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Review regression-sensitive LawnBerry Pi manual control and camera paths: RoboHAT USB handoff, watchdog feeding, motor authorization, joystick responsiveness, MJPEG and snapshot fallback, camera ownership, and stream backpressure handling.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use for LawnBerry Pi simulation vs real-hardware validation. Covers SIM_MODE selection, .env and config checks, simulation-safe test-first workflow, hardware preflight expectations, and avoiding false claims that local success proves on-device behavior.
Use for improving LawnBerry Pi AI result quality without breaking the existing backend contract. Covers ai_service review, model artifact or rule improvement, conservative evaluation, performance notes, and avoiding premature accelerator-specific churn.
Keep LawnBerry Pi maintainer documentation synchronized with implementation. Use when runtime behavior, supported hardware scope, subsystem maturity, immediate focus, two-week plan, release notes, or callable interfaces have changed and docs must be updated in the same pass.
Use when returning to LawnBerry Pi after time away, starting substantial maintenance, or needing a fast project re-entry workflow. Covers docs to read first, runtime ports, hardware baseline, simulation vs hardware mode, important files by task, and validation planning.
Use when a LawnBerry Pi task spans re-entry, specialist routing, implementation, documentation sync, and validation. Covers choosing the right specialist workflow and sequencing investigation, changes, doc updates, and test planning.
Use for LawnBerry Pi mission persistence and recovery work. Covers what mission state should survive restart, safe restart semantics, paused vs running recovery behavior, lifecycle validation, and persistence-backed tests.
| name | control-camera-regression-review |
| description | Review regression-sensitive LawnBerry Pi manual control and camera paths: RoboHAT USB handoff, watchdog feeding, motor authorization, joystick responsiveness, MJPEG and snapshot fallback, camera ownership, and stream backpressure handling. |
| argument-hint | What manual-control or camera change should be reviewed or hardened? |
| user-invocable | true |
This skill provides a conservative review workflow for the two highest-regression surfaces called out in the maintainer handbook: manual control and camera streaming.
docs/developer-toolkit.mddocs/RELEASE_NOTES.mdbackend/src/services/robohat_service.pybackend/src/services/camera_stream_service.py