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 Bynn-Intelligence/skills --skill bynn-document-fraudgit clone --depth 1 https://github.com/Bynn-Intelligence/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/bynn-intelligence/skills/bynn-document-fraud)<a href="https://agentmods.dev/skills/bynn-intelligence/skills/bynn-document-fraud"><img src="https://agentmods.dev/badge/skills/bynn-intelligence/skills/bynn-document-fraud/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/bynn-intelligence/skills/bynn-document-fraud"><img src="https://agentmods.dev/badge/skills/bynn-intelligence/skills/bynn-document-fraud.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00102 | $0.01486 |
| Opus 5 | $0.00051 | $0.00743 |
| Sonnet 5 | $0.00020 | $0.00297 |
| Haiku 4.5 | $0.00010 | $0.00149 |
Grade A, and why
bynn-document-fraud scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST https://api.bynn.com/v1/documents \ How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document fraud detection
One submission runs the full forensic pipeline: forgery template matching, tampering and manipulation detection, font and layout consistency, signature validation, deepfake and AI-generation scoring, MRZ and barcode parsing, EXIF analysis, classification, and an overall risk score.
This is asynchronous. Submitting returns a receipt, not a verdict.
Base: https://api.bynn.com/v1. Private key on both calls.
1. Submit
Multipart, which is what you want for anything of real size:
curl -X POST https://api.bynn.com/v1/documents \
-H "Authorization: Bearer <YOUR_PRIVATE_KEY>" \
-F "file=@/path/to/document.pdf" \
-F "reference_id=order-8123"
Or JSON with strict base64, no line breaks and no data: prefix:
curl -X POST https://api.bynn.com/v1/documents \
-H "Authorization: Bearer <YOUR_PRIVATE_KEY>" \
-H "Content-Type: application/json" \
-d '{ "document_base64_strict": "<...>", "reference_id": "order-8123" }'
Send file or document_base64_strict, not both.
Response:
{ "submission_id": "document_...", "document_id": "...", "status": "received" }
submission_id is what you poll. Store it.
Submit the original file, never a derivative
Do not resize, crop, re-encode, re-compress, or otherwise process the document before submitting it. Send the bytes exactly as you received them.
Tampering detection, AI-generation scoring, and deepfake analysis all work on traces the manipulation left in the file: compression and quantization artifacts, resampling patterns, sensor noise, font and edge inconsistencies, and EXIF. Those traces live in the exact bytes. Resizing resamples them away, re-encoding overwrites them with your own encoder's artifacts, and stripping metadata removes evidence outright.
The failure mode is quiet and dangerous: a processed forgery comes back looking clean.
- Store the original upload and submit that, not a thumbnail or a normalized copy.
- Do not flatten a PDF to an image, and do not re-render it. Send the PDF.
- Do not run the file through an image optimizer, a screenshot, or a messaging app.
- If a file exceeds the size limit, reject or escalate it. Do not shrink it and submit anyway. A downscaled submission can return a false clean verdict.
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.
- 9d ago First seen · 161 lines · 102 tokens per session scan A 90fd1df5b989
bynn-document-fraud is a skill published in the GitHub repository Bynn-Intelligence/skills (0 stars, last pushed 16d ago), licensed MIT. It adds 102 tokens to every session and 1,486 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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