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-pdfsgit 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-pdfs)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-malicious-pdfs"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-malicious-pdfs/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-pdfs"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-malicious-pdfs.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 | $0.00073 | $0.00762 |
| Opus 5 | $0.00036 | $0.00381 |
| Sonnet 5 | $0.00015 | $0.00152 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
analyzing-malicious-pdfs 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 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.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Malicious PDFs
When to Use
- You have a suspicious PDF (often from phishing) and must determine if it is weaponized.
- You need to find auto-executing actions, embedded JavaScript, launch actions, or embedded files.
- You want to extract and decode suspicious streams safely.
Do not use a PDF reader to "just look" — opening in a viewer can trigger the very actions you are investigating. Parse the structure statically.
Prerequisites
- A PDF object parser (Python, or pdfid/pdf-parser/peepdf); the sample handled inertly.
Safety & Handling
- Never open the PDF in a graphical reader; parse the raw object structure only.
- Defang any extracted URLs and store embedded payloads password-protected.
Workflow
Step 1: Triage the object inventory
Count high-risk keywords: /JavaScript, /JS, /OpenAction, /AA, /Launch, /EmbeddedFile,
/URI, /RichMedia. Their presence (especially auto-actions) flags weaponization.
python scripts/analyst.py triage sample.pdf
Step 2: Locate auto-execution triggers
Resolve /OpenAction and /AA (additional actions) to see what runs on open, and any /Launch
actions that spawn external programs.
Step 3: Extract and decode streams
Pull JavaScript and suspicious streams, applying filters (/FlateDecode, /ASCIIHexDecode) to
get the cleartext; deobfuscate layered JS as needed.
Step 4: Recover payloads and IOCs
Extract embedded files and URIs; hash payloads, defang URLs, and route extracted executables to PE analysis.
Validation
- Auto-execution triggers (
/OpenAction,/AA,/Launch) are resolved to concrete actions. - JavaScript/streams are decoded through their filters, not left encoded.
- Embedded payloads and URIs are extracted, hashed, and defanged.
Pitfalls
- Opening the PDF in a reader and triggering the payload.
- Missing object-stream (
/ObjStm) compressed objects that hide the malicious content. - Stopping at the first JS layer when it is multiply obfuscated.
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.
- 9d ago First seen · 92 lines · 73 tokens per session scan A 50955fe4c890
analyzing-malicious-pdfs is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 762 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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A Chinese-language skill for examining suspicious PDF files with peepdf, pdfid, and pdf-parser. It is intended for static malware analysis, which studies a file without running it.