performing-memory-forensics-with-volatility3

performing-memory-forensics-with-volatility3 is a skill for Claude Code from oyi77/1ai-skills. It costs 53 tokens per session (927 once invoked), scanned C, original, MIT.

A guide to examining a computer's RAM, the short-term memory that holds running programs, network connections, and other live data, with Volatility 3.

In plain words
What is it for?
It is for incident response and forensic analysis of memory dumps from compromised or suspicious systems.
Why use it?
Important evidence may disappear when a computer is shut down and may not exist on its disk. Memory analysis can reveal hidden malware, injected code, credentials, and active connections.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 209 skills, 4 commands shipped together

Good fit It is for incident response and forensic analysis of memory dumps from compromised or suspicious systems.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/performing-memory-forensics-with-volatility3
Install

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.

Any agent
npx skills add oyi77/1ai-skills --skill performing-memory-forensics-with-volatility3
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 209 skills, 4 commands.

Wrote 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.

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README.md
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Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 927 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.00927
Opus 5 $0.00026 $0.00464
Sonnet 5 $0.00011 $0.00185
Haiku 4.5 $0.00005 $0.00093

Measured 7d ago against content hash 7bdb58e98a5a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade C, and why

performing-memory-forensics-with-volatility3 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 7d 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.

- When you need to extract credentials, encryption keys, or network connections from memory
cybersecurity/_deprecated/performing-memory-forensics-with-volatility3/SKILL.md · 111 lines

How it starts

The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performing Memory Forensics With Volatility3

Overview

Cybersecurity skill for performing memory forensics with volatility3. Follows industry best practices and security standards.

When to Use

Trigger phrases:

  • "performing memory forensics with volatility3"

  • "Analyze volatile memory dumps using Volatility 3 to extract running processes, n"

  • When analyzing a RAM dump from a compromised or suspect system

  • During incident response to identify running malware, injected code, or rootkits

  • When you need to extract credentials, encryption keys, or network connections from memory

  • For detecting process hollowing, DLL injection, or hidden processes

  • When disk-based forensics alone is insufficient and volatile data is critical

When NOT to Use

  • When you lack proper authorization for testing
  • For production systems without change management
  • When the task requires legal or compliance expertise beyond technical scope

Prerequisites

  • Python 3.7+ installed
  • Volatility 3 framework installed (pip install volatility3)
  • Memory dump in raw, ELF, or crash dump format
  • Appropriate symbol tables (ISF files) for the target OS version
  • Sufficient disk space for analysis output (2-3x memory dump size)
  • Optional: YARA rules for malware scanning in memory

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
  1. Plan Operations — Define objectives, scope, and success criteria for memory forensics operations.
  2. Prepare Environment — Set up tools, access, and data sources required for memory forensics.
  3. Execute Core Workflow — Use volatility3 to perform memory forensics operations following established procedures.
  4. Validate Results — Verify that results meet quality standards and objectives.
  5. Report Findings — Document results, observations, and recommendations.
  6. Follow Up — Track remediation actions and verify fixes where applicable.

Read the full file on GitHub · 111 lines

Changes

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.

  1. 7d ago First seen · 111 lines · 53 tokens per session scan C 7bdb58e98a5a

Subscribe to this mod's changes

performing-memory-forensics-with-volatility3 is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 927 once invoked, about $0.0003 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 2026-09-04.

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