agent
coding-scaffold setup-addon --target . --addon caveman-compression

llmfit is useful early because it improves hardware-aware model choice. RouteLLM and Open Multi-Agent are advanced; add them after the first agentic coding loop is working. Obsidian is a desktop app, so WSL users should usually install it on Windows and open .coding-scaffold/knowledge as a vault. Context sidecars work without the Caveman add-on; install it only when you want to try the upstream compression engine with compress-context --engine caveman.

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