Reconstrucción de imagen a prompt

# Image-to-Prompt Reconstruction

## When to use

Reverse-engineer an authorized reference image into a reusable prompt template and a concrete generation prompt.

## Workflow

1. Confirm the user owns the input or has permission to process it. Collect the source material, audience, output format, and acceptance criteria.
2. Separate observed facts and user-provided constraints from inference. Ask for missing information instead of inventing it.
3. Translate the brief into subject, composition, style, lighting, color, text, aspect ratio, and negative constraints.
4. Use <skill-tag>skill:eaa2a601-3d68-59cc-849c-be2dd4948ead</skill-tag> for the connected visual workflow, then compare the result with the approved brief.

## Output contract

Return the input assumptions, the approved plan, the generated or analyzed result, uncertainty and limitations, and a concise verification checklist. Preserve source links and provider-returned identifiers when available. Stop if the required connected capability is unavailable; do not substitute local scripts, package installation, or embedded credentials.

## Safety and review

Do not upload private or sensitive media without explicit authorization. Do not claim certainty beyond the evidence. Require review before externally visible, paid, destructive, or account-changing actions.

## Source and license

This Aident-native re-authoring is based on [the pinned upstream source](https://github.com/fluentlc/shiny-skills/tree/9f6363042deb644e4fc79a4739cd19e409ac6e84/image-to-prompt-skill), discovered through [ModelScope](https://modelscope.cn/skills/fluentlc/image-to-prompt-skill), and used under MIT. It preserves the upstream workflow goal while removing local executable, credential, and package assumptions.