Awesome-omni-skills memory-forensics
Memory Forensics workflow skill. Use this skill when the user needs Comprehensive techniques for acquiring, analyzing, and extracting artifacts from memory dumps for incident response and malware analysis and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
git clone https://github.com/diegosouzapw/awesome-omni-skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/memory-forensics" ~/.claude/skills/diegosouzapw-awesome-omni-skills-memory-forensics && rm -rf "$T"
skills/memory-forensics/SKILL.mdMemory Forensics
Overview
This public intake copy packages
plugins/antigravity-awesome-skills-claude/skills/memory-forensics from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses
metadata.json plus ORIGIN.md as the provenance anchor for review.
Memory Forensics Comprehensive techniques for acquiring, analyzing, and extracting artifacts from memory dumps for incident response and malware analysis.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Memory Acquisition, Volatility 3 Framework, Data Structures, Detection Patterns, YARA Integration, String Analysis.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
- Working on memory forensics tasks or workflows
- Needing guidance, best practices, or checklists for memory forensics
- The task is unrelated to memory forensics
- You need a different domain or tool outside this scope
- Use when the request clearly matches the imported source intent: Comprehensive techniques for acquiring, analyzing, and extracting artifacts from memory dumps for incident response and malware analysis.
- Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Operating Table
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | | Confirms repository, branch, commit, and imported path before touching the copied workflow |
| Provenance review | | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | | Starts with the smallest copied file that materially changes execution |
| Supporting context | | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | | Helps the operator switch to a stronger native skill when the task drifts |
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open resources/implementation-playbook.md.
- Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
- Read the overview and provenance files before loading any copied upstream support files.
- Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Imported Workflow Notes
Imported: Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
.resources/implementation-playbook.md
Imported: Analysis Workflows
Malware Analysis Workflow
# 1. Initial process survey vol -f memory.raw windows.pstree > processes.txt vol -f memory.raw windows.pslist > pslist.txt # 2. Network connections vol -f memory.raw windows.netscan > network.txt # 3. Detect injection vol -f memory.raw windows.malfind > malfind.txt # 4. Analyze suspicious processes vol -f memory.raw windows.dlllist --pid <PID> vol -f memory.raw windows.handles --pid <PID> # 5. Dump suspicious executables vol -f memory.raw windows.pslist --pid <PID> --dump # 6. Extract strings from dumps strings -a pid.<PID>.exe > strings.txt # 7. YARA scanning vol -f memory.raw windows.yarascan --yara-rules malware.yar
Incident Response Workflow
# 1. Timeline of events vol -f memory.raw windows.timeliner > timeline.csv # 2. User activity vol -f memory.raw windows.cmdline vol -f memory.raw windows.consoles # 3. Persistence mechanisms vol -f memory.raw windows.registry.printkey \ --key "Software\Microsoft\Windows\CurrentVersion\Run" # 4. Services vol -f memory.raw windows.svcscan # 5. Scheduled tasks vol -f memory.raw windows.scheduled_tasks # 6. Recent files vol -f memory.raw windows.filescan | grep -i "recent"
Imported: Memory Acquisition
Live Acquisition Tools
Windows
# WinPmem (Recommended) winpmem_mini_x64.exe memory.raw # DumpIt DumpIt.exe # Belkasoft RAM Capturer # GUI-based, outputs raw format # Magnet RAM Capture # GUI-based, outputs raw format
Linux
# LiME (Linux Memory Extractor) sudo insmod lime.ko "path=/tmp/memory.lime format=lime" # /dev/mem (limited, requires permissions) sudo dd if=/dev/mem of=memory.raw bs=1M # /proc/kcore (ELF format) sudo cp /proc/kcore memory.elf
macOS
# osxpmem sudo ./osxpmem -o memory.raw # MacQuisition (commercial)
Virtual Machine Memory
# VMware: .vmem file is raw memory cp vm.vmem memory.raw # VirtualBox: Use debug console vboxmanage debugvm "VMName" dumpvmcore --filename memory.elf # QEMU virsh dump <domain> memory.raw --memory-only # Hyper-V # Checkpoint contains memory state
Examples
Example 1: Ask for the upstream workflow directly
