Agent skill

Investigating Ransomware Attack Artifacts

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Forensically preserve memory and disk, collect ransom notes and encrypted file samples, and identify the ransomware variant using tools such as ID Ransomware, Volatility, and Chainsaw/Hayabusa to…

Apache-2.0Auto-check: notesSecurity

Install Investigating Ransomware Attack Artifacts

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill investigating-ransomware-attack-artifacts -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills investigating-ransomware-attack-artifacts --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/investigating-ransomware-attack-artifacts .claude/skills/investigating-ransomware-attack-artifacts && 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
investigating-ransomware-attack-artifacts
GitHub stars
34k
Token cost
~4.1k tokens
SKILL.md length
497 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Forensically preserve memory and disk, collect ransom notes and encrypted file samples, and identify the ransomware variant using tools such as ID Ransomware, Volatility, and Chainsaw/Hayabusa to…

  • Works in 5 steps: Preserve Evidence and Identify the… → Determine the Attack Timeline → Trace Initial Access and Lateral Movement → …
  • Security work in your project
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3; reaches nomoreransom.org

What it does

Investigating Ransomware Attack Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Forensically preserve memory and disk, collect ransom notes and encrypted file samples, and identify the ransomware variant using tools such as ID Ransomware, Volatility, and Chainsaw/Hayabusa to determine the initial access vector and recovery options. Use immediately after discovering ransomware encryption, when scoping the incident forensically, or when documenting evidence for law enforcement and insurance claims.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security. 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

  • Security work in your project

Example prompts

  • “/investigating-ransomware-attack-artifacts”

Requirements

  • Python 3

Workflow steps

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

  1. Preserve Evidence and Identify the Ransomware Variant
  2. Determine the Attack Timeline
  3. Trace Initial Access and Lateral Movement
  4. Assess Encryption Scope and Recovery Options
  5. Document Findings and Generate Report

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:

    • nomoreransom.org

    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

Investigating Ransomware Attack Artifacts loads about 4.1k tokens when it runs, and up to ~4.6k if it reads all its reference files. Until then it costs about 116 tokens; SKILL.md has 497 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~116
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.6k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:83
    # Linux: sudo insmod lime.ko "path=/evidence/memory.lime format=lime"

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). 497 words, ~4,051 tokens.

Download SKILL.mdSave it as .claude/skills/investigating-ransomware-attack-artifacts/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
investigating-ransomware-attack-artifacts
description
Forensically preserve memory and disk, collect ransom notes and encrypted file samples, and identify the ransomware variant using tools such as ID Ransomware, Volatility, and Chainsaw/Hayabusa to determine the initial access vector and recovery options. Use immediately after discovering ransomware encryption, when scoping the incident forensically, or when documenting evidence for law enforcement and insurance claims.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, ransomware, malware-analysis, incident-response, encryption-recovery, evidence-collection
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1005, T1074, T1119, T1070, T1486
mitre_f3.version
1.1
mitre_f3.tactics
initial-access, stealth, monetization

Investigating Ransomware Attack Artifacts

When to Use

  • Immediately after discovering ransomware encryption on systems
  • When performing forensic analysis to understand the full scope of a ransomware incident
  • For identifying the ransomware variant and determining if decryption is possible
  • When tracing the attack chain from initial access to encryption
  • For documenting evidence to support law enforcement and insurance claims

Prerequisites

  • Forensic images of affected systems (preserve before remediation)
  • Memory dumps captured before system shutdown (if available)
  • Ransom notes and encrypted file samples
  • Network traffic captures from the attack period
  • Windows Event Logs, Prefetch files, and registry hives
  • Access to ransomware identification tools (ID Ransomware, No More Ransom)
  • Isolated sandbox environment for malware analysis

Workflow

Step 1: Preserve Evidence and Identify the Ransomware Variant
bash
# CRITICAL: Do NOT restart systems. Preserve memory first if possible.
# Encryption keys may still be in memory.

