SKILLS/analyzing-memory-forensics-with-lime-and-volatility/SKILL.md
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.
npx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-memory-forensics-with-lime-and-volatilityInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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:
# 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
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.