Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add drolosoft/immich-photo-manager --skill duplicate-reportgit clone --depth 1 https://github.com/drolosoft/immich-photo-managerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/drolosoft/immich-photo-manager/duplicate-report)<a href="https://agentmods.dev/skills/drolosoft/immich-photo-manager/duplicate-report"><img src="https://agentmods.dev/badge/skills/drolosoft/immich-photo-manager/duplicate-report/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/drolosoft/immich-photo-manager/duplicate-report"><img src="https://agentmods.dev/badge/skills/drolosoft/immich-photo-manager/duplicate-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.02402 |
| Opus 5 | $0.00049 | $0.01201 |
| Sonnet 5 | $0.00020 | $0.00480 |
| Haiku 4.5 | $0.00010 | $0.00240 |
Grade A, and why
duplicate-report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Duplicate Report
⚠️ Connection Required: ALWAYS CHECK FIRST
Before doing ANYTHING else in this skill, call ping on the Immich MCP server.
- If
pingsucceeds → proceed with the skill normally. - If
pingfails or the MCP tools are not available → STOP. Do not continue. Tell the user:
❌ Immich is not connected. This plugin needs a running Immich MCP server to work.
Run /setup-immich-photo-manager to configure your Immich connection. You'll need:
- Your Immich server URL (e.g.,
http://192.168.1.100:2283)- An Immich API key (how to create one)
- The MCP server configured (see /setup-immich-photo-manager)
Nothing in this plugin will work until the connection is configured.
Do NOT skip this check. Do NOT try to run any other tool first. Always ping, always block if it fails.
Generate a full duplicate analysis of an Immich photo library. Uses perceptual hashing to find visually identical photos even when they have different checksums (common when photos are exported from Apple Photos and Google Photos).
Why Perceptual Hashing?
When users import the same photo library from multiple sources (Apple Photos export, Google Takeout, manual folder copies), the files are often re-encoded by each platform. This means:
- Checksums differ: same photo, different binary → SHA/MD5 won't match
- Immich's built-in CLIP duplicate detection uses too strict a threshold for re-encoded content
- Filename matching catches only a fraction (filenames often differ across platforms)
Perceptual hashing (pHash) computes a fingerprint based on the visual content of the image, not the binary data. Two re-encoded copies of the same photo produce the same perceptual hash.
Prerequisites
The user's machine needs:
pip3 install Pillow imagehash pillow-heif --break-system-packages
Pillow: image loadingimagehash: perceptual hashingpillow-heif: HEIC/HEIF support (critical for Apple Photos)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago Changed 53176902ee22
- 7d ago Changed · +8 lines 5223708f9b4c
- 10d ago First seen · 228 lines · 99 tokens per session scan A bac3445ed699
duplicate-report is a skill published in the GitHub repository drolosoft/immich-photo-manager (47 stars, last pushed 6d ago), licensed MIT. It adds 99 tokens to every session and 2,402 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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