Workflow

Kaddo has one practical loop:

kaddo init          # state: new | pre-ai | legacy, 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
# ── use your LLM with the context pack + agents to create
#    capabilities, architecture 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

In one sentence: scan the repo → prepare context → use agents in your LLM → create roadmap-driven work items → connect knowledge to code → guard against drift → explain the state.

flowchart LR
    A[Request] --> B[Discovery]
    B --> C[Scan]
    C --> D[Context Pack]
    D --> E[LLM Agents]
    E --> F[Capabilities / Architecture / Risks]
    F --> G[Roadmap]
    G --> H[Classification]
    H --> I[Work Item]
    I --> J[Ownership]
    J --> K[Build]
    K --> L[Guard]
    L --> M[Learn]
    M --> N[Explain]
    N --> A

CLI vs LLM agents

Kaddo works in two layers, and the split is deliberate.

Layer	Responsibility
Kaddo CLI (deterministic)	initialize knowledge structure, scan signals, generate context packs, install agent prompts, guide handoff, create work items, declare ownership, detect drift, explain project state
LLM chat (interpretation)	extract capabilities, reconstruct architecture, propose a roadmap, identify risks, draft structured artifacts

The CLI prepares and stores context. Your LLM interprets it using Kaddo agents. Kaddo does not call an LLM by default and never requires an API key.

New, pre-AI and legacy projects

Kaddo adapts to where your project is.

Project state	What Kaddo does
new	Start with a minimal knowledge structure (roadmap, work items, minimum context) without process overhead.
pre-AI	Scan the repo, prepare a context pack and understand it with agents before evolving.
legacy	Map ownership gradually and identify risky areas before changing code.

kaddo init asks for the project state, team size and repository structure, and the rest of the commands adapt their guidance accordingly.

What Kaddo does not do

• It is not a code generator.
• It is not an agent execution framework — it ships agent prompts, it does not run them.
• It does not replace Jira, Linear or documentation tools.
• It is not a platform.
• It does not call an LLM, require an API key, or infer business truth.
• It does not replace human review.