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-java-jar-malwaregit 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-java-jar-malware)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-java-jar-malware"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-java-jar-malware/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-java-jar-malware"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-java-jar-malware.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 | $0.00083 | $0.00745 |
| Opus 5.5 | $0.00033 | $0.00298 |
| Sonnet 5.5 | $0.00017 | $0.00149 |
| Haiku 4.5 | $0.00008 | $0.00075 |
Grade A, and why
analyzing-java-jar-malware 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 1mo 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Java/JAR Malware
When to Use
- You have a malicious or suspicious
.jar(often a cross-platform RAT) and need to map its structure, entry point, obfuscation, and capability surface before decompiling. - You want to triage a Java payload without running the JVM.
Do not use java -jar to run it — that executes the malware. Treat the JAR as a ZIP and read
its contents statically.
Prerequisites
- The JAR file, read inertly. Optional: a Java decompiler (CFR, Procyon) for the next stage.
Safety & Handling
- Read the archive statically; never launch the JVM on the sample. Defang any URLs found.
Workflow
Step 1: Inventory the archive and entry point
python scripts/analyst.py inspect sample.jar
Lists .class files, embedded resources/payloads (nested JARs, scripts, encrypted blobs), and
reads META-INF/MANIFEST.MF for Main-Class/Premain-Class.
Step 2: Detect obfuscation and packers
Flags obfuscator fingerprints (Allatori, ProGuard, Zelix), single-character class/package names, and string-decryption indicators.
Step 3: Flag capability classes
Surface dangerous API usage in strings/constant pools: Runtime.exec/ProcessBuilder,
java.lang.reflect, URLClassLoader, javax.crypto, java.net.Socket, registry/persistence
helpers.
Step 4: Route to decompilation
Hand the key classes to a decompiler (CFR/Procyon) for source recovery; record IOCs.
Validation
- The manifest entry point is read and reported.
- Embedded payloads/nested archives are enumerated.
- Capability flags are backed by concrete class/string evidence.
Pitfalls
- String-encrypted samples where capability strings appear only after decryption.
- Multi-stage droppers that unpack a second JAR at runtime.
- Benign obfuscated commercial JARs — corroborate with capability and delivery context.
References
- See
references/api-reference.mdfor the inspector. - JVM/JAR format and ATT&CK T1027 references (linked in frontmatter).
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.
- 1mo ago First seen · 91 lines · 83 tokens per session scan A aa89f68360a6
analyzing-java-jar-malware is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 83 tokens to every session and 745 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-golang-malware-with-ghidra
Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a…
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF…
re-java
A workflow for reading compiled Java programs such as JAR, WAR, and CLASS files. It uses decompilers and archive tools to turn Java bytecode back into code that is easier to inspect.