What problem does it solve?
Building HoloHub applications involves many error-prone steps: resolving the right checkout, scaffolding new apps, running containerized builds, producing finite test evidence, and benchmarking data flows. This Skill gives AI agents a safe, repeatable procedure for the entire non-failing HoloHub application lifecycle using the public ./holohub wrapper.
Core Features & Use Cases
- Checkout and workspace safety: Resolves one valid HoloHub checkout, preserves dirty state and unrelated work, and requires authorization before cloning or mutating anything.
- Preview-then-act workflow: Every effect-bearing ./holohub command (setup, create, build, run, test) is previewed with --dryrun before the identical real command executes.
- Finite evidence and benchmarking: Enforces bounded run modes, visual/recording inspection, lint, and optional flow benchmarking with raw-log validation.
- Use Case: Ask your agent to scaffold a new Python HoloHub app that replays a video with a Holoviz overlay, add a 30-frame headless smoke test, and produce a reproducible flow benchmark — the Skill walks through setup preview, create, build, run, test, and benchmark in order.
Quick Start
Ask your agent to scaffold, build, run, and test a new HoloHub application from your brief using the ./holohub workflow.