# Capture memory from running systems
# Windows: DumpIt.exe (generates memory.raw)
# Linux: sudo insmod lime.ko "path=/evidence/memory.lime format=lime"

# Collect ransom note
cp /mnt/evidence/Users/*/Desktop/README*.txt /cases/case-2024-001/ransomware/ransom_notes/
cp /mnt/evidence/Users/*/Desktop/DECRYPT*.txt /cases/case-2024-001/ransomware/ransom_notes/
cp /mnt/evidence/Users/*/Desktop/HOW_TO*.txt /cases/case-2024-001/ransomware/ransom_notes/
find /mnt/evidence/ -name "*.hta" -o -name "*DECRYPT*" -o -name "*RANSOM*" -o -name "*README*" \
   2>/dev/null | head -20 > /cases/case-2024-001/ransomware/note_locations.txt

# Collect sample encrypted files (for identification)
find /mnt/evidence/Users/ -name "*.encrypted" -o -name "*.locked" -o -name "*.crypted" \
   -o -name "*.crypt" -o -name "*.enc" | head -10 > /cases/case-2024-001/ransomware/encrypted_samples.txt

# Copy sample encrypted files
mkdir -p /cases/case-2024-001/ransomware/samples/
head -5 /cases/case-2024-001/ransomware/encrypted_samples.txt | while read f; do
    cp "$f" /cases/case-2024-001/ransomware/samples/
done

# Identify ransomware variant using file extension and ransom note
python3 << 'PYEOF'
import os, hashlib, json

ransomware_indicators = {
    '.lockbit': 'LockBit',
    '.blackcat': 'BlackCat/ALPHV',
    '.royal': 'Royal',
    '.akira': 'Akira',
    '.clop': 'Cl0p',
    '.conti': 'Conti',
    '.ryuk': 'Ryuk',
    '.revil': 'REvil/Sodinokibi',
    '.maze': 'Maze',
    '.phobos': 'Phobos',
    '.dharma': 'Dharma/CrySIS',
    '.stop': 'STOP/Djvu',
    '.hive': 'Hive',
    '.blackbasta': 'Black Basta',
    '.play': 'Play',
}

# Check encrypted file extensions
samples_dir = '/cases/case-2024-001/ransomware/samples/'
for f in os.listdir(samples_dir):
    ext = os.path.splitext(f)[1].lower()
    variant = ransomware_indicators.get(ext, 'Unknown')
    sha256 = hashlib.sha256(open(os.path.join(samples_dir, f), 'rb').read()).hexdigest()
    print(f"File: {f}")
    print(f"  Extension: {ext}")
    print(f"  Suspected Variant: {variant}")
    print(f"  SHA-256: {sha256}")
    print()

# Parse ransom note for IoCs
note_dir = '/cases/case-2024-001/ransomware/ransom_notes/'
for note in os.listdir(note_dir):
    with open(os.path.join(note_dir, note), 'r', errors='ignore') as f:
        content = f.read()
        print(f"\n=== Ransom Note: {note} ===")
        # Extract bitcoin addresses
        import re
        btc = re.findall(r'[13][a-km-zA-HJ-NP-Z1-9]{25,34}|bc1[a-zA-HJ-NP-Z0-9]{25,39}', content)
        tor = re.findall(r'[a-z2-7]{56}\.onion', content)
        emails = re.findall(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', content)

        if btc: print(f"  Bitcoin addresses: {btc}")
        if tor: print(f"  Tor addresses: {tor}")
        if emails: print(f"  Contact emails: {emails}")
PYEOF
Step 2: Determine the Attack Timeline
bash
# Find the earliest encrypted file (encryption start time)
find /mnt/evidence/ -name "*.encrypted" -printf '%T+ %p\n' 2>/dev/null | sort | head -5 \
   > /cases/case-2024-001/ransomware/encryption_start.txt

# Find the latest encrypted file (encryption end time)
find /mnt/evidence/ -name "*.encrypted" -printf '%T+ %p\n' 2>/dev/null | sort -r | head -5 \
   > /cases/case-2024-001/ransomware/encryption_end.txt

# Analyze Prefetch for ransomware executable
ls /mnt/evidence/Windows/Prefetch/ | grep -iE "(encrypt|ransom|lock|crypt)" \
   > /cases/case-2024-001/ransomware/prefetch_hits.txt

# Check Windows Event Logs for key events
python3 << 'PYEOF'
import json
from evtx import PyEvtxParser

# Security log - authentication and access events
parser = PyEvtxParser("/cases/case-2024-001/evtx/Security.evtx")

attack_events = []
for record in parser.records_json():
    data = json.loads(record['data'])
    event_id = str(data['Event']['System']['EventID'])
    timestamp = data['Event']['System']['TimeCreated']['#attributes']['SystemTime']

