Memory Forensics Triage logo

Memory Forensics Triage

Organization
zero-lab-ai
memory-forensics-triage

Triages supplied Windows, Linux, or other volatile-memory images while preserving acquisition provenance, image integrity, process and region evidence, network and object correlations, extracted-artifact custody, timeline limits, and claim confidence. Use for offline memory-forensics review and handoff, not live rootkit trust assessment or full malware reverse engineering.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namememory-forensics-triage
Stars
232
Forks
14
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Memory Forensics Triage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zero-lab-ai/skills/tree/main/plugins/binary-forensics/skills/memory-forensics-triage
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/binary-forensics/skills/memory-forensics-triage .claude/skills/memory-forensics-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Forensics Triage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Memory Forensics Triage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Memory Forensics Triage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Memory forensics triage

Memory is a captured vantage, not an infallible narrator. Acquisition changes the system; the acquisition path can be subverted; symbols can be wrong; one image can produce many correlated outputs without becoming multiple independent sources.

This skill analyzes supplied image facts and normalized outputs. It does not acquire memory, provide live collection commands, recover credential material, execute extracted code, query reputation services, or authorize containment.

When to Use

  • Triage memory forensics for authorized security work.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Record written authorization, case and asset identity, platform, image name, size, format, acquisition method and window, tool identity, hash, transfer, custodian, storage, access-log state, acquisition impact, and clock basis.
  2. Record the analysis engine, version, reported platform or build, selected symbols or profile, match state, validation evidence, and limitations. A successful plugin run does not prove the profile is correct.
  3. Inventory each supplied observation with its image, source group, artifact class, locator, role, honest time precision, and limitations. Different plugins over one image share one source group.
  4. Correlate process inventory and lineage, executable regions, modules or mappings, network objects, handles, registry objects, files, credentials, kernel or driver structures, and supplied baselines. Preserve benign alternatives and missing cross-vantage evidence.
  5. Keep claim types narrow. A connection is a network object, not C2 attribution. An LSASS handle is credential access capability, not credential extraction. A Run value is a persistence mechanism, not proof it executed.
  6. Use suspicious for mutually reinforcing indicators within one source group. Use corroborated only with at least two independent source groups. Use confirmed only when the claim-specific proof requirements in
  7. Track every candidate extraction. extracted needs a preserved digest and custody. Malware classification needs analysis of the extracted artifact; bytes that were never recovered cannot be classified.
  8. Build timeline events only from cited times. Keep host-clock time separate from trusted time; unknown skew remains a limitation. Do not assign an exact timestamp to an object merely because the image has an acquisition window.
  9. Cover all required artifact and case categories. Mark missing work blocked with a blocker rather than calling the surface clean.
  10. Record owned, authorization-aware next steps and handoffs. Contact with a live host or external service needs explicit authority beyond offline review.

Boundary with adjacent skills

  • Use rootkit-triage when live host views may be lying and the task is to compare kernel, boot, firmware, memory, disk, and external vantages.
  • Use protocol-traffic-analysis for packet-capture interpretation.
  • Use credential-exposure-audit for credential-material inventory and handling.
  • Hand extracted binaries to a malware or binary-analysis workflow; this skill preserves extraction provenance and the questions to answer.
  • Use detection-engineering to turn stable observations into detector logic.
  • Use findings-report for final stakeholder finding prose.

Rationalizations to reject

  • "Several plugins agree, so the finding is independently corroborated." Plugins reading one image share acquisition and parsing dependencies.
  • "RWX plus an MZ header proves malware." Legitimate software and injected security components can create similar regions; preserve and analyze the bytes.
  • "Port 443 from a suspicious process is C2." The memory object proves a connection, not destination ownership, protocol content, or intent.
  • "A handle to LSASS proves credentials were stolen." Access capability is not proof that credential material was read or used.
  • "A Run value proves persistence succeeded." It proves a mechanism was represented in memory; execution and durable disk state need more evidence.
  • "The image hash makes acquisition trustworthy." It protects the captured file after hashing; it does not validate the acquisition binary or compromised source path.
  • "Exact plugin timestamps are trusted UTC." They remain host-clock values until clock basis and skew are established.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Memory Forensics Triage AI skill do?

Triages supplied Windows, Linux, or other volatile-memory images while preserving acquisition provenance, image integrity, process and region evidence, network and object correlations, extracted-artifact custody, timeline limits, and claim confidence. Use for offline memory-forensics review and handoff, not live rootkit trust assessment or full malware reverse engineering.

Why use Memory Forensics Triage on TypingMind?

Because you install it once and use it with any model. Memory Forensics Triage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Memory Forensics Triage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/binary-forensics/skills/memory-forensics-triage. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Memory Forensics Triage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Memory Forensics Triage?

As many as you like. As long as a model supports skills, you can use Memory Forensics Triage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Memory Forensics Triage AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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