Agent skill

Detecting Lateral Movement In Network

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to…

Apache-2.0Auto-check: notesSecurity

Install Detecting Lateral Movement In Network

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-lateral-movement-in-network -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-lateral-movement-in-network --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-lateral-movement-in-network .claude/skills/detecting-lateral-movement-in-network && 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-lateral-movement-in-network
GitHub stars
34k
Token cost
~4.3k tokens
SKILL.md length
663 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to…

  • Works in 6 steps: Configure Log Collection for Lateral… → Build Detection Rules for Common Lateral… → Network-Level Detection with Zeek → …
  • Tasks that involve Red teaming and adversary simulation
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls pip3

What it does

Detecting Lateral Movement In Network is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

Its SKILL.md is about 4.3k 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, covering Red teaming and adversary simulation and Security operations. 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

  • Tasks that involve Red teaming and adversary simulation
  • Tasks that involve Security operations

Example prompts

  • “Use the detecting-lateral-movement-in-network skill to identify lateral movement techniques in enterprise networks by analyzing authentication logs…”
  • “/detecting-lateral-movement-in-network”

Requirements

  • Python 3

Workflow steps

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

  1. Configure Log Collection for Lateral Movement Detection
  2. Build Detection Rules for Common Lateral Movement Techniques
  3. Network-Level Detection with Zeek
  4. Threat Hunting for Lateral Movement Indicators
  5. Automated Response and Containment
  6. Build Detection Dashboard

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:

    • pip3

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

  • Network

    No URLs in SKILL.md. Its commands use pip3, which can reach the network depending on how they are called.

    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 Lateral Movement In Network loads about 4.3k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 663 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

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:112
    sudo zeekctl deploy
  • NoteRuns commands with sudoSKILL.md:215
    sudo tee /opt/zeek/share/zeek/site/custom-detections/lateral-movement.zeek << 'ZEEKEOF'
  • NoteRuns commands with sudoSKILL.md:268
    sudo zeekctl deploy
  • NoteRuns commands with sudoSKILL.md:337
    sudo iptables -I FORWARD -s 10.10.5.23 -j DROP

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). 663 words, ~4,258 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-lateral-movement-in-network/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-lateral-movement-in-network
description
Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.
domain
cybersecurity
subdomain
network-security
tags
network-security, lateral-movement, threat-detection, siem, pass-the-hash
version
1.0
author
mahipal
license
Apache-2.0
d3fend_techniques
Application Protocol Command Analysis, Network Isolation, Network Traffic Analysis, Client-server Payload Profiling, Network Traffic Community Deviation
nist_csf
PR.IR-01, DE.CM-01, ID.AM-03, PR.DS-02
mitre_attack
T1046, T1040, T1557, T1071, T1021

Detecting Lateral Movement in Network

When to Use

  • Monitoring enterprise networks for post-compromise lateral movement patterns (pass-the-hash, RDP hopping, PSExec)
  • Building SIEM detection rules and alerts for common MITRE ATT&CK lateral movement techniques (T1021, T1570)
  • Investigating suspected breaches by analyzing authentication patterns and network connections between internal hosts
  • Hunting for anomalous east-west traffic patterns that indicate an attacker pivoting through the network
  • Validating that network segmentation and access controls effectively limit lateral movement paths

Do not use as a substitute for endpoint detection and response (EDR) tools, for monitoring only north-south traffic while ignoring internal traffic flows, or without baseline knowledge of normal internal communication patterns.

Prerequisites

  • Network security monitoring deployed at internal choke points (Zeek, Suricata, or network TAPs)
  • SIEM platform (Splunk, Elastic, Microsoft Sentinel) collecting Windows Security Event Logs, DNS, and flow data
  • Windows Event Log forwarding configured for Security events (4624, 4625, 4648, 4672, 4768, 4769)
  • Baseline of normal internal authentication and connection patterns
  • Understanding of MITRE ATT&CK Lateral Movement tactics (TA0008)

Workflow

Step 1: Configure Log Collection for Lateral Movement Detection
bash
# Windows Event Logs to collect (via WEF or agent):
# Security Log:
#   4624 - Successful logon (Type 3=Network, Type 10=RemoteInteractive)
#   4625 - Failed logon
#   4648 - Logon using explicit credentials (RunAs, PsExec)
#   4672 - Special privileges assigned (admin logon)
#   4768 - Kerberos TGT request
#   4769 - Kerberos service ticket request
#   4776 - NTLM authentication (credential validation)
# System Log:
#   7045 - New service installed (PsExec indicator)
#   7036 - Service started/stopped

# Configure Windows Event Forwarding (WEF) subscription
# On the collector server (PowerShell):
# wecutil cs lateral-movement-subscription.xml

# Filebeat configuration for Windows Event Log shipping
cat > /etc/filebeat/modules.d/security.yml << 'EOF'
- module: system
  auth:
    enabled: true
    var.paths: ["/var/log/auth.log"]
  syslog:
    enabled: true

