AI Photo Restorer Agent Skill — AI Pass API

In the era of autonomous agents, the ability to "see" is no longer enough. Modern AI agents must also be able to "repair." Whether you are building a genealogy bot, a digital archive manager, or a memorial application, the AI Photo Restorer Agent Skill provides a standardized interface for rehabilitating degraded historical media.

By leveraging the AI Pass API, developers can equip their agents with sophisticated computer vision capabilities that go beyond simple filters. This skill allows agents to analyze, reconstruct, and revitalize images that have been lost to time.

What the AI Photo Restorer Does

The Photo Restorer Skill is designed to act as a specialized "sub-routine" for larger agentic workflows. When an agent encounters an image file—whether from a Discord upload, a database scan, or a web scrape—it can trigger this skill to address four primary types of degradation:

1. General Aging: A holistic approach that balances noise reduction, color correction, and light enhancement.
2. Scratches & Tears: Targeted reconstruction of physical damage, effectively "filling in" missing pixels where paper has folded or cracked.
3. Fading: Intelligent contrast recovery and color re-saturation for photos that have lost their vibrancy due to UV exposure.
4. Blur & Soft Focus: Detail recovery and edge sharpening to bring fuzzy, out-of-focus subjects into clarity.

Setting Up Your Environment

To integrate this skill, your agent requires an authenticated connection to the AI Pass infrastructure.

1. Generate your Key: Visit the AI Pass Developer Panel to create your unique API key.
2. Environment Configuration: For security and portability, this skill strictly utilizes environment variables. Never hardcode your credentials.

Run the following command in your terminal (or add it to your .env file):

export AIPASS_API_KEY="your_secret_key_here"

Implementation: The Python Skill

The following implementation uses the gemini/gemini-3-pro-image-preview model via the AI Pass unified gateway. This model is specifically optimized for high-fidelity image reasoning and reconstruction tasks.

import requests
import base64
import os

def restore_photo(image_path, damage="general"):
    """
    Restores an image by identifying and fixing specific damage types.
    
    Args:
        image_path (str): Path to the local image file.
        damage (str): The type of restoration needed ('general', 'scratched', 'faded', 'blurry').
    
    Returns:
        str: The API response containing the processed image data or URL.
    """
    # CRITICAL: Securely fetch the API key from environment variables
    AIPASS_API_KEY = os.environ["AIPASS_API_KEY"]
    
    # Define prompt engineering logic based on damage type
    prompts = {
        "general": "Restore this old photo: remove scratches, fix fading, enhance clarity",
        "scratched": "Remove all scratches and tears, reconstruct damaged areas",
        "faded": "Restore faded colors and contrast, enhance naturally",
        "blurry": "Enhance sharpness, recover fine details, reduce blur"
    }
    
    # Encode the image to Base64 for transmission
    with open(image_path, "rb") as f:
        b64 = base64.b64encode(f.read()).decode()
    
    # Execute the request to the AI Pass API gateway
    r = requests.post(
        "https://aipass.one/apikey/v1/chat/completions",
        headers={
            "Authorization": f"Bearer {AIPASS_API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "model": "gemini/gemini-3-pro-image-preview",
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": prompts.get(damage, prompts["general"])},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}}
                    ]
                }
            ]
        }
    )
    
    return r.json()["choices"][0]["message"]["content"]

Strategic Use Cases

1. Genealogy Research Bots

Agents designed for family history research often ingest thousands of low-quality scans from public records. By integrating the restore_photo skill, the bot can automatically clean up a portrait of an ancestor before presenting it to the user, making facial recognition and document reading significantly more accurate.

2. Autonomous Digital Archives

Digital librarians and archive automation agents can use this skill to "triage" incoming collections. If the agent detects a high level of "noise" or "scratches" in a metadata field, it can autonomously run a restoration pass to ensure the digital twin of the historical asset is of the highest possible quality.

3. Memorial & Legacy Apps

Apps that focus on "In Memoriam" features can utilize agents to monitor user uploads. When a user uploads a weathered photo of a loved one, the agent can proactively offer a restored version, adding emotional value and improving the user experience without manual editing.

Conclusion

The AI Photo Restorer Agent Skill turns complex image processing into a simple, prompt-driven API call. By centralizing your AI needs through AI Pass, you ensure your agents have access to the latest vision models with minimal architectural overhead.

Start restoring history today. Get your API key at aipass.one and empower your agents to see the past more clearly.

# AI Photo Restorer Skill
Get key: https://aipass.one/panel/developer.html
Set $AIPASS_API_KEY env var.