    # Key events for ransomware investigation
    if event_id in ('4624', '4625', '4648', '4672', '4697', '4698', '4688', '1102'):
        event_data = data['Event'].get('EventData', {})
        attack_events.append({
            'time': timestamp,
            'event_id': event_id,
            'data': json.dumps(event_data, default=str)[:200]
        })

# Sort and display timeline
attack_events.sort(key=lambda x: x['time'])
print("=== RANSOMWARE ATTACK TIMELINE ===\n")
for event in attack_events[-50:]:
    print(f"  [{event['time']}] EventID {event['event_id']}: {event['data'][:150]}")
PYEOF

# Check for Volume Shadow Copy deletion (common ransomware behavior)
# Look for vssadmin.exe or wmic shadowcopy in event logs and Prefetch
grep -l "vssadmin" /cases/case-2024-001/evtx/*.evtx 2>/dev/null
ls /mnt/evidence/Windows/Prefetch/ | grep -i "vssadmin\|wmic\|bcdedit\|wbadmin"
Step 3: Trace Initial Access and Lateral Movement
bash
# Check for common ransomware initial access vectors

# RDP brute force
python3 << 'PYEOF'
import json
from evtx import PyEvtxParser
from collections import defaultdict

parser = PyEvtxParser("/cases/case-2024-001/evtx/Security.evtx")

failed_rdp = defaultdict(int)
successful_rdp = []

for record in parser.records_json():
    data = json.loads(record['data'])
    event_id = str(data['Event']['System']['EventID'])
    event_data = data['Event'].get('EventData', {})
    timestamp = data['Event']['System']['TimeCreated']['#attributes']['SystemTime']

    if event_id == '4625':  # Failed logon
        logon_type = str(event_data.get('LogonType', ''))
        if logon_type == '10':  # RDP
            source_ip = event_data.get('IpAddress', 'Unknown')
            failed_rdp[source_ip] += 1

    if event_id == '4624':  # Successful logon
        logon_type = str(event_data.get('LogonType', ''))
        if logon_type in ('10', '3'):  # RDP or Network
            source_ip = event_data.get('IpAddress', 'Unknown')
            username = event_data.get('TargetUserName', 'Unknown')
            successful_rdp.append({'time': timestamp, 'user': username, 'ip': source_ip, 'type': logon_type})

print("=== FAILED RDP ATTEMPTS ===")
for ip, count in sorted(failed_rdp.items(), key=lambda x: x[1], reverse=True)[:10]:
    print(f"  {ip}: {count} failed attempts")

print(f"\n=== SUCCESSFUL NETWORK/RDP LOGONS ===")
for logon in successful_rdp[-20:]:
    type_name = 'RDP' if logon['type'] == '10' else 'Network'
    print(f"  [{logon['time']}] {logon['user']} from {logon['ip']} ({type_name})")
PYEOF

# Check for phishing-related artifacts
# Browser downloads, email attachments, Office macros
find /mnt/evidence/Users/*/Downloads/ -name "*.exe" -o -name "*.dll" -o -name "*.js" \
   -o -name "*.vbs" -o -name "*.hta" -o -name "*.ps1" 2>/dev/null \
   > /cases/case-2024-001/ransomware/suspicious_downloads.txt

# Check PowerShell execution
ls /mnt/evidence/Windows/Prefetch/ | grep -i powershell
Step 4: Assess Encryption Scope and Recovery Options
bash
# Count encrypted files by directory
find /mnt/evidence/ -name "*.encrypted" 2>/dev/null | \
   awk -F/ '{OFS="/"; NF--; print}' | sort | uniq -c | sort -rn | head -20 \
   > /cases/case-2024-001/ransomware/encryption_scope.txt

# Check if Volume Shadow Copies survived
vssadmin list shadows 2>/dev/null > /cases/case-2024-001/ransomware/vss_status.txt

# Check for backup integrity
find /mnt/evidence/ -name "*.bak" -o -name "*.backup" 2>/dev/null | head -20

# Check No More Ransom project for available decryptors
# https://www.nomoreransom.org/en/decryption-tools.html
echo "Check https://www.nomoreransom.org/ for decryption tools" \
   > /cases/case-2024-001/ransomware/decryption_options.txt

