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Location Aware Backgrounds

作者 Chad Newbry · GitHub ↗ · v1.0.2 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install location-aware-backgrounds
功能描述
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 w...
使用说明 (SKILL.md)

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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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
  7. 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.

安全使用建议
This skill's behavior is ambiguous: before installing or supplying secrets, ask the skill author to explain (1) why GEMINI_API_KEY is required and exactly how/where that key will be used (which hostname/endpoints receive data), (2) what the 'uv' binary is expected to do and why it's necessary, and (3) how 'nano-banana-pro' is invoked (CLI vs API) and what credentials it needs. Do not provide a general-purpose GEMINI/Google API key until the author confirms minimal, scoped permissions and a clear network endpoint. If you must try it, use a scoped test key and run in a sandboxed environment; review the homepage and vendor docs for nano-banana-pro to confirm the integration path and trustworthiness. If the author cannot justify the GEMINI_API_KEY or 'uv' requirement, treat the skill as unsafe to use.
功能分析
Type: OpenClaw Skill Name: location-aware-backgrounds Version: 1.0.2 The skill is designed to generate location-aware background images using a specific rendering tool ($nano-banana-pro). The instructions in SKILL.md and references/prompt-patterns.md are well-structured and include explicit safety boundaries, such as prohibiting the silent reading of local files or capturing screenshots without user consent. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found.
能力评估
Purpose & Capability
The skill claims to render images through 'nano-banana-pro' and to use only user-supplied files or explicit lookups. However, the registry metadata requires a GEMINI_API_KEY and the 'uv' binary even though SKILL.md never references using Gemini or the 'uv' binary. It's unclear why a Gemini API key (typically associated with a different provider) or a 'uv' binary are necessary for a renderer named 'nano-banana-pro'. This mismatch suggests the declared requirements are not justified by the stated purpose.
Instruction Scope
The SKILL.md stays focused on generating finished images, saving files, and limits file/system reads to user-supplied inputs (good). However, it instructs the agent to 'invoke $nano-banana-pro' without specifying how (CLI, API endpoint, or what credentials to pass). The lack of concrete integration details combined with the unexplained declared env var/binary leaves ambiguity about what the agent will actually do at runtime.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so there is no author-provided installer or archive to evaluate. That minimizes disk-write installation risk.
Credentials
The skill requires a single primary credential GEMINI_API_KEY but the SKILL.md does not mention Gemini or explain why that credential is needed. There is no justification for requesting that key from the instructions. Requiring a named API key without showing how it is used is disproportionate and should be explained before granting the secret.
Persistence & Privilege
always is false and the skill does not request persistent installation or changes to other skills or system-wide config. Autonomous invocation is allowed by default (not flagged by itself) and there are no metadata signs of elevated persistence.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install location-aware-backgrounds
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /location-aware-backgrounds 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.2
Shift the shared skill from prompt-only guidance to end-to-end image generation through Nano Banana Pro, keeping location selection reusable and Tongue-specific styling outside the shared package.
v1.0.1
Tighten scope: prompt writing by default, explicit approval for live lookups, screenshots, local files, and generation tools.
v1.0.0
Initial release for place-aware UI background generation, landmark selection, and prompt shaping for image models.
元数据
Slug location-aware-backgrounds
版本 1.0.2
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Location Aware Backgrounds 是什么?

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 w... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 128 次。

如何安装 Location Aware Backgrounds?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install location-aware-backgrounds」即可一键安装,无需额外配置。

Location Aware Backgrounds 是免费的吗?

是的,Location Aware Backgrounds 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Location Aware Backgrounds 支持哪些平台?

Location Aware Backgrounds 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Location Aware Backgrounds?

由 Chad Newbry(@chadnewbry)开发并维护,当前版本 v1.0.2。

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