LaunchKit

A marketing assistant built as a harness on Agno, one step at a time. A marketer asks for a poster. LaunchKit answers with the poster's words (the copy) and its picture, following that brand's rules. Brands are workspaces: a folder of plain files, no code.

Setup

Needs Python 3.12+ and uv.

git clone <this repo> && cd ai-upskill
uv sync
cp .env.example .env        # add your OPENAI_API_KEY
uv run launchkit check 0    # prints PASS
uv run launchkit ui         # opens the chat page on http://localhost:8000

Without a key everything still runs: live runs use a fake model and a fake picture, and say so in the trace.

Commands

Command	What it does
uv run launchkit run harbor "Poster for the Tidal launch"	One request in the terminal: trace, spec, picture path
uv run launchkit ui	The chat page: chat, live trace, context view, poster
uv run launchkit explain	What the model saw on the last run, in what order, at what cost
uv run launchkit check 1	Prove step 1 with fake models. check all is the CP1.1 gate
uv run launchkit run harbor "..." --model fake:echo	Force a fake model for one run
uv run launchkit run harbor "..." --facts workspaces/harbor/brand-book.md	Load a file into the facts section

The build, in four steps

Each step fills one TODO slot in launchkit/harness/. Each step has a check. The answer to each step is on its own branch.

Step	Slot	Check	Branch
1 · The agent reads its rules from files	workspace.py load_workspace, agent.py rules section	check 1	cp1.1-step1-agent-from-files
2 · Check the form, fix it once, then stop	spec.py validate_spec, run.py repair	check 2	cp1.1-step2-validate-and-repair
3 · A tool and a leash	tools.py, limits.py, agent.py picture agent	check 3	cp1.1-step3-tool-and-leash
4 · The request says who you are	request.py resolve_workspace_id	check 4	cp1.1-step4-trust-the-request

Prompt cards for each step are in docs/.

Layout

launchkit/
  cli.py            run · ui · explain · check
  settings.py       env, paths, limits, section budgets
  harness/          request → workspace → context → copy agent → validate → picture agent → result
  adapters/         model resolver, fake models, image adapters, prices
  ui/               FastAPI app and one HTML page
workspaces/         one folder per brand: SOUL.md, AGENTS.md, rules.yaml, facts.md
checks/             one pytest file per step; `launchkit check` wraps them
docs/               setup, glossary, one page per step