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Agent Skill · 研究检索

tapo-camera(塔波相机)

tapo-camera 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

tapo-camera 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 17,379。

总安装 6,635周安装 285GitHub Stars 公开资料未说明下载 2,326核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tapo-camera(塔波相机)
来源仓库:https://github.com/ivangdavila/tapo-camera
安装命令:
openclaw skills install tapo-camera
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install tapo-camera

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

连接到 Tapo 摄像机、验证本地访问、捕获快照并使用本地优先 RTSP 工作流程和安全后备检查帧。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install tapo-camera;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

tapo-camera 怎么安装?

在终端执行 openclaw skills install tapo-camera。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tapo-camera 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

tapo-camera 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

tapo-camera 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

tapo-camera 用的人多吗?

公开数据是累计安装 6,635 次,近一周安装 285 次,下载 2,326 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

tapo-camera 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
tapo-camera

塔波相机 · 本页

6,635—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
Tapo Camera
slug
tapo-camera
version
1.0.0
homepage
https://clawic.com/skills/tapo-camera
description
Connect to Tapo cameras, verify local access, capture snapshots, and inspect frames with local-first RTSP workflows and safe fallbacks.
changelog
Initial release with local discovery, RTSP snapshot capture, device-boundary guardrails, and API fallback guidance for unsupported cases.
metadata
{"clawdbot":{"emoji":"📷","requires":{"bins":["python3","ffmpeg","kasa"],"pip":["python-kasa"],"config":["~/tapo-camera/"],"env.optional":["TAPO_CAMERA_USERNAME","TAPO_CAMERA_PASSWORD","KASA_CREDENTIALS_HASH"]},"install":[{"id":"pip-python-kasa","kind":"pip","package":"python-kasa","bins":["kasa"],"label":"Install python-kasa and the kasa CLI (pip)"},{"id":"brew-ffmpeg","kind":"brew","formula":"ffmpeg","bins":["ffmpeg"],"label":"Install ffmpeg (Homebrew)"}],"os":["darwin","linux","win32"],"configPaths":["~/tapo-camera/"]}}

When to Use

Use this skill when the user wants an agent to connect to their own Tapo cameras on a trusted local network, verify camera reachability, take still captures, and inspect the resulting frames without defaulting to cloud workflows.

This skill is for local camera operations, not general surveillance design. Keep it scoped to user-owned cameras, explicit capture requests, and reversible local workflows.

Architecture

Memory lives in ~/tapo-camera/. If ~/tapo-camera/ does not exist, run setup.md. See memory-template.md for structure.

~/tapo-camera/
├── memory.md               # activation boundaries, preferred capture defaults, and trust limits
├── cameras.md              # hostnames, labels, model notes, and stream capabilities
├── sessions/
│   └── YYYY-MM-DD.md       # capture attempts, failures, and what worked
├── captures/
│   └── ...                 # user-approved local stills only
└── incidents.md            # auth, network, RTSP, and model-specific failure notes

Only create cameras.md, sessions/, captures/, or incidents.md if the user wants persistent local state.

Quick Reference

TopicFile
Setup and activation behaviorsetup.md
Memory schema and status valuesmemory-template.md
Camera inventory templatecameras.md
Incident log templateincidents.md
Discovery, auth, and capability checksdiscovery-and-auth.md
Local still-capture and review flowssnapshot-workflows.md
Unofficial API fallback boundaryapi-fallback.md
Failure diagnosis and recovery ordertroubleshooting.md
Local capture helpertapo-capture.py

Requirements

  • python3
  • python-kasa and its kasa CLI for local discovery, device state, and camera module access
  • ffmpeg for one-frame JPEG capture from RTSP
  • A Tapo camera account with RTSP and ONVIF enabled in the Tapo app when the model supports third-party compatibility
  • Camera host or IP on the same trusted local network as the agent
  • Optional environment variables for the helper: TAPO_CAMERA_USERNAME, TAPO_CAMERA_PASSWORD, or KASA_CREDENTIALS_HASH
  • Optional unofficial fallback: a local API library only when RTSP or ONVIF is unavailable and the user approves that narrower path

Treat Tapo credentials and KASA_CREDENTIALS_HASH as secrets. Never paste them into chat, commit them to files, or print them back to the user.

Data Storage

Use local notes only when they improve repeatability:

  • the memory file for activation boundaries, privacy limits, and preferred output paths
  • cameras.md for camera labels, hosts, model quirks, and stream capability notes
  • dated session notes for capture attempts and the exact path that worked
  • incidents.md for recurring auth, RTSP, ONVIF, or firmware regressions
  • the captures/ folder for user-approved still images only

Core Rules

1. Prove scope and ownership before touching a camera

  • Confirm the camera belongs to the user and the agent is on the same trusted network segment.
  • Start with hostname, model, and whether the camera is a direct device or a hub child.
  • Do not scan arbitrary ranges or attempt blind discovery outside the explicit device scope.

