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 meltedinhex/analyst-ai-pack --skill analyzing-malicious-onenote-and-html-smugglinggit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/analyzing-malicious-onenote-and-html-smuggling)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-malicious-onenote-and-html-smuggling"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-malicious-onenote-and-html-smuggling/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/meltedinhex/analyst-ai-pack/analyzing-malicious-onenote-and-html-smuggling"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-malicious-onenote-and-html-smuggling.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 | $0.00078 | $0.00807 |
| Opus 5 | $0.00039 | $0.00404 |
| Sonnet 5 | $0.00016 | $0.00161 |
| Haiku 4.5 | $0.00008 | $0.00081 |
Grade A, and why
analyzing-malicious-onenote-and-html-smuggling 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 11d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Malicious OneNote and HTML Smuggling
When to Use
- You have a OneNote (
.one) attachment suspected of hiding an embedded executable/script behind a "click to view" lure. - You have an HTML/SVG page that reconstructs and auto-downloads a payload from an embedded blob (HTML smuggling).
- You need to extract the hidden payload without triggering the lure.
Do not use OneNote or a browser to open these files for analysis — that is exactly the delivery mechanism. Carve the embedded data statically.
Prerequisites
- A static carver (Python) for embedded file signatures; the sample handled inertly.
Safety & Handling
- Never open the
.onein OneNote or the HTML in a browser. - Defang URLs and store carved payloads password-protected.
Workflow
Step 1: For OneNote — carve embedded files
OneNote stores attached files in the document. Scan for embedded file signatures (MZ, script
headers, archives) and the FileDataStoreObject GUIDs, and carve them out.
python scripts/analyst.py carve sample.one
Step 2: For HTML smuggling — find the embedded blob
Look for large base64/Blob/Uint8Array constructions, data: URIs, and a JS routine that
builds a Blob and triggers a download (msSaveOrOpenBlob, anchor download, createObjectURL).
Step 3: Reconstruct the payload statically
Decode the embedded base64/byte array (and any XOR/char-code layer) to recover the payload as data — without executing the page.
Step 4: Analyze and extract IOCs
Hash carved payloads, identify their type, defang any URLs, and route executables/scripts to the appropriate analysis workflow.
Validation
- Embedded payloads are carved/reconstructed from the inert file, not by opening it.
- The recovered payload's type is identified and hashed.
- The lure/trigger mechanism (fake button, Blob download) is documented.
Pitfalls
- Opening the OneNote/HTML to "see" the lure and executing the payload.
- Decoding only the first layer when the blob is additionally XOR/char-code encoded.
- Missing multiple embedded objects in a single OneNote page.
What ships with it
3 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.
- 11d ago First seen · 94 lines · 78 tokens per session scan A 4a2005c4bfa0
analyzing-malicious-onenote-and-html-smuggling is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 807 once invoked, about $0.0004 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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