# Attempt to recover encryption keys from memory dump
if [ -f /cases/case-2024-001/memory/memory.raw ]; then
    # Search for AES key schedules in memory
    vol -f /cases/case-2024-001/memory/memory.raw yarascan \
       --yara-rules 'rule AES_Key { strings: $aes = { 63 7C 77 7B F2 6B 6F C5 30 01 67 2B FE D7 AB 76 } condition: $aes }' \
       > /cases/case-2024-001/ransomware/aes_key_search.txt

    # Search for RSA key material
    vol -f /cases/case-2024-001/memory/memory.raw yarascan \
       --yara-rules 'rule RSA_Key { strings: $rsa = "RSA PRIVATE KEY" condition: $rsa }' \
       > /cases/case-2024-001/ransomware/rsa_key_search.txt
fi
Step 5: Document Findings and Generate Report
bash
# Generate comprehensive ransomware investigation report
cat << 'REPORT' > /cases/case-2024-001/ransomware/investigation_report.txt
RANSOMWARE INCIDENT INVESTIGATION REPORT
==========================================
Case Number: 2024-001
Date: $(date -u)
Analyst: [Examiner Name]

1. INCIDENT OVERVIEW
   - Ransomware Variant: [Identified variant]
   - First Encryption: [Timestamp from earliest encrypted file]
   - Last Encryption: [Timestamp from latest encrypted file]
   - Systems Affected: [Count]
   - Data Encrypted: [Volume estimate]

2. INITIAL ACCESS VECTOR
   - Method: [RDP brute force / Phishing / Exploit / etc.]
   - Entry Point: [System and IP]
   - Timestamp: [First unauthorized access]
   - Credentials Used: [Account names]

3. ATTACK CHAIN
   a. Initial Access: [Details]
   b. Execution: [Ransomware binary details]
   c. Persistence: [Services, scheduled tasks]
   d. Privilege Escalation: [Method used]
   e. Lateral Movement: [Systems accessed, methods]
   f. Collection/Staging: [Data staging before encryption]
   g. Impact: [Encryption execution]

4. INDICATORS OF COMPROMISE
   - Ransomware Binary SHA-256: [Hash]
   - C2 Servers: [IPs/Domains]
   - Bitcoin Wallet: [Address]
   - Tor Site: [.onion address]
   - Attacker IPs: [Source IPs]

5. RECOVERY ASSESSMENT
   - Decryptor Available: [Yes/No]
   - Shadow Copies: [Survived/Deleted]
   - Backups: [Status and integrity]
   - Memory Key Recovery: [Attempted/Results]

6. RECOMMENDATIONS
   - [Remediation steps]
   - [Prevention measures]
   - [Monitoring improvements]
REPORT

Key Concepts

ConceptDescription
Ransomware variant identificationDetermining the specific ransomware family from extensions, notes, and behavior
Double extortionAttack combining encryption with data theft and threatened public release
Volume Shadow CopiesWindows backup mechanism often deleted by ransomware to prevent recovery
Encryption scopeAssessment of which files, directories, and systems were encrypted
Dwell timePeriod between initial access and ransomware deployment (often days to weeks)
Ransom note IoCsBitcoin addresses, Tor sites, and email addresses in ransom demands
Key recoveryAttempting to extract encryption keys from memory before shutdown
No More RansomLaw enforcement initiative providing free decryption tools for some variants

Tools & Systems

ToolPurpose
ID RansomwareOnline service identifying ransomware variant from samples
No More RansomFree decryption tools from law enforcement partnerships
VolatilityMemory forensics for encryption key and malware artifact recovery
Chainsaw/HayabusaWindows Event Log analysis for attack timeline reconstruction
PECmdPrefetch analysis confirming ransomware executable execution
YARAPattern matching for ransomware variant identification
Any.Run/Joe SandboxOnline malware sandboxes for ransomware behavior analysis
CapaMandiant tool identifying malware capabilities from static analysis
Show full SKILL.md (169 more words)Show less

Common Scenarios

Scenario 1: LockBit Attack via RDP Trace initial access through RDP brute force in event logs, identify attacker IP and compromised account, follow lateral movement through network logons, find LockBit deployment via PsExec or GPO, document encryption timeline from file timestamps, check for data exfiltration before encryption.

Scenario 2: Phishing-Initiated Ransomware Trace phishing email through browser history and email artifacts, identify malicious attachment execution in Prefetch, follow Cobalt Strike beacon communication in network logs, trace privilege escalation and domain compromise, document ransomware deployment across the network.

