edge-ai-lab

A lab for edge AI — the exploration engine for the mesh's edge and local-model work: designs, runs and compares experiments across edge hardware and model stacks, then hands proven configurations to lobes-cli, which is the runtime that serves them.

What you get

• An agent-first CLI cited from teken (afi-cli) — the runtime package has no third-party dependencies.
• A mesh identity — culture.yaml (suffix + backend) and the matching resident prompt file (AGENTS.colleague.md, since this template runs backend: colleague).
• The canonical guildmaster skill kit (11 skills) under .claude/skills/, vendored cite-don't-import. See docs/skill-sources.md.
• A build + deploy baseline — pytest, lint, the agent-first rubric gate, and PyPI Trusted Publishing wired into GitHub Actions.

Quickstart

uv sync
uv run pytest -n auto                 # run the test suite
uv run edge-ai-lab whoami  # identity from culture.yaml
uv run edge-ai-lab learn   # self-teaching prompt (add --json)
uv run teken cli doctor . --strict    # the agent-first rubric gate CI runs

CLI

Every command supports --json. Results go to stdout, errors/diagnostics to stderr (never mixed). Exit codes: 0 success, 1 user error, 2 environment error, 3+ reserved.

Make it your own

1. Rename the package edge_ai_lab/ and the edge-ai-lab CLI/dist name throughout pyproject.toml, the package, tests/, sonar-project.properties, and this README.md. The name is hard-coded in ~100 places, so list every occurrence first — see the git grep discovery command in CLAUDE.md, the authoritative rename procedure.
2. Edit culture.yaml with your suffix and backend.
3. Rewrite CLAUDE.md for your agent and run /init.
4. Re-vendor only the skills you need from guildmaster (see docs/skill-sources.md).

See CLAUDE.md for the full conventions (version-bump-every-PR, the cicd PR lane, deploy setup).

License

Apache 2.0 — see LICENSE.