navigation-hardening-pass

스타9

포크3

업데이트2026년 3월 15일 18:11

Use for LawnBerry Pi navigation hardening work: feedback audit, waypoint progress validation, stop and fault behavior, obstacle gating, interrupted traversal handling, and targeted regression coverage in navigation and mission flows.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

출처

acredsfan

acredsfan/lawnberry_pi

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Navigation Hardening Pass

What this skill does

This skill packages the navigation/runtime credibility workflow from the maintainer handbook into a repeatable implementation sequence.

Best use cases

waypoint progress advancing too optimistically
pause, abort, interrupt, or fault paths not stopping cleanly
obstacle hold or lost-position behavior uncertainty
mission execution depending on navigation behavior without enough feedback evidence

Procedure

Start with a navigation feedback audit.
- trace where the system assumes movement succeeded
- identify where encoder, GPS, controller, or other feedback actually confirms motion
- list state transitions that can advance without enough evidence
Harden stop and fault behavior.
- ensure controller failures, obstacle holds, interrupts, and aborts fail closed
- verify pause and abort paths route through a clean stop path
Tighten regression coverage.
- prioritize service-level and contract-level tests around interrupted waypoint traversal, lost position, obstacle gating, and command-delivery failures
Re-run the focused navigation and mission validation slice.
Update maintainer docs if subsystem maturity, behavior, or limitations changed materially.

Working rules

prefer concrete field-feedback evidence over optimistic state updates
keep behavior deterministic when control or position confidence drops
do not broaden scope into unrelated autonomy work during a hardening pass
if navigation service signatures or public helpers changed, sync docs/code_structure_overview.md

Completion checks

a defect list or risk list exists before edits begin
stop and interruption semantics are explicit after the change
regression coverage matches the specific failure mode addressed
final notes distinguish hardened behavior from remaining field limitations

이 저장소의 다른 Skills

같은 저장소

sim-hardware-validation

acredsfan/lawnberry_pi

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.

2026-06-259

ai-model-quality-pass

acredsfan/lawnberry_pi

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.

2026-03-159

control-camera-regression-review

acredsfan/lawnberry_pi

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.

2026-03-159

maintainer-doc-sync

acredsfan/lawnberry_pi

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.

2026-03-159

maintainer-reentry

acredsfan/lawnberry_pi

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.

2026-03-159

maintenance-orchestration

acredsfan/lawnberry_pi

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.

2026-03-159

name	navigation-hardening-pass
description	Use for LawnBerry Pi navigation hardening work: feedback audit, waypoint progress validation, stop and fault behavior, obstacle gating, interrupted traversal handling, and targeted regression coverage in navigation and mission flows.
argument-hint	What navigation behavior, failure mode, or contract needs hardening?
user-invocable	true

Navigation Hardening Pass

What this skill does

This skill packages the navigation/runtime credibility workflow from the maintainer handbook into a repeatable implementation sequence.

Best use cases

waypoint progress advancing too optimistically
pause, abort, interrupt, or fault paths not stopping cleanly
obstacle hold or lost-position behavior uncertainty
mission execution depending on navigation behavior without enough feedback evidence

Procedure

Start with a navigation feedback audit.
- trace where the system assumes movement succeeded
- identify where encoder, GPS, controller, or other feedback actually confirms motion
- list state transitions that can advance without enough evidence
Harden stop and fault behavior.
- ensure controller failures, obstacle holds, interrupts, and aborts fail closed
- verify pause and abort paths route through a clean stop path
Tighten regression coverage.
- prioritize service-level and contract-level tests around interrupted waypoint traversal, lost position, obstacle gating, and command-delivery failures
Re-run the focused navigation and mission validation slice.
Update maintainer docs if subsystem maturity, behavior, or limitations changed materially.

Working rules

prefer concrete field-feedback evidence over optimistic state updates
keep behavior deterministic when control or position confidence drops
do not broaden scope into unrelated autonomy work during a hardening pass
if navigation service signatures or public helpers changed, sync docs/code_structure_overview.md

Completion checks

a defect list or risk list exists before edits begin
stop and interruption semantics are explicit after the change
regression coverage matches the specific failure mode addressed
final notes distinguish hardened behavior from remaining field limitations