Anthropic Cybersecurity Skills is a library of structured cybersecurity procedures for AI agents, covering security domains and mappings to established security frameworks. It is for authorized security analysis, penetration testing, incident response, research, defense, and education across compatible AI platforms. The catalogue entries package parts of this library as agent skills, instructions, or a plugin.
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 mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-memory-dumps-with-volatilitygit clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-SkillsWrote 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/mukul975/anthropic-cybersecurity-skills/analyzing-memory-dumps-with-volatility)<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-memory-dumps-with-volatility"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-memory-dumps-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/mukul975/anthropic-cybersecurity-skills/analyzing-memory-dumps-with-volatility"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-memory-dumps-with-volatility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- 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 302 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.
- critical YARA Match · line 305 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 | $0.00080 | $0.02960 |
| Opus 5 | $0.00040 | $0.01480 |
| Sonnet 5 | $0.00016 | $0.00592 |
| Haiku 4.5 | $0.00008 | $0.00296 |
Grade C, and why
analyzing-memory-dumps-with-volatility scanned grade C 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 11d 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 variableshighData exfiltration
Enumerating or grepping the environment for keys collects credentials unrelated to what the mod says it does.
# Extract environment variables Copies of this mod
7 near-identical copies found in the catalogue:
- analyzing-memory-dumps-with-volatility — 100% identical, 0 lines differ
- analyzing-memory-dumps-with-volatility — 100% identical, 0 lines differ
- analyzing-memory-dumps-with-volatility — 100% identical, 0 lines differ
- analyzing-memory-dumps-with-volatility — 98% identical, 29 lines differ
- analyzing-memory-dumps-with-volatility — 98% identical, 29 lines differ
- analyzing-memory-dumps-with-volatility — 98% identical, 29 lines differ
- analyzing-memory-dumps-with-volatility — 98% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Memory Dumps with Volatility
When to Use
- A compromised system's RAM has been captured and needs forensic analysis for malware artifacts
- Detecting fileless malware that exists only in memory without persistent disk artifacts
- Extracting encryption keys, passwords, or decrypted configuration from process memory
- Identifying process injection, DLL injection, or process hollowing in a compromised system
- Analyzing rootkit activity that hides from standard disk-based forensic tools
Do not use for disk image analysis; use Autopsy, FTK, or Sleuth Kit for disk forensics.
Prerequisites
- Volatility 3 installed (
pip install volatility3) with symbol tables for target OS - Memory dump file acquired from the target system (using WinPmem, LiME, or DumpIt)
- Knowledge of the source OS version for correct profile/symbol selection
- Sufficient disk space (memory dumps can be 4-64 GB)
- YARA rules for scanning memory for known malware signatures
- Strings utility for extracting readable strings from memory regions
Workflow
Step 1: Identify the Memory Dump Profile
Determine the operating system and version from the memory dump:
# Volatility 3: Automatic OS detection
vol3 -f memory.dmp windows.info
# List available plugins
vol3 -f memory.dmp --help
# If symbols are needed, download from:
# https://downloads.volatilityfoundation.org/volatility3/symbols/
# For Volatility 2 (legacy):
vol2 -f memory.dmp imageinfo
vol2 -f memory.dmp kdbgscan
Step 2: Enumerate Running Processes
List all processes and identify suspicious entries:
# List all processes
vol3 -f memory.dmp windows.pslist
# Process tree (parent-child relationships)
vol3 -f memory.dmp windows.pstree
# Scan for hidden/unlinked processes (rootkit detection)
vol3 -f memory.dmp windows.psscan
# Compare pslist vs psscan to find hidden processes
# Processes in psscan but not pslist are potentially hidden by rootkits
# Check for process hollowing
vol3 -f memory.dmp windows.pslist --dump
# Then verify the dumped EXE matches the expected binary on disk
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.
- 11d ago First seen · 314 lines · 80 tokens per session scan C 59e91aebae4d
analyzing-memory-dumps-with-volatility is a skill published in the GitHub repository mukul975/Anthropic-Cybersecurity-Skills (32,541 stars, last pushed 11d ago), licensed Apache-2.0. It adds 80 tokens to every session and 2,960 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (harvests environment variables). 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-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…
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…
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…
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…
analyzing-memory-dumps-with-volatility
A procedure for examining a captured computer's RAM with Volatility, a memory-forensics tool. It helps investigate activity that may exist only in memory, such as hidden processes, injected code, network connections, or credentials.
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.