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calibrate-room 精选

Run the ADR-151 per-room calibration pipeline — baseline → enroll → extract → train → a bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly).

在 GitHub 打开

安装方式

cp -r harness/ruview/.claude/skills/calibrate-room ~/.claude/skills/calibrate-room

把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/

整仓一次性获取:git clone --depth 1 https://github.com/ruvnet/RuView.git

依赖与权限

  • 无需 API Key
  • 无需联网
  • 依赖外部命令:node cargo
  • 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。

SKILL.md 正文

来源 ruvnet/RuView@a71634a · 许可 MIT · 内容未修改

calibrate-room

Turn a provisioned node + sensing-server into a working room model. Pure-Rust,

edge-deployable (ADR-151). Use the ruview_calibrate tool (installed

wifi-densepose binary, else cargo run -p wifi-densepose-cli).

Sequence

  1. baseline — capture the empty room (Welford amplitude + von Mises phase). Leave

the room empty.

ruview_calibrate {step: "baseline"}

  1. enroll — record the occupant(s) doing the target activities.

ruview_calibrate {step: "enroll"}

  1. train-room — train the bank of small specialists from baseline + enrollment.

ruview_calibrate {step: "train-room"}

  1. room-watch — live presence/posture/breathing from the trained room.

ruview_calibrate {step: "room-watch"} (or the room-watch skill)

Honesty

The specialists are calibrated to this room; cross-room transfer is a separate

problem (LoRA recalibration, ADR-079 P9). Report which room a number came from, and

tag presence/vitals accuracy MEASURED only with a held-out check — run

ruview_claim_check on the writeup.

质量与来源

所属仓库
ruvnet/RuView
仓库星标
9.8 万
最近更新
今天
采集时间
今天
commit
a71634ae8a60
内容指纹
sha256:4b29c7c331f47ac
文件路径
harness/ruview/.claude/skills/calibrate-room/SKILL.md

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