Use @memory-forensics to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @memory-forensics against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @memory-forensics for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @memory-forensics using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
- Minimize footprint: Use lightweight acquisition tools
- Document everything: Record time, tool, and hash of capture
- Verify integrity: Hash memory dump immediately after capture
- Chain of custody: Maintain proper forensic handling
- Start broad: Get overview before deep diving
- Cross-reference: Use multiple plugins for same data
- Timeline correlation: Correlate memory findings with disk/network
Imported Operating Notes
Imported: Best Practices
Acquisition Best Practices
- Minimize footprint: Use lightweight acquisition tools
- Document everything: Record time, tool, and hash of capture
- Verify integrity: Hash memory dump immediately after capture
- Chain of custody: Maintain proper forensic handling
Analysis Best Practices
- Start broad: Get overview before deep diving
- Cross-reference: Use multiple plugins for same data
- Timeline correlation: Correlate memory findings with disk/network
- Document findings: Keep detailed notes and screenshots
- Validate results: Verify findings through multiple methods
Common Pitfalls
- Stale data: Memory is volatile, analyze promptly
- Incomplete dumps: Verify dump size matches expected RAM
- Symbol issues: Ensure correct symbol files for OS version
- Smear: Memory may change during acquisition
- Encryption: Some data may be encrypted in memory
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in
plugins/antigravity-awesome-skills-claude/skills/memory-forensics, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated
SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
- Use when the work is better handled by that native specialization after this imported skill establishes context.@linear-claude-skill
- Use when the work is better handled by that native specialization after this imported skill establishes context.@linkedin-automation
- Use when the work is better handled by that native specialization after this imported skill establishes context.@linkedin-cli
- Use when the work is better handled by that native specialization after this imported skill establishes context.@linkedin-profile-optimizer
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
|---|---|---|
| copied reference notes, guides, or background material from upstream | |
| worked examples or reusable prompts copied from upstream | |
| upstream helper scripts that change execution or validation | |
| routing or delegation notes that are genuinely part of the imported package | |
| supporting assets or schemas copied from the source package | |
Imported Reference Notes
Imported: Volatility 3 Framework
Installation and Setup
# Install Volatility 3 pip install volatility3 # Install symbol tables (Windows) # Download from https://downloads.volatilityfoundation.org/volatility3/symbols/ # Basic usage vol -f memory.raw <plugin> # With symbol path vol -f memory.raw -s /path/to/symbols windows.pslist
Essential Plugins
Process Analysis
# List processes vol -f memory.raw windows.pslist # Process tree (parent-child relationships) vol -f memory.raw windows.pstree # Hidden process detection vol -f memory.raw windows.psscan # Process memory dumps vol -f memory.raw windows.memmap --pid <PID> --dump # Process environment variables vol -f memory.raw windows.envars --pid <PID> # Command line arguments vol -f memory.raw windows.cmdline
Network Analysis
# Network connections vol -f memory.raw windows.netscan # Network connection state vol -f memory.raw windows.netstat
DLL and Module Analysis
# Loaded DLLs per process vol -f memory.raw windows.dlllist --pid <PID> # Find hidden/injected DLLs vol -f memory.raw windows.ldrmodules # Kernel modules vol -f memory.raw windows.modules # Module dumps vol -f memory.raw windows.moddump --pid <PID>
Memory Injection Detection
# Detect code injection vol -f memory.raw windows.malfind # VAD (Virtual Address Descriptor) analysis vol -f memory.raw windows.vadinfo --pid <PID> # Dump suspicious memory regions vol -f memory.raw windows.vadyarascan --yara-rules rules.yar
Registry Analysis
# List registry hives vol -f memory.raw windows.registry.hivelist # Print registry key vol -f memory.raw windows.registry.printkey --key "Software\Microsoft\Windows\CurrentVersion\Run" # Dump registry hive vol -f memory.raw windows.registry.hivescan --dump
File System Artifacts
