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Agent skill

Detecting Ransomware Encryption Behavior

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal…

Apache-2.0Auto-check passedSecurity

Install Detecting Ransomware Encryption Behavior

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-ransomware-encryption-behavior -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-ransomware-encryption-behavior --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-ransomware-encryption-behavior .claude/skills/detecting-ransomware-encryption-behavior && 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-ransomware-encryption-behavior
GitHub stars
34k
Token cost
~2.2k tokens
SKILL.md length
468 words
Files
4 (incl. scripts, references)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal…

  • Works in 5 steps: Establish Entropy Baselines → Implement Real-Time Entropy Monitoring → Monitor File System I/O Patterns → …
  • Building real-time ransomware detection
  • SKILL.md covers When to Use, Prerequisites, Workflow and Verification, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Detecting Ransomware Encryption Behavior is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal entropy spikes in written data, and suspicious process behavior characteristic of encryption routines. Use when building real-time ransomware detection, tuning entropy thresholds, or investigating suspected active encryption on an endpoint.

Its SKILL.md is about 2.2k 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

  • Building real-time ransomware detection
  • Tuning entropy thresholds
  • Investigating suspected active encryption on an endpoint

Example prompts

  • “Use the detecting-ransomware-encryption-behavior skill to detect ransomware encryption activity in real time using entropy analysis, file system I/O…”
  • “/detecting-ransomware-encryption-behavior”

Requirements

  • Python 3

Workflow steps

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

  1. Establish Entropy Baselines
  2. Implement Real-Time Entropy Monitoring
  3. Monitor File System I/O Patterns
  4. Implement Behavioral Scoring
  5. Configure Automated Response Thresholds

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.

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

  • Network

    No URLs in SKILL.md.

    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 Ransomware Encryption Behavior loads about 2.2k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 120 tokens; SKILL.md has 468 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~120
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

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). 468 words, ~2,158 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-ransomware-encryption-behavior/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-ransomware-encryption-behavior
description
Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal entropy spikes in written data, and suspicious process behavior characteristic of encryption routines. Use when building real-time ransomware detection, tuning entropy thresholds, or investigating suspected active encryption on an endpoint.
domain
cybersecurity
subdomain
ransomware-defense
tags
ransomware, detection, entropy, behavioral-analysis, file-monitoring, heuristics
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
PR.DS-11, RS.MA-01, RC.RP-01, PR.IR-01
mitre_attack
T1078, T1190, T1059, T1486, T1490
mitre_f3.version
1.1
mitre_f3.tactics
monetization, positioning, stealth

Detecting Ransomware Encryption Behavior

When to Use

  • Building or tuning a behavioral detection layer for ransomware that catches unknown/zero-day variants
  • Monitoring file servers and endpoints for mass encryption activity that evades signature-based detection
  • Implementing entropy-based detection to identify when files are being replaced with encrypted (high-entropy) content
  • Analyzing suspicious process behavior patterns: rapid sequential file opens, writes, renames, and deletes
  • Validating EDR detection rules against actual ransomware encryption patterns during red team exercises

Do not use entropy analysis alone as the only detection signal. Compressed files (ZIP, JPEG, MP4) naturally have high entropy and will cause false positives. Always combine entropy with behavioral signals like I/O rate and file rename patterns.

Prerequisites

  • Python 3.8+ with watchdog and psutil libraries
  • Administrative access for process monitoring and file system event capture
  • Understanding of Shannon entropy and its application to file content analysis
  • Windows: Sysmon installed for detailed process and file system event logging
  • Linux: auditd configured for file access monitoring, or inotify-based watchers
  • Baseline entropy values for common file types in the monitored environment

Workflow

Step 1: Establish Entropy Baselines

Calculate normal entropy ranges for files in the environment:

Entropy Baselines by File Type:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
File Type       Normal Entropy    Encrypted Entropy
.docx           3.5 - 6.5        7.8 - 8.0
.xlsx           4.0 - 6.8        7.8 - 8.0
.pdf            5.0 - 7.2        7.8 - 8.0
.txt            2.0 - 5.0        7.8 - 8.0
.csv            2.0 - 5.5        7.8 - 8.0
.sql            2.5 - 5.0        7.8 - 8.0
.jpg/.png       7.0 - 7.9        7.9 - 8.0 (hard to distinguish)
.zip/.7z        7.5 - 8.0        7.9 - 8.0 (hard to distinguish)

Key insight: Text-based files show the largest entropy jump when encrypted,
making them the best candidates for entropy-based detection.
Step 2: Implement Real-Time Entropy Monitoring

Monitor file writes and calculate entropy of new content:

python
import math
from collections import Counter

def shannon_entropy(data):
    """Calculate Shannon entropy of byte data (0.0 to 8.0 scale)."""
    if not data:
        return 0.0
    freq = Counter(data)
    length = len(data)
    return -sum((c / length) * math.log2(c / length) for c in freq.values())

def is_encryption_entropy(data, threshold=7.5):
    """Check if data entropy indicates encryption."""
    entropy = shannon_entropy(data)
    return entropy >= threshold, entropy
Step 3: Monitor File System I/O Patterns

Track process-level file operations for ransomware patterns:

Ransomware I/O Behavior Signatures:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Rapid sequential file modification:
   - >20 files modified per minute by single process
   - Read original → Write encrypted → Rename with new extension
   - Pattern: CreateFile → ReadFile → WriteFile → CloseHandle → MoveFile

2. File extension changes:
   - Original: report.docx → Encrypted: report.docx.locked
   - Many extensions changed within short time window

