Pre-AI project

When to use this: you have a working codebase that was never prepared for AI-assisted development. The knowledge exists, but it is scattered and not structured for humans or LLM agents.

Workflow

kaddo init          # state: pre-ai, team size, structure
kaddo scan          # deterministic technical inventory → .kaddo/scan.json
kaddo context       # LLM context pack → .kaddo/context-pack.md
kaddo add agents    # install agent prompt packs
kaddo understand    # guided CLI → LLM handoff plan
# ── in your LLM, use capability-agent, architecture-agent and roadmap-agent to draft
#    capabilities, the architecture baseline and a roadmap ──
kaddo create --from roadmap   # turn a roadmap candidate into a Work Item
kaddo owners suggest          # declare code: ownership on the Work Item
kaddo guard                   # detect possible knowledge drift
kaddo explain                 # summarize what Kaddo currently knows

CLI vs LLM

CLI (deterministic): scan builds a technical inventory; context packages it; create, owners suggest, guard and explain keep knowledge connected to code.
LLM (interpretation): the capability-agent extracts business capabilities, the architecture-agent reconstructs the current architecture, and the roadmap-agent proposes a prioritized roadmap — all from the context pack, not from guesses.

kaddo scan detects signals and asks confirmation questions; it never claims to understand your business capabilities. That interpretation happens in the LLM.

Expected artifacts

.kaddo/scan.json
architecture/inventory.md
architecture/capabilities.md
architecture/current-state.md
architecture/roadmap.md
architecture/work-items/*.md
.kaddo/explain.md

Next steps

Start with the highest-value capabilities, declare ownership on the artifacts that map to real code, and let kaddo guard warn you when changes drift from the documented knowledge. See the Full workflow.

See it in action: the Loyalty Lite demo repo (includes a Guard drift demo), or browse all Examples.