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Guide · Agent skill

How to use the Linux ELF Malware Analysis agent skill

Updated · written with AI from the project's own documentation and checked against our catalogue data

The analyzing-linux-elf-malware skill gives an AI agent a structured playbook for examining suspicious Linux program files (ELF binaries) that may be botnets, cryptominers, ransomware or rootkits. It is written for incident responders, malware analysts working in an isolated lab, and security students. After this guide you will know what the skill does, how to install it, how to phrase requests so the agent uses it, and what safety precautions to take before running any sample.

Linux ELF Malware Analysismukul975/Anthropic-Cybersecurity-Skills · Apache-2.0

Quick start

  1. 1Download and unzip the skill. Download the ZIP from this page and unzip it to get the skill folder containing SKILL.md, scripts/agent.py and references/api-reference.md.
  2. 2Place the folder in the skills location. Move the folder to ~/.claude/skills/ for personal use or to .claude/skills/ in a project. Alternatively, upload it under Settings → Capabilities → Skills in the Claude apps.
  3. 3Install pyelftools. Run pip install pyelftools so the bundled Python script can parse ELF files.
  4. 4Prepare an isolated analysis VM. Set up a Linux VM, the skill recommends Ubuntu 22.04, and install the tools such as strace, ltrace, GDB and Ghidra before handling samples.
  5. 5Ask a natural triage question. Describe the suspicious binary and its context in plain language, such as a file found on a compromised server.
  6. 6Review each step before tracing. Check the static results first, then let the agent move to dynamic tracing only inside the isolated VM.

What is an agent skill and what does this one do?

An agent skill is a folder containing a SKILL.md file with instructions the agent loads when a task matches its description. This skill is for analyzing Linux malware targeting servers, containers and cloud infrastructure. Its SKILL.md describes a six-step workflow: identify the ELF binary's properties, extract strings and indicators, analyze system calls and library usage, debug with GDB, reverse engineer with Ghidra, and check Linux-specific persistence mechanisms.

The package also includes a Python script, scripts/agent.py, which computes hashes, entropy and basic ELF header details, and a reference file, references/api-reference.md, covering readelf and pyelftools usage.

  • Static inspection: file type, ELF header, sections, segments, symbols, hashes and UPX packing checks
  • String and indicator hunting: URLs, IP addresses, shell commands, mining terms, SSH and credential files, persistence paths
  • Dynamic tracing in an isolated VM: strace, ltrace and GDB
  • Reverse engineering guidance for Ghidra, including checks for Mirai-like and cryptominer behavior
  • Linux persistence checks such as ld.so.preload, crontab, systemd, init scripts and authorized_keys

Install the skill from the ZIP download

Download the ZIP for this skill from this page and unzip it. To make it available across your projects, place the unzipped skill folder in ~/.claude/skills/. To make it available only inside one project, place it in that project's .claude/skills/ folder.

If you use the Claude apps instead, open Settings, go to Capabilities, and upload the skill under Skills.

  • Personal install: ~/.claude/skills/
  • Project install: .claude/skills/ inside your project folder
  • Claude apps: Settings → Capabilities → Skills, then upload

Install the tools the workflow needs

The bundled script needs Python 3 and the pyelftools library. The rest of the workflow uses separate tools. The skill lists these as prerequisites, and the optional ones are referenced in its instructions rather than required for every step.

Run the workflow on a Linux analysis VM. The skill recommends Ubuntu 22.04 with development tools installed.

  • Python 3 with pyelftools for scripts/agent.py
  • Ghidra or IDA for disassembly and decompilation
  • strace, ltrace and GDB for dynamic analysis
  • readelf, objdump and nm from GNU binutils for static inspection
  • Radare2 for quick triage, FLOSS for obfuscated strings, UPX for packing checks
  • Docker for isolated container-based execution (optional)
pip install pyelftools

How to trigger the skill with a natural request

You do not need special syntax. The skill's description says it applies when you are investigating Linux malware, triaging a suspicious ELF binary, assessing a compromised Linux server, or analyzing container-targeted malware. Describe your situation in plain language and name the file if you have it.

State the context too. Saying the binary came from a compromised server, or that you are working in a lab VM, helps the agent choose the right steps.

  • "I found an unknown binary in /tmp on a compromised Linux server. Help me triage it."
  • "Analyze this ELF file and tell me whether it looks like a cryptominer."
  • "Check this suspicious binary for persistence mechanisms like cron, systemd or ld.so.preload."
  • "Give me the hashes, architecture and entropy of this sample using the bundled script."

Safety: scripts, network and isolation

The skill runs scripts and includes dynamic analysis steps that execute the sample under strace, ltrace and GDB. Its own instructions mark tracing steps as in an isolated VM only. Do not run unknown samples on a production or personal machine. If isolation fails, the malware could cause harm.

The skill does not use the network itself, according to its listing. Some commands in the workflow, such as ss to list sockets, observe activity on the host, so keep the lab network controlled.

  • Analyze samples only in an isolated lab VM or container
  • Do not run ldd on binaries of a different architecture, as the skill's own comment warns
  • Do not use this skill for Windows PE files; use PEStudio, Ghidra or IDA for those

Example: a first triage pass

A typical first pass starts with static checks. Ask the agent to identify the file, compute hashes and search strings. Only after that should the sample be traced, and only inside the isolated VM. The agent follows the order given in SKILL.md, so you can review each result before moving on.

  • Identify properties and hashes with file, readelf and sha256sum
  • Search strings for network indicators and persistence terms
  • Trace with strace in the isolated VM and review the output file
  • Open the binary in Ghidra for deeper reverse engineering if needed
strace -f -e trace=network,process,file -o strace_output.txt ./suspect_binary

What people use it for

  • Triaging a suspicious binary found in /tmp or a container after a server compromise
  • Checking whether a sample is a cryptominer by looking for stratum or pool strings
  • Looking for persistence through ld.so.preload, crontab, systemd or authorized_keys
  • Learning a structured Linux malware workflow as a security student
  • Preparing a Ghidra analysis of a Mirai-like botnet's scanner or attack modules

Frequently asked questions

Is the Linux ELF malware analysis skill free?

The skill's listing is licensed under Apache-2.0. The tools it references, such as Ghidra, Radare2, GDB and strace, are separate software with their own licences, so check each one before you install it.

What do I need to install before using this skill?

You need Python 3 with pyelftools for the bundled script. The full workflow also references a Linux analysis VM, Ghidra or IDA, strace, ltrace, GDB, binutils, Radare2, FLOSS and UPX. Docker is optional for container-based execution.

Where do I put the skill files?

Place the unzipped folder in ~/.claude/skills/ for personal use or in a project's .claude/skills/ folder. In the Claude apps, you can instead upload it under Settings → Capabilities → Skills.

Does the skill run code on my machine?

Yes. The skill runs scripts, and its dynamic analysis steps execute the sample under strace, ltrace and GDB. Its listing says it does not use the network, but the sample should only ever be run in an isolated VM.

Can this skill analyze Windows malware?

No. The skill says not to use it for Windows PE binary analysis and recommends PEStudio, Ghidra or IDA for Windows malware.

How is this different from just running strings and readelf myself?

The skill gives the agent a fixed order of checks, from static inspection to tracing to persistence, plus a script that computes hashes, entropy and ELF header details. It does not replace your judgement, and you still need to review each result.

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Our verdict

Use this skill if you are an incident responder, malware analyst or student who works on suspicious Linux ELF files in an isolated lab VM and wants a structured workflow. Pick something else if you need to analyze Windows PE malware, or if you cannot isolate samples from production or personal machines.

Download the skill