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 plurigrid/asi --skill analyzing-memory-forensics-with-lime-and-volatilitygit clone --depth 1 https://github.com/plurigrid/asiWrote 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/plurigrid/asi/analyzing-memory-forensics-with-lime-and-volatility)<a href="https://agentmods.dev/skills/plurigrid/asi/analyzing-memory-forensics-with-lime-and-volatility"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-memory-forensics-with-lime-and-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/plurigrid/asi/analyzing-memory-forensics-with-lime-and-volatility"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-memory-forensics-with-lime-and-volatility.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 | $0.00071 | $0.00554 |
| Opus 5 | $0.00036 | $0.00277 |
| Sonnet 5 | $0.00014 | $0.00111 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
analyzing-memory-forensics-with-lime-and-volatility scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
83% identical to analyzing-memory-forensics-with-lime-and-volatility — 32 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Analyzing Memory Forensics with LiME and Volatility
When to Use
- When investigating security incidents that require analyzing memory forensics with lime and volatility
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
Acquire Linux memory using LiME kernel module, then analyze with Volatility 3 to extract forensic artifacts from the memory image.
# LiME acquisition
insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime"
# Volatility 3 analysis
vol3 -f /evidence/memory.lime linux.pslist
vol3 -f /evidence/memory.lime linux.bash
vol3 -f /evidence/memory.lime linux.sockstat
import volatility3
from volatility3.framework import contexts, automagic
from volatility3.plugins.linux import pslist, bash, sockstat
# Programmatic Volatility 3 usage
context = contexts.Context()
automagics = automagic.available(context)
Key analysis steps:
- Acquire memory with LiME (format=lime or format=raw)
- List processes with linux.pslist, compare with linux.psscan
- Extract bash command history with linux.bash
- List network connections with linux.sockstat
- Check loaded kernel modules with linux.lsmod for rootkits
Examples
# Full forensic workflow
vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]"
vol3 -f memory.lime linux.bash
vol3 -f memory.lime linux.malfind
vol3 -f memory.lime linux.lsmod
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.
- 8d ago First seen · 74 lines · 71 tokens per session scan A 8fef57280869
analyzing-memory-forensics-with-lime-and-volatility is a skill published in the GitHub repository plurigrid/asi (62 stars, last pushed 2mo ago), licensed MIT. It adds 71 tokens to every session and 554 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to analyzing-memory-forensics-with-lime-and-volatility, differing in 32 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-memory-forensics-with-lime-and-volatility
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.
analyzing-memory-forensics-with-lime-and-volatility
A method for collecting Linux computer memory with LiME and examining it with Volatility 3, a framework for extracting evidence from memory images.
analyzing-memory-forensics-with-lime-and-volatility
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.
analyzing-memory-forensics-with-lime-and-volatility
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.
analyzing-memory-forensics-with-lime-and-volatility
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.
nsm-session-pivot
Pivot through Zeek session and protocol metadata from a packet capture (read-only) — connection listing, service filtering, and following a connection uid into dns/http/ssl logs. Use when reconstructing what sessions occurred in a capture, following a connection across protocols, or investigating retrospectively.