2. Prefer maintained local tooling first

  • Use python-kasa and the kasa CLI for discovery, auth validation, and camera capability checks.
  • Use RTSP and ONVIF on the local camera only after confirming third-party compatibility is enabled.
  • Reach for unofficial APIs only when the maintained local path cannot produce the required capture flow.

3. Separate discovery from capture

  • First prove the device answers and the camera module is present.
  • Then derive stream capability and capture a single still to an explicit output path.
  • Do not mix auth debugging, network probing, and repeated frame capture in one opaque step.

4. Keep secrets out of chat, disk, and process output

  • Inject camera credentials from a secret manager or ephemeral environment variables.
  • Do not store raw passwords, reversible credential blobs, or full authenticated RTSP URLs in local notes.
  • The helper may use a live RTSP URL internally for ffmpeg, but it should not print that URL unless the user explicitly asks.

5. Capture the smallest useful artifact

  • Default to one still image, not continuous recording.
  • Write captures only to a user-approved local path under ~/tapo-camera/captures/ or another explicit destination.
  • Name captures with camera label and timestamp so later inspection stays deterministic.

6. Keep the trust boundary local by default

  • Local Tapo device traffic is allowed only to the camera host on the user's LAN.
  • Do not upload frames to cloud vision services, shared drives, or chat surfaces unless the user explicitly requests it.
  • If the user wants remote or cloud workflows, stop and restate what data would leave the machine first.

7. Fall back deterministically

  • If kasa cannot expose a camera module or RTSP URL, check model support, privacy mode, and third-party compatibility before changing approach.
  • If the camera is a hub child, battery device, or a model with broken RTSP support, use the API fallback playbook and keep that path local-first.
  • Record the final working path so the next capture does not repeat the same trial-and-error.

Capture Traps

TrapWhy It FailsBetter Move
Treating every Tapo device like a direct RTSP cameraHub children and some battery devices do not expose the same local stream surfaceIdentify device class first, then choose RTSP, ONVIF, or API fallback
Printing the full RTSP URL into logsThat leaks camera credentials into history and shared outputKeep URLs redacted by default and only reveal them on explicit request
Using cloud login assumptions for local captureLocal device auth and camera account setup are separate in practiceVerify the camera account and third-party compatibility state first
Jumping straight to repeated frame pullsHarder to debug and easier to cross privacy boundariesProve one still capture before any loop or batch job
Storing captures and credentials togetherRaises the blast radius if the local folder is copied or syncedKeep images and secrets separate, and never persist secrets in ~/tapo-camera/

External Endpoints

EndpointData SentPurpose
https://{camera-host}local camera authentication and feature querieslocal device connection through python-kasa
rtsp://{camera-host}:554/stream1 or stream2local camera credentials and stream requeststill capture via ffmpeg from the LAN stream
http://{camera-host}:2020/onvif/device_servicelocal device-service requestsONVIF capability checks when enabled on the camera

No third-party endpoint is part of the default workflow.

Security & Privacy

Data that may leave your machine:

  • Nothing to third parties by default
  • Local-LAN requests to the user's camera host for auth, capability checks, and frame capture

Data that stays local:

  • User-approved notes under ~/tapo-camera/
  • Captured stills in a local folder chosen by the user
  • Troubleshooting logs and device capability notes

This skill does NOT:

  • store camera passwords, tokens, or reversible credential blobs in plain text
  • capture or upload images without explicit user intent
  • run undeclared cloud vision or cloud relay workflows
  • modify camera firmware, privacy settings, or motor position by default
  • access files outside the working directory or ~/tapo-camera/ for memory

Scope

This skill ONLY:

  • connects to user-owned Tapo cameras on a trusted local network
  • validates local access with maintained tools
  • captures still images to explicit local paths
  • documents the minimum fallback needed when RTSP or ONVIF is unavailable

This skill NEVER:

  • brute-force camera credentials or scan arbitrary networks
  • enable silent background monitoring
  • upload frames to external services by default
  • treat hub children, battery devices, and direct cameras as interchangeable
  • rewrite the installed skill files

Related Skills

Install with clawhub install <slug> if user confirms:

  • cameras - broader camera capture and review workflows outside the Tapo-specific lane
  • ffmpeg - deeper frame extraction, transcoding, and media inspection once capture works
  • smart-home - ecosystem-level device coordination and automation planning
  • iot - local-device debugging and network-aware connector reasoning
  • photos - organize and inspect saved captures after acquisition

Feedback

  • If useful: clawhub star tapo-camera
  • Stay updated: clawhub sync

平台分布

OpenClaw

73.6%
按下载量换算1,712

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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