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-dotnet-malware-internalsgit 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-dotnet-malware-internals)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-dotnet-malware-internals"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-dotnet-malware-internals/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-dotnet-malware-internals"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-dotnet-malware-internals.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 | $0.00072 | $0.00792 |
| Opus 5 | $0.00036 | $0.00396 |
| Sonnet 5 | $0.00014 | $0.00158 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
analyzing-dotnet-malware-internals 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing .NET Malware Internals
When to Use
- A sample is a managed (.NET) assembly — confirmed by a CLR header /
mscoreeimport orBSJBmetadata signature. - You need readable C# from MSIL and want to defeat .NET-specific obfuscation.
- A loader uses reflection (
Assembly.Load) to run an in-memory payload you must recover.
Do not use native disassembly workflows (Ghidra for x86) as the primary tool — managed code decompiles far more cleanly with a .NET decompiler.
Prerequisites
- ILSpy / dnSpyEx for decompilation and (with dnSpyEx) managed debugging.
- de4dot or equivalent for known protectors; familiarity with common .NET obfuscators.
Workflow
Step 1: Confirm it is managed
Check for the CLR runtime header and the BSJB metadata magic:
python scripts/analyst.py identify sample.exe
Step 2: Decompile
Open in ILSpy/dnSpyEx and review the entry point, Main, and module initializer
(<Module>.cctor), which protectors often abuse.
Step 3: Handle obfuscation
Recognize and undo common schemes:
- String encryption — a decryptor method called everywhere; run/trace it to recover plaintext (de4dot can often static-decrypt).
- Control-flow flattening — follow the dispatcher state machine.
- Proxy methods / renaming — rely on decompiler analysis rather than names.
Step 4: Trace reflection loaders
Find Assembly.Load(byte[]) / Activator.CreateInstance; dump the byte array argument at
runtime (managed debugger breakpoint) to recover the real second-stage assembly, then recurse.
Step 5: Analyze the payload
Decompile the recovered stage; extract C2, configuration, and capabilities for the report.
Validation
- Decompiled C# is coherent (named or recovered) and the entry path is traced.
- Encrypted strings are recovered to plaintext.
- The reflection-loaded stage is dumped and itself decompiles.
Pitfalls
- Treating the loader as the payload — managed malware is frequently multi-stage.
- Ignoring the module initializer where protectors install hooks.
- Static-decrypting strings when the scheme is runtime-keyed; debug and dump instead.
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 · 97 lines · 72 tokens per session scan A 1f743167cb69
analyzing-dotnet-malware-internals is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 792 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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