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-malware-in-memory-with-volatility3git 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-malware-in-memory-with-volatility3)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-malware-in-memory-with-volatility3"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-malware-in-memory-with-volatility3/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-malware-in-memory-with-volatility3"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-malware-in-memory-with-volatility3.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 | $0.00876 |
| Opus 5 | $0.00036 | $0.00438 |
| Sonnet 5 | $0.00015 | $0.00175 |
| Haiku 4.5 | $0.00007 | $0.00088 |
Grade A, and why
analyzing-malware-in-memory-with-volatility3 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 10d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Malware in Memory with Volatility 3
When to Use
- You have a RAM image from a suspected-infected host and need to find malicious activity.
- Disk artifacts are insufficient (fileless/in-memory malware) and you need volatile evidence.
- You want to extract injected code, command lines, or network connections for analysis.
Do not use Volatility plugins blindly without an order of investigation — start broad (processes, network) before deep per-process dumps.
Prerequisites
- Volatility 3 (
pip install volatility3) with appropriate symbol tables. - A memory image acquired with a sound tool (WinPmem, LiME, or hypervisor snapshot).
- Knowledge of the source OS/version to select the right symbols.
Safety & Handling
- Work on a copy of the image; preserve the original with a recorded hash.
- Treat any dumped executable region as a live sample — store and handle it accordingly.
Workflow
Step 1: Enumerate processes and spot anomalies
vol -f memory.raw windows.pslist
vol -f memory.raw windows.pstree
Look for unusual parents (Word spawning cmd/powershell), masquerading names
(scvhost.exe), processes with no disk path, and orphaned children.
Step 2: Hunt injected code
vol -f memory.raw windows.malfind
malfind flags private, executable, RWX regions with no backing file — classic injection.
Note the PID and base address for dumping.
Step 3: Review network connections
vol -f memory.raw windows.netscan
Correlate listening/established connections with suspicious PIDs and the C2 endpoints from other analysis.
Step 4: Check modules, handles, and persistence
Examine loaded DLLs (windows.dlllist), services, and registry (windows.registry.*) for
persistence and unexpected modules.
Step 5: Dump artifacts
Dump the suspicious process or injected region for static/RE analysis:
vol -f memory.raw windows.dumpfiles --pid <pid>
The helper script parses Volatility's JSON renderer output to highlight injection candidates.
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.
- 10d ago First seen · 120 lines · 73 tokens per session scan A c12855de173a
analyzing-malware-in-memory-with-volatility3 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 876 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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