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 26zl/cybersec-toolkit --skill conducting-memory-forensics-with-volatilitygit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/conducting-memory-forensics-with-volatility)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/conducting-memory-forensics-with-volatility"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/conducting-memory-forensics-with-volatility/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/26zl/cybersec-toolkit/conducting-memory-forensics-with-volatility"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/conducting-memory-forensics-with-volatility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to critical
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- critical YARA Match · line 243 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- high YARA Match · line 42 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
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 | $0.00087 | $0.02639 |
| Opus 5 | $0.00044 | $0.01319 |
| Sonnet 5 | $0.00017 | $0.00528 |
| Haiku 4.5 | $0.00009 | $0.00264 |
Grade B, and why
conducting-memory-forensics-with-volatility scanned grade B with 1 finding 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 6d 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.
Harvests environment variablesmediumData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
### Step 6: Extract Credentials and Artifacts Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Copies of this mod
1 near-identical copy found in the catalogue:
- conducting-memory-forensics-with-volatility — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conducting Memory Forensics with Volatility
When to Use
- An endpoint has been contained during an active incident and volatile evidence must be preserved
- EDR alerts suggest process injection or fileless malware that only exists in memory
- Encryption keys need to be recovered from a ransomware-infected system before shutdown
- Credential theft (Mimikatz, LSASS dumping) is suspected and evidence must be confirmed
- A rootkit or kernel-level compromise is suspected and disk-based analysis is insufficient
Do not use for analyzing disk images or file system artifacts; use disk forensics tools (Autopsy, FTK) for those tasks.
Prerequisites
- Memory acquisition tool deployed or available: WinPmem, Magnet RAM Capture, DumpIt, or AVML (Linux)
- Volatility 3 installed with Python 3.8+ and required symbol tables
- Sufficient storage for memory dumps (equal to system RAM size, typically 8-64 GB)
- YARA rules for malware detection in memory (Florian Roth's signature-base, custom rules)
- Reference baseline of normal processes and DLLs for the OS version being analyzed
- Chain of custody documentation for evidence handling
Workflow
Step 1: Acquire Memory Image
Capture RAM from the target system using a forensically sound method:
Windows (WinPmem):
winpmem_mini_x64.exe output.raw
Windows (Magnet RAM Capture):
MagnetRAMCapture.exe
# GUI-based, select output path, generates .raw file
Windows (DumpIt):
DumpIt.exe
# Creates memory dump in current directory automatically
Linux (AVML - Acquire Volatile Memory for Linux):
./avml output.lime
Document acquisition metadata:
Acquisition Record:
━━━━━━━━━━━━━━━━━
Target Host: WKSTN-042
RAM Size: 16 GB
Dump File: WKSTN-042_20251115_1445.raw
Dump Size: 16,843,612,160 bytes
SHA-256: a4b3c2d1e5f6...
Acquisition Tool: WinPmem 4.0
Acquired By: [Analyst Name]
Timestamp: 2025-11-15T14:45:00Z
Step 2: Identify the Operating System and Profile
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.
- 6d ago First seen · 288 lines · 87 tokens per session scan B 1959b65c644e
conducting-memory-forensics-with-volatility is a skill published in the GitHub repository 26zl/cybersec-toolkit (51 stars, last pushed yesterday), licensed MIT. It adds 87 tokens to every session and 2,639 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (harvests environment variables). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
conducting-memory-forensics-with-volatility
Use when performing memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for…
conducting-memory-forensics-with-volatility
Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests…
conducting-memory-forensics-with-volatility
Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
detecting-process-injection-in-memory
Detects process injection in a memory image by identifying private executable regions with no file backing, RWX protections, and modified entry points using Volatility 3 malfind-style analysis. Activates for requests to detect process injection, find injected code in memory, or triage suspicious executable regions in…
performing-memory-forensics-with-volatility3
Use when analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity. Use when analyzeing volatile memory dumps using volatility 3 to extract running.