Scenario 3: Supply Chain Ransomware Attack Identify the compromised software update mechanism, trace the malicious update distribution in application logs, analyze the ransomware payload delivered via the trusted channel, assess which systems received the update, determine if the vendor was notified.

Scenario 4: Recovery from Partial Encryption Determine which systems and files were encrypted before containment, check for surviving volume shadow copies, verify backup integrity and restoration capability, attempt memory-based key recovery, contact law enforcement for potential decryptor availability.

Output Format

Ransomware Investigation Summary:
  Variant: LockBit 3.0
  First Seen: 2024-01-18 02:00:00 UTC
  Encryption Duration: 4 hours 23 minutes
  Systems Encrypted: 45 out of 200 (containment stopped spread)

  Attack Timeline:
    2024-01-10 14:32 - RDP brute force from 203.0.113.45 (1,234 attempts)
    2024-01-10 15:00 - Successful RDP login as admin_backup
    2024-01-12 02:00 - Mimikatz executed (credential dump)
    2024-01-12 02:30 - Domain Admin credentials obtained
    2024-01-15 03:00 - Data exfiltration (45 GB to 185.x.x.x)
    2024-01-18 02:00 - LockBit deployed via PsExec to 45 systems
    2024-01-18 06:23 - Encryption completed on affected systems

  Recovery Options:
    Decryptor: Not available (LockBit 3.0)
    Shadow Copies: Deleted on all systems
    Backups: Last clean backup 2024-01-09 (9 days of data loss)
    Memory Keys: Not recovered (systems rebooted)

  IOCs:
    Ransomware Hash: a1b2c3d4e5f6...
    C2 IP: 185.x.x.x
    Bitcoin: bc1q...
    Tor: http://lockbit...onion

© 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 3 other files (scripts, references) in skills/investigating-ransomware-attack-artifacts of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit4811 repos~3.3kAutomated safety check: PassNone
Security Alert Triageelastic/agent-skills5921 repos~3.5kAutomated safety check: NotesApache-2.0
Shiro Attack CLISummerSec/ShiroAttack22.6k—~945Automated safety check: PassMIT

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    mukul975/Anthropic-Cybersecurity-Skills

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Questions about Investigating Ransomware Attack Artifacts

What does Investigating Ransomware Attack Artifacts do?

Forensically preserve memory and disk, collect ransom notes and encrypted file samples, and identify the ransomware variant using tools such as ID Ransomware, Volatility, and Chainsaw/Hayabusa to…. Investigating Ransomware Attack Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Forensically preserve memory and disk, collect ransom notes and encrypted file samples, and identify the ransomware variant using tools such as ID Ransomware, Volatility, and Chainsaw/Hayabusa to determine the initial access vector and recovery options.

When should I use Investigating Ransomware Attack Artifacts?

Investigating Ransomware Attack Artifacts fits situations like: security work in your project.

How do I install Investigating Ransomware Attack Artifacts in Claude Code?

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

How do I install Investigating Ransomware Attack Artifacts in Codex?

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

Can I use Investigating Ransomware Attack Artifacts 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 investigating-ransomware-attack-artifacts -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/investigating-ransomware-attack-artifacts, .gemini/skills/investigating-ransomware-attack-artifacts, .github/skills/investigating-ransomware-attack-artifacts and .opencode/skills/investigating-ransomware-attack-artifacts in your project.

What does Investigating Ransomware Attack Artifacts need to run?

Going by SKILL.md and its folder, Investigating Ransomware Attack Artifacts needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Investigating Ransomware Attack Artifacts access the network?

SKILL.md names 1 domain. In commands or code: nomoreransom.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Investigating Ransomware Attack Artifacts safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. 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 Investigating Ransomware Attack Artifacts use?

Investigating Ransomware Attack Artifacts 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 Investigating Ransomware Attack Artifacts use?

About 4.1k tokens (SKILL.md is roughly 16k 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 547 tokens, read only when the agent opens those files.

What are the alternatives to Investigating Ransomware Attack Artifacts?

Skills that share tags, products or a category with Investigating Ransomware Attack Artifacts: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars), Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars) and Security Alert Triage (elastic/agent-skills, 592 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Investigating Ransomware Attack Artifacts?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 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.