# Scan for file objects vol -f memory.raw windows.filescan # Dump files from memory vol -f memory.raw windows.dumpfiles --pid <PID> # MFT analysis vol -f memory.raw windows.mftscan
Linux Analysis
# Process listing vol -f memory.raw linux.pslist # Process tree vol -f memory.raw linux.pstree # Bash history vol -f memory.raw linux.bash # Network connections vol -f memory.raw linux.sockstat # Loaded kernel modules vol -f memory.raw linux.lsmod # Mount points vol -f memory.raw linux.mount # Environment variables vol -f memory.raw linux.envars
macOS Analysis
# Process listing vol -f memory.raw mac.pslist # Process tree vol -f memory.raw mac.pstree # Network connections vol -f memory.raw mac.netstat # Kernel extensions vol -f memory.raw mac.lsmod
Imported: Data Structures
Windows Process Structures
// EPROCESS (Executive Process) typedef struct _EPROCESS { KPROCESS Pcb; // Kernel process block EX_PUSH_LOCK ProcessLock; LARGE_INTEGER CreateTime; LARGE_INTEGER ExitTime; // ... LIST_ENTRY ActiveProcessLinks; // Doubly-linked list ULONG_PTR UniqueProcessId; // PID // ... PEB* Peb; // Process Environment Block // ... } EPROCESS; // PEB (Process Environment Block) typedef struct _PEB { BOOLEAN InheritedAddressSpace; BOOLEAN ReadImageFileExecOptions; BOOLEAN BeingDebugged; // Anti-debug check // ... PVOID ImageBaseAddress; // Base address of executable PPEB_LDR_DATA Ldr; // Loader data (DLL list) PRTL_USER_PROCESS_PARAMETERS ProcessParameters; // ... } PEB;
VAD (Virtual Address Descriptor)
typedef struct _MMVAD { MMVAD_SHORT Core; union { ULONG LongFlags; MMVAD_FLAGS VadFlags; } u; // ... PVOID FirstPrototypePte; PVOID LastContiguousPte; // ... PFILE_OBJECT FileObject; } MMVAD; // Memory protection flags #define PAGE_EXECUTE 0x10 #define PAGE_EXECUTE_READ 0x20 #define PAGE_EXECUTE_READWRITE 0x40 #define PAGE_EXECUTE_WRITECOPY 0x80
Imported: Detection Patterns
Process Injection Indicators
# Malfind indicators # - PAGE_EXECUTE_READWRITE protection (suspicious) # - MZ header in non-image VAD region # - Shellcode patterns at allocation start # Common injection techniques # 1. Classic DLL Injection # - VirtualAllocEx + WriteProcessMemory + CreateRemoteThread # 2. Process Hollowing # - CreateProcess (SUSPENDED) + NtUnmapViewOfSection + WriteProcessMemory # 3. APC Injection # - QueueUserAPC targeting alertable threads # 4. Thread Execution Hijacking # - SuspendThread + SetThreadContext + ResumeThread
Rootkit Detection
# Compare process lists vol -f memory.raw windows.pslist > pslist.txt vol -f memory.raw windows.psscan > psscan.txt diff pslist.txt psscan.txt # Hidden processes # Check for DKOM (Direct Kernel Object Manipulation) vol -f memory.raw windows.callbacks # Detect hooked functions vol -f memory.raw windows.ssdt # System Service Descriptor Table # Driver analysis vol -f memory.raw windows.driverscan vol -f memory.raw windows.driverirp
Credential Extraction
# Dump hashes (requires hivelist first) vol -f memory.raw windows.hashdump # LSA secrets vol -f memory.raw windows.lsadump # Cached domain credentials vol -f memory.raw windows.cachedump # Mimikatz-style extraction # Requires specific plugins/tools
Imported: YARA Integration
Writing Memory YARA Rules
rule Suspicious_Injection { meta: description = "Detects common injection shellcode" strings: // Common shellcode patterns $mz = { 4D 5A } $shellcode1 = { 55 8B EC 83 EC } // Function prologue $api_hash = { 68 ?? ?? ?? ?? 68 ?? ?? ?? ?? E8 } // Push hash, call condition: $mz at 0 or any of ($shellcode*) } rule Cobalt_Strike_Beacon { meta: description = "Detects Cobalt Strike beacon in memory" strings: $config = { 00 01 00 01 00 02 } $sleep = "sleeptime" $beacon = "%s (admin)" wide condition: 2 of them }
Scanning Memory
# Scan all process memory vol -f memory.raw windows.yarascan --yara-rules rules.yar # Scan specific process vol -f memory.raw windows.yarascan --yara-rules rules.yar --pid 1234 # Scan kernel memory vol -f memory.raw windows.yarascan --yara-rules rules.yar --kernel
Imported: String Analysis
Extracting Strings
# Basic string extraction strings -a memory.raw > all_strings.txt # Unicode strings strings -el memory.raw >> all_strings.txt # Targeted extraction from process dump vol -f memory.raw windows.memmap --pid 1234 --dump strings -a pid.1234.dmp > process_strings.txt # Pattern matching grep -E "(https?://|[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})" all_strings.txt
FLOSS for Obfuscated Strings
# FLOSS extracts obfuscated strings floss malware.exe > floss_output.txt # From memory dump floss pid.1234.dmp
Imported: Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.