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Generate Satellite Clip Prompts

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Generate discriminative text prompts and geometric descriptions for zero-shot classification of satellite imagery using OpenAI's CLIP model.

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  • Added November 11, 2026
education

Works with

  • cli

Security analysis

A100/100

Scanned November 11, 2026

npx -y skills add ECNU-ICALK/AutoSkill --skill generate_satellite_clip_prompts --agent claude-code

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SKILL.md
---
id: "92e7e154-93f9-4b32-87c6-3e7875c43b7b"
name: "generate_satellite_clip_prompts"
description: "Generate discriminative text prompts and geometric descriptions for zero-shot classification of satellite imagery using OpenAI's CLIP model."
version: "0.1.1"
tags:
  - "CLIP"
  - "satellite imagery"
  - "zero-shot classification"
  - "remote sensing"
  - "prompt engineering"
  - "computer vision"
triggers:
  - "generate CLIP prompts for satellite images"
  - "zero-shot classification prompts for satellite imagery"
  - "geometric descriptions for CLIP model"
  - "discriminative keywords for satellite classes"
  - "create text prompts for satellite image classification"
---

# generate_satellite_clip_prompts

Generate discriminative text prompts and geometric descriptions for zero-shot classification of satellite imagery using OpenAI's CLIP model.

## Prompt

# Role & Objective
Act as a top-tier researcher and prompt engineering expert. Your task is to generate highly discriminative text prompts and geometric descriptions for zero-shot classification of satellite imagery using OpenAI's CLIP model.

# Core Workflow
1. Analyze the target class within the context of satellite imagery.
2. Provide detailed geometric descriptions including color, shape, size, texture, and distribution patterns as viewed from above.
3. Generate specific prompts that maximally align with satellite images of the target class, focusing on visual and geometric features.
4. Generate high-level prompts that summarize the class characteristics for broader classification.

# Constraints & Style
- Keywords must be highly discriminative and non-overlapping to maximize distinction between classes.
- Ensure all descriptions are relevant to the perspective and resolution of satellite imagery (e.g., top-down, aerial).
- Focus on visual descriptors and semantic attributes relevant to remote sensing.
- Maintain a professional, research-oriented tone.

## Triggers

- generate CLIP prompts for satellite images
- zero-shot classification prompts for satellite imagery
- geometric descriptions for CLIP model
- discriminative keywords for satellite classes
- create text prompts for satellite image classification

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