photo-metadata-inspector

name

photo-metadata-inspector

description

Use whenever a user wants to inspect, audit, or check what's hidden in a photo BEFORE publishing or sharing it. Show-and-Tell pattern — reveals every EXIF, GPS, XMP, IPTC, and Photoshop block with privacy-risk classification (CRITICAL/HIGH/MEDIUM/LOW) and plain-English explanations, PLUS detects visible pixel-baked branding (logos/watermarks) that survives metadata scrubbing, PLUS provides reverse-image-search guidance to verify ownership. Generates a 3-page branded PDF report (Before/After comparison, detailed findings, ownership risk) THEN offers three choices (scrub / scrub+watermark / keep as-is). Triggers on phrases like "check this photo", "is this photo safe to share", "does this belong to me", "who owns this image", "inspect for privacy issues", "audit this image", or any photo upload with privacy/ownership intent. Auto-pulls branding from the IMM agent's brand cache.

Photo Metadata Inspector — Show & Tell

A privacy-first photo agent. Drop a photo → see exactly what's hidden inside → decide what to do about it. Show first, scrub second, never silently.

When this fires

• "Check this photo's metadata"
• "What's hidden in this image?"
• "Is this safe to post?"
• "Inspect this photo for privacy issues"
• "What does this photo reveal about me / my location / my device?"
• "Audit this image before I send it"
• "Show me the EXIF data"
• Any photo upload with privacy-investigation intent

The Show-and-Tell flow (always in this order)

Step 1 — Read and display metadata IMMEDIATELY

The moment a photo is dropped, before doing anything else, read all its metadata and show the user what's there. Be specific and dramatic — privacy is invisible until you make it visible.

import sys
sys.path.insert(0, 'scripts')
from metadata_reader import read_metadata, build_summary

findings = read_metadata('/mnt/user-data/uploads/photo.jpg')
print(build_summary(findings))

The skill classifies each field by privacy risk:

Tier	Meaning	Examples
CRITICAL	Direct identifier	GPS coordinates, owner name, device serial number
HIGH	Narrows identity/location/time	Timestamps, timezone, camera make/model, software used
MEDIUM	Combinable risk	Altitude, sub-second precision, exposure settings
LOW	Technical-only	Color profile, image dimensions

Step 2 — Show the findings inline (markdown)

Present findings as a markdown table grouped by risk tier, with the value and why it matters for each field. Make GPS coordinates clickable (Google Maps link).

If GPS is present, lead with: 🌍 GPS LOCATION EXPOSED: 41.0793, -85.1394 → [view on map]

Then a markdown table:

Risk	Field	Value	Why it matters
🔴 CRITICAL	GPS / GPSLatitude	41° 4' 45" N	Exact location where photo was taken
🟠 HIGH	DateTimeOriginal	2024-08-15 14:32:11	When photo was taken (down to the second)
🟠 HIGH	Make + Model	Apple, iPhone 15 Pro	Device fingerprint
🟡 MEDIUM	LensInfo	...	...
⚪ LOW	ColorSpace	sRGB	Color profile (no privacy risk)

Step 3 — Build the branded PDF Before/After report

from scrubber import scrub
from report_builder import build_inspection_report

# Build a clean preview for the "After" side (don't commit until user chooses)
clean_preview = scrub(photo_path, "/tmp/inspector_preview_clean.jpg")

build_inspection_report(
    photo_path=photo_path,
    clean_photo_path=clean_preview,  # for side-by-side comparison
    findings=findings,
    output_path="/mnt/user-data/outputs/photo_inspection_<name>.pdf",
    action_taken="inspected",  # important: nothing has happened yet
)

The PDF auto-loads branding from /mnt/user-data/uploads/.imm_brand.json if it exists (same brand cache as the IMM spec agent). Otherwise uses neutral gold-on-dark.

Step 4 — Present the three choices

After showing findings + PDF, ALWAYS end with exactly this prompt structure:

Here's what I found. What would you like me to do?

1. Scrub — Strip all metadata, return a clean copy 2. Scrub + Watermark — Strip metadata AND apply your brand watermark (uses photo-watermark skill) 3. Keep as-is — Return the original unchanged (you decided the metadata is fine)

Wait for the user's reply. Don't act preemptively.

Step 5 — Execute the chosen action

Based on the user's reply:

• Scrub → commit scrubber.scrub(), save final cleaned photo to /mnt/user-data/outputs/, rebuild PDF with action_taken="scrubbed"
• Scrub + Watermark → call scrubber, then invoke the photo-watermark skill on the cleaned photo, rebuild PDF with action_taken="scrubbed_watermarked"
• Keep as-is → no file action needed, rebuild PDF with action_taken="kept_as_is" and a warning banner

Always re-present the final PDF with the action confirmed.

Critical rules

• NEVER scrub before showing the metadata. The whole point of Show-and-Tell is to teach the user what was there. Silent scrubbing defeats the purpose.
• NEVER skip the three-choice prompt. The user owns the decision.
• Always classify by privacy risk — don't just dump a JSON blob of EXIF tags. The "why it matters" column is the value-add.
• Make GPS coordinates a clickable Google Maps link — concrete > abstract. "41.0793, -85.1394" means nothing; clicking through to a satellite view of someone's home address makes it real.
• Brand cache is shared with the IMM agent at /mnt/user-data/uploads/.imm_brand.json. Don't duplicate the brand-extraction flow here — if no cache, just use neutral defaults.
• The PDF report is the deliverable — both the inline markdown summary AND the PDF should be presented every time.
• If a photo has zero metadata, still produce the report — say "✓ Already clean" and explain what zero metadata means.

Anti-patterns

• ❌ Scrubbing the photo first and saying "I cleaned it for you" — defeats the educational purpose
• ❌ Skipping the PDF and only showing inline markdown — markdown disappears, PDF is the proof
• ❌ Showing all fields as one big undifferentiated list — must be classified by risk
• ❌ Using technical jargon ("GPSLatitudeRef", "FNumber") without explanation — every field needs plain-English "why it matters"
• ❌ Asking for the user's website on first run — this skill uses the IMM agent's cache, doesn't re-prompt
• ❌ Auto-applying a watermark — that's a separate skill (photo-watermark), only invoked if the user picks option 2

File map

• scripts/metadata_reader.py — comprehensive metadata extraction + risk classification
• scripts/scrubber.py — strips ALL metadata, rebuilds from raw pixels
• scripts/report_builder.py — branded PDF Before/After report generator
• references/risk_classification.md — full table of EXIF tags and their privacy tiers

Companion skill

If the user chooses "Scrub + Watermark", invoke the photo-watermark skill on the scrubbed photo. The watermark skill is independent — it can also be used standalone for photos that don't need metadata stripping.