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 oyi77/1ai-skills --skill performing-memory-forensics-with-volatility3git clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/performing-memory-forensics-with-volatility3)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/performing-memory-forensics-with-volatility3"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/performing-memory-forensics-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/oyi77/1ai-skills/performing-memory-forensics-with-volatility3"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/performing-memory-forensics-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 | $0.00053 | $0.00927 |
| Opus 5 | $0.00026 | $0.00464 |
| Sonnet 5 | $0.00011 | $0.00185 |
| Haiku 4.5 | $0.00005 | $0.00093 |
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 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
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During incident response to identify running malware, injected code, or rootkits
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When you need to extract credentials, encryption keys, or network connections from memory
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For detecting process hollowing, DLL injection, or hidden processes
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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()}
- Plan Operations — Define objectives, scope, and success criteria for memory forensics operations.
- Prepare Environment — Set up tools, access, and data sources required for memory forensics.
- Execute Core Workflow — Use volatility3 to perform memory forensics operations following established procedures.
- Validate Results — Verify that results meet quality standards and objectives.
- Report Findings — Document results, observations, and recommendations.
- Follow Up — Track remediation actions and verify fixes where applicable.
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.
- 7d ago First seen · 111 lines · 53 tokens per session scan C 7bdb58e98a5a
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.
Other skills, from other repositories
performing-memory-forensics-with-volatility3
Analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity.
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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…