3. Ransom note creation:
   - Same text file (README.txt, DECRYPT.html) created in multiple directories
   - Created immediately after file encryption in each directory

4. Shadow copy deletion:
   - vssadmin.exe delete shadows /all /quiet
   - wmic.exe shadowcopy delete
   - PowerShell: Get-WmiObject Win32_Shadowcopy | Remove-WmiObject

5. Entropy spike pattern:
   - File read: entropy 3.5 (normal document)
   - File write: entropy 7.9 (encrypted content)
   - Delta > 3.0 is strong ransomware indicator
Step 4: Implement Behavioral Scoring

Combine multiple signals into a composite ransomware score:

python
def calculate_ransomware_score(process_metrics):
    """Score process behavior for ransomware likelihood (0-100)."""
    score = 0

    # High file modification rate
    files_per_min = process_metrics.get("files_modified_per_minute", 0)
    if files_per_min > 50:
        score += 30
    elif files_per_min > 20:
        score += 15

    # Entropy increase in written files
    avg_entropy_delta = process_metrics.get("avg_entropy_delta", 0)
    if avg_entropy_delta > 3.0:
        score += 30
    elif avg_entropy_delta > 2.0:
        score += 15

    # File extension changes
    extension_changes = process_metrics.get("extension_changes", 0)
    if extension_changes > 10:
        score += 20
    elif extension_changes > 3:
        score += 10

    # Ransom note creation
    if process_metrics.get("ransom_note_created", False):
        score += 20

    return min(score, 100)
Step 5: Configure Automated Response Thresholds

Set detection thresholds and automated containment actions:

Detection Thresholds:
━━━━━━━━━━━━━━━━━━━━
Score 0-25:   INFORMATIONAL - Log only, no action
Score 25-50:  LOW - Alert SOC for investigation
Score 50-75:  HIGH - Alert SOC, suspend process, snapshot VM
Score 75-100: CRITICAL - Kill process, isolate endpoint, alert IR team

Automated Response Actions:
  - Suspend/kill the encrypting process
  - Disable network adapter to prevent lateral movement
  - Create volume shadow copy snapshot before further damage
  - Capture process memory dump for forensic analysis
  - Send SIEM alert with process details, affected files, and timeline

Verification

  • Test detection against known ransomware samples in an isolated sandbox environment
  • Verify that entropy monitoring correctly identifies encrypted vs. compressed files
  • Confirm that behavioral scoring produces low false-positive rates on normal workloads
  • Validate automated response actions execute within acceptable time (under 5 seconds)
  • Test with multiple ransomware families (LockBit, BlackCat, Conti) to verify coverage
  • Benchmark monitoring overhead to ensure it does not degrade endpoint performance
Show full SKILL.md (160 more words)Show less

Key Concepts

TermDefinition
Shannon EntropyMathematical measure of randomness in data (0-8 for bytes); encrypted data approaches 8.0, while text files are typically 2-5
Differential EntropyThe change in entropy between a file's original and modified content; a spike indicates encryption
I/O Rate AnomalyAbnormally high rate of file read/write operations by a single process, characteristic of bulk encryption
Behavioral ScoringCombining multiple weak signals (entropy, I/O rate, file renames) into a composite confidence score
Entropy EvasionTechniques used by advanced ransomware to defeat entropy detection, such as Base64 encoding output or partial encryption

Tools & Systems

  • Sysmon: Windows system monitor providing detailed file system and process events for behavioral analysis
  • watchdog (Python): Cross-platform file system monitoring library for real-time file change detection
  • psutil (Python): Process and system monitoring library for tracking per-process I/O statistics
  • Elastic Endpoint: Commercial endpoint protection with built-in ransomware behavioral detection using canary files
  • Wazuh: Open-source security platform with file integrity monitoring and active response capabilities

© 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/detecting-ransomware-encryption-behavior of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Detecting Ransomware Encryption Behavior

What does Detecting Ransomware Encryption Behavior do?

Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal…. Detecting Ransomware Encryption Behavior is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring (Sysmon, watchdog, psutil), and behavioral scoring to identify mass file modification, abnormal entropy spikes in written data, and suspicious process behavior characteristic of encryption routines.

When should I use Detecting Ransomware Encryption Behavior?

Detecting Ransomware Encryption Behavior fits situations like: building real-time ransomware detection; tuning entropy thresholds; investigating suspected active encryption on an endpoint.

How do I install Detecting Ransomware Encryption Behavior in Claude Code?

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

How do I install Detecting Ransomware Encryption Behavior in Codex?

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

Can I use Detecting Ransomware Encryption Behavior 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-ransomware-encryption-behavior -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-ransomware-encryption-behavior, .gemini/skills/detecting-ransomware-encryption-behavior, .github/skills/detecting-ransomware-encryption-behavior and .opencode/skills/detecting-ransomware-encryption-behavior in your project.

What does Detecting Ransomware Encryption Behavior need to run?

Going by SKILL.md and its folder, Detecting Ransomware Encryption Behavior needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Detecting Ransomware Encryption Behavior access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Detecting Ransomware Encryption Behavior 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 Ransomware Encryption Behavior use?

Detecting Ransomware Encryption Behavior 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 Ransomware Encryption Behavior use?

About 2.2k tokens (SKILL.md is roughly 8.6k 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 656 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Ransomware Encryption Behavior?

Skills that share tags, products or a category with Detecting Ransomware Encryption Behavior: Fla Ascend Performance (fla-org/flash-linear-attention, 5.8k stars), Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars) and Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 480 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Ransomware Encryption Behavior?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 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.