kaddo context

kaddo context

Assembles a context pack you can hand off to an LLM chat agent (Claude, ChatGPT, Cursor, Copilot, Windsurf…). It reads existing Kaddo artifacts and writes two files:

.kaddo/context-pack.md — compact, LLM-friendly markdown to paste into a chat.
.kaddo/context-pack.json — structured data for future tooling and automations.

Inputs

The command reads (each is optional except config):

.kaddo/config.yml          # required — run `kaddo init` first
.kaddo/scan.json           # scan baseline
.kaddo/modules.yml         # mapped multirepo modules (if any)
architecture/inventory.md  # technical inventory
architecture/knowledge.md  # current knowledge
architecture/roadmap.md    # roadmap
architecture/work-items/   # work item metadata (front matter only)

Missing files do not stop the command — their sections are marked under Missing Context so the LLM knows what is not yet available.

Mapped modules (multirepo)

If the project has modules registered with kaddo modules map, the pack adds a ## Mapped Modules section (and a mappedModules array in the JSON) listing each module’s type, repo path, owner, capabilities and which architecture/modules/<id>/ artifacts exist. Kaddo reads .kaddo/modules.yml and the module artifacts only — it never scans the secondary repositories.

Deterministic, no LLM

kaddo context does not call an LLM, require an API key, or interpret your system. It assembles metadata and summaries — never full source code. Interpretation is the agent’s job.

State-aware handoff

Recommended agents adapt to the project state recorded by kaddo init:

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

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.

Flags

kaddo context                    # writes both .md and .json
kaddo context --format markdown  # only the markdown pack
kaddo context --format json      # only the JSON pack

Example

# Kaddo Context Pack

Generated by `kaddo context`.

## Project Metadata

- Name: dotear-web
- State: pre-ai
- Team size: indie
- Structure: monorepo

## Recommended Agent Handoff

Recommended agents for a pre-AI project:

1. capability-agent
2. architecture-agent
3. roadmap-agent

## Instructions for the LLM

- Use this context pack as the project baseline.
- Do not write code yet.
- First extract: system capabilities, architecture notes, risks, open questions and roadmap candidates.