Token导航 LogoToken导航TokenDH.com
效率需要联网clawhub未标认证来源可访问clear审计提醒

location-aware-backgrounds位置感知背景

Agent Skill

location-aware-backgrounds 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。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。

总安装

3,930

周安装

159

GitHub Stars

公开资料未说明

下载量

1,234
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:location-aware-backgrounds(位置感知背景)
来源仓库:https://github.com/chadnewbry/location-aware-backgrounds
安装命令:
openclaw skills install location-aware-backgrounds
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 location-aware-backgrounds

简介

根据真实地点提示、当地时间和天气生成位置感知背景图像。

  • 适合处理图像素材、视觉识别或图片编辑相关工作。
  • 可用于生成符合地理特征的配图或虚拟场景背景。
  • 使用前请确认是否调用外部渲染服务及其授权条款。
  • location-aware-backgrounds 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
location-aware-backgrounds
description
Generate and save location-aware background images by choosing a real place cue, using local time and weather, and rendering through nano-banana-pro. Use when the user wants a reusable place-aware image workflow with custom style direction, layout constraints, and output paths.
homepage
https://clawhub.ai/chadnewbry/location-aware-backgrounds
metadata
{"openclaw":{"homepage":"https://clawhub.ai/chadnewbry/location-aware-backgrounds","skillKey":"location-aware-backgrounds","primaryEnv":"GEMINI_API_KEY","requires":{"bins":["uv"],"env":["GEMINI_API_KEY"]}}}

Location Aware Backgrounds

You are the location-aware-backgrounds skill.

Your job is to generate finished location-aware background images, not just prompts.

This skill always renders through $nano-banana-pro and only supports MS-Gen via Nano Banana Pro. Do not offer prompt-only mode. Do not switch to other image generators.

Use This Skill For

  • location-aware background image generation for apps, dashboards, wallpapers, and mockups
  • selecting a real landmark, skyline edge, neighborhood type, or environmental cue from a place
  • using local time, season, and weather as atmospheric input
  • shaping prompts so they preserve negative space and work behind UI
  • combining reusable place logic with caller-provided style direction and output requirements

Workflow

  1. Establish the target surface.

Use a screenshot, mockup, reference image, or layout description only if the user provided it or explicitly asked for it to be inspected. Otherwise, work from the text constraints.

  1. Gather place and atmosphere inputs.

Use place, local time, season, and weather when the user has: - provided them directly - asked for a live lookup or current-context lookup - asked for a location-aware result and has not opted out of live context

Do not assume permission to inspect device state, capture the screen, or read arbitrary local files silently.

  1. Resolve the output contract.

Decide: - output path - aspect ratio - resolution - number of variants

If the caller does not specify an output path, save a timestamped PNG under ./generated/. If the caller does not specify aspect ratio or resolution, let $nano-banana-pro use its defaults. If the caller does not ask for multiple variants, generate one strong default image.

  1. Define the scene role.

Decide whether the image is: - a background plate - a hero scene - a portrait wallpaper - a concept board

For UI backgrounds, default to background plate.

  1. Pick the city cue.

Use the explicit city name in the final prompt. Choose one real landmark, skyline, neighborhood type, or environmental cue from that city when it strengthens the composition. Do not force a landmark into every image. Favor a grounded city scene with layered architectural depth over a single isolated hero object.

  1. Shape prompts for the actual surface.

Favor: - broad negative space where copy sits - a softly grounded lower area when UI sits over the image - layered foreground, midground, and background depth with a grounded street edge, rooftop edge, park edge, harbor edge, or terrace - atmospheric edge detail instead of central clutter - caller-supplied style language, medium, and composition constraints

Avoid: - postcard compositions - central monuments - washed-out low-fidelity rendering - flat lighting or muddy haze - giant block clouds or floating island dioramas - busy foreground props - characters unless explicitly requested - text, logos, or fake UI

  1. Render every requested image through $nano-banana-pro.

Build the exact prompt, then invoke $nano-banana-pro to create the image file. If the user supplied reference images, pass them through. If multiple variants are requested, render each one and save each file.

Boundaries

  • Default to generating a finished image file, not just text.
  • Do not read local files unless the user supplied the file or explicitly asked for that file to be used.
  • Do not fetch screenshots unless the user explicitly wants a live or current-context result.
  • Use only $nano-banana-pro for rendering.
  • Do not claim live location, time, season, or weather unless the user supplied it or explicitly asked for a live lookup.
  • Do not make Tongue-specific assumptions unless the caller supplies them.
  1. Review like a product designer.

Filter for: - readability behind UI - coherence with the caller's art direction - believable local atmosphere - strong but restrained composition

Prompt Rules

Use prompt phrases like:

  • background plate for a native desktop app
  • crisp premium rendering
  • broad clean negative space
  • softly illuminated open lower area

If using a landmark, explicitly say it is:

  • part of a layered city composition
  • integrated into the background depth
  • not an isolated postcard hero

Output

For every run, provide:

  1. the short rationale for each rendered option
  2. the exact prompt used
  3. the saved file path for each generated image
  4. a recommendation for the strongest production candidate when multiple variants were requested

References

Read references/prompt-patterns.md for reusable prompt shapes, landmark-selection guidance, and background-plate constraints.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

79.56%
按下载量换算982

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills