kaddo understand

kaddo understand

Guides the handoff from the CLI (deterministic context) to your LLM (interpretation). It refreshes the context pack, recommends which agents to use — in what order — based on your project state, and writes a reusable guide you can re-open any time.

It writes / refreshes:

• .kaddo/context-pack.md and .kaddo/context-pack.json — the input for agents.
• .kaddo/understand.md — the step-by-step handoff guide with the recommended flow, expected outputs and copy/paste instructions.

What it does

1. Requires an initialized project (kaddo init).
2. Checks for a scan baseline (.kaddo/scan.json) — warns but continues if missing.
3. Generates / refreshes the context pack (reuses kaddo context).
4. Builds a state-aware agent plan and flags any agents not yet installed (kaddo add agents).
5. Prints a concise terminal summary and writes .kaddo/understand.md.

Deterministic, no LLM

kaddo understand does not call an LLM, execute agents, or auto-generate architecture artifacts. It prepares context and tells you exactly which agent to run next. You stay in control of the interpretation.

State-aware agent flow

The recommended flow adapts to the project state recorded by kaddo init:

State	Recommended flow
new	roadmap-agent → architecture-agent
pre-ai	capability-agent → architecture-agent → roadmap-agent
legacy	legacy-agent → architecture-agent → capability-agent → roadmap-agent

Each step maps to an expected output, for example:

• capability-agent → architecture/capabilities.md
• architecture-agent → architecture/current-state.md
• roadmap-agent → architecture/roadmap.md
• legacy-agent → architecture/legacy/risks.md

Works even when context is incomplete

If the scan baseline or some agents are missing, the command still produces a plan and tells you the next concrete step (run kaddo scan or kaddo add agents).

scan vs context vs understand

• scan collects deterministic technical signals.
• context packages those signals (plus knowledge and work items) into an LLM-ready pack.
• understand ties it together: refreshes the pack and tells you which agent to run next, in what order, for your project state.

Example

Kaddo Understand

Project: demo
State: pre-ai
Team: indie
Structure: monorepo

Recommended flow:
  1. capability-agent → architecture/capabilities.md
  2. architecture-agent → architecture/current-state.md
  3. roadmap-agent → architecture/roadmap.md

First step: use capability-agent.

  Context:         .kaddo/context-pack.md
  Agent prompt:    architecture/agents/capability-agent.md
  Expected output: architecture/capabilities.md

Kaddo does not call an LLM. You stay in control of the interpretation.