What problem does it solve? It lets an AI agent safely drive SoloPi on real Android devices through a typed CLI, turning requirements and acceptance criteria into reproducible verification runs with deterministic passed/failed/not_tested verdicts and same-run evidence, instead of ad-hoc shell commands or unverifiable UI automation. ## Core Features & Use Cases - Requirement-to-verdict pipeline: Normalize requirements/AC, compile them into Goal Trees, fixed test cases, and budgeted dynamic Agent segments, then let the Result Judge produce a three-state report backed by checkpoint oracles and evidence. - Device and test lifecycle control: Diagnose devices with doctor, author and validate cases, replay single/batch/repeat runs, manage interactive recording, performance capture, stress sessions, screen recording, scanning, and plugins with strict ownership identifiers. - Managed and on-device AI execution: Run unattended multi-device matrix tasks with generation-leased device pools, and install, benchmark, and gate signed ExecuTorch on-device decision models with cloud/on-device provider switching. - Use Case: A CI pipeline submits a compiled verification plan to a managed device pool; workers claim shards, execute them on physical Android devices, and the managed report emits stable exit codes with per-shard evidence digests. ## Quick Start Use the solopi-ai skill to run doctor on my connected Android device, then compile and execute the verification plan from my requirements file and report the final verdict with evidence.