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 UseOSINT/Skills --skill find-the-original-imagegit clone --depth 1 https://github.com/UseOSINT/SkillsWrote 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/useosint/skills/find-the-original-image)<a href="https://agentmods.dev/skills/useosint/skills/find-the-original-image"><img src="https://agentmods.dev/badge/skills/useosint/skills/find-the-original-image/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/useosint/skills/find-the-original-image"><img src="https://agentmods.dev/badge/skills/useosint/skills/find-the-original-image.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00141 | $0.02815 |
| Opus 5 | $0.00071 | $0.01407 |
| Sonnet 5 | $0.00028 | $0.00563 |
| Haiku 4.5 | $0.00014 | $0.00281 |
Grade A, and why
find-the-original-image 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 10d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Find the original image
The goal is almost never "find a match". It is find the earliest publication and read its page. A match tells you the image exists elsewhere; the earliest page tells you the photographer, the date, the caption, and the names — which is what you actually pivot on.
The beginner mistake: uploading the full frame to one engine, getting nothing, and concluding the image is unindexed. Cropping to one distinctive object and re-searching finds things full-frame search cannot.
Pick your engine by what you are holding
| You have | Start with | Why |
|---|---|---|
| A face | Yandex | Its index is built around facial similarity, so it returns different people who look alike and the same person in other photographs. No other general engine does this. |
| A face, and Yandex fails | A dedicated face engine (see below) | Only after you have cleared the legal and consent questions. |
| A street scene outside North America / Western Europe | Yandex | Deeply indexed Russian, Central Asian, Eastern European, Turkish and Chinese web content that Google under-crawls. |
| A product, book cover, artwork, plant, animal | Google Lens | Object and entity recognition, tied to Shopping and Knowledge Graph. |
| Text inside the image | Google Lens | It OCRs the frame and lets you search the extracted string. Often the text is the answer and the image search is irrelevant. |
| A landmark or a well-known building | Google Lens | Landmark classification is its strongest single feature. |
| A specific region of a cluttered photo | Bing Visual Search | Draw a box on the uploaded image and it re-searches only that region — the fastest crop-and-retry loop of any engine. |
| A press photo, meme, or anything you suspect is old | TinEye | The only major engine that sorts by oldest and that reliably surfaces modified copies. |
| Chinese-language or China-hosted content | Baidu image search | Coverage the others simply do not have. |
Run at least three. They disagree constantly, and that disagreement is information: TinEye finding an exact copy from years back while Lens finds only recent reposts is the signature of recycled media.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 215 lines · 141 tokens per session scan A aa86cabffa6f
find-the-original-image is a skill published in the GitHub repository UseOSINT/Skills (33 stars, last pushed 1mo ago), licensed MIT. It adds 141 tokens to every session and 2,815 once invoked, about $0.0007 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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