- module: zeek
  connection:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/conn.log"]
  dns:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/dns.log"]
  smb_mapping:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/smb_mapping.log"]
  dce_rpc:
    enabled: true
    var.paths: ["/opt/zeek/logs/current/dce_rpc.log"]
EOF

# Zeek configuration for lateral movement detection
# Enable SMB, DCE-RPC, and Kerberos logging
cat >> /opt/zeek/share/zeek/site/local.zeek << 'EOF'
@load policy/protocols/smb
@load policy/protocols/conn/known-hosts
@load policy/protocols/conn/known-services
@load frameworks/intel/seen
EOF

sudo zeekctl deploy
Step 2: Build Detection Rules for Common Lateral Movement Techniques
yaml
# Splunk SPL queries for lateral movement detection

# 1. Detect PsExec usage (new service creation on remote hosts)
# index=wineventlog EventCode=7045 ServiceName="PSEXESVC" OR ServiceName="*psexec*"
# | stats count by ComputerName, ServiceName, ImagePath
# | where count > 0

# 2. Detect Pass-the-Hash (Type 3 logon with NTLM)
# index=wineventlog EventCode=4624 LogonType=3 AuthenticationPackageName="NTLM"
# | where TargetUserName!="ANONYMOUS LOGON" AND TargetUserName!="$"
# | stats count dc(ComputerName) as unique_hosts by TargetUserName, IpAddress
# | where unique_hosts > 3

# 3. Detect RDP lateral movement (Type 10 logon from internal IPs)
# index=wineventlog EventCode=4624 LogonType=10
# | where cidrmatch("10.0.0.0/8", IpAddress) OR cidrmatch("192.168.0.0/16", IpAddress)
# | stats count dc(ComputerName) as rdp_hosts by TargetUserName, IpAddress
# | where rdp_hosts > 2

# Elastic SIEM detection rules (KQL)
# event.code: "4624" and winlog.event_data.LogonType: "3"
#   and winlog.event_data.AuthenticationPackageName: "NTLM"
#   and not winlog.event_data.TargetUserName: *$
#   and source.ip: (10.0.0.0/8 or 172.16.0.0/12 or 192.168.0.0/16)
bash
# Sigma rules for lateral movement detection
# Install sigma and convert to target SIEM format
pip3 install sigma-cli

cat > lateral_movement_pth.yml << 'EOF'
title: Pass-the-Hash Lateral Movement Detection
id: f8d98d6c-7a07-4d74-b064-dd4a3c244528
status: experimental
description: Detects network logon with NTLM authentication to multiple hosts
logsource:
    product: windows
    service: security
detection:
    selection:
        EventID: 4624
        LogonType: 3
        AuthenticationPackageName: NTLM
    filter:
        TargetUserName|endswith: '$'
    condition: selection and not filter
    timeframe: 15m
    count:
        field: ComputerName
        min: 3
        group-by: TargetUserName
level: high
tags:
    - attack.lateral_movement
    - attack.t1550.002
EOF

# Convert Sigma rule to Splunk SPL
sigma convert -t splunk lateral_movement_pth.yml

# Convert to Elastic query
sigma convert -t elasticsearch lateral_movement_pth.yml
Step 3: Network-Level Detection with Zeek
bash
# Detect SMB lateral movement (admin$ and c$ share access)
cat /opt/zeek/logs/current/smb_mapping.log | \
  zeek-cut ts id.orig_h id.resp_h path | \
  grep -iE "(admin\$|c\$|ipc\$)" | \
  sort -t$'\t' -k2 | uniq -c | sort -rn

# Detect hosts connecting to many internal hosts on port 445 (SMB spreading)
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h id.resp_p | \
  awk '$4 == 445' | \
  awk '{print $2}' | sort | uniq -c | sort -rn | head -10

# Detect WMI lateral movement (DCE-RPC to IWbemServices)
cat /opt/zeek/logs/current/dce_rpc.log | \
  zeek-cut ts id.orig_h id.resp_h operation | \
  grep -i "wbem\|wmi" | sort | uniq -c | sort -rn

# Detect RDP connections between internal hosts
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h id.resp_p duration | \
  awk '$4 == 3389 && $5 > 60' | \
  sort -t$'\t' -k2 | head -20

# Detect Kerberos ticket-granting anomalies
cat /opt/zeek/logs/current/kerberos.log | \
  zeek-cut ts id.orig_h id.resp_h client service success error_msg | \
  grep -v "true" | head -20

# Custom Zeek script for lateral movement detection
sudo tee /opt/zeek/share/zeek/site/custom-detections/lateral-movement.zeek << 'ZEEKEOF'
@load base/frameworks/notice
@load base/frameworks/sumstats

module LateralMovement;

export {
    redef enum Notice::Type += {
        SMB_Lateral_Spread,
        RDP_Lateral_Chain
    };
    const smb_host_threshold: count = 5 &redef;
    const smb_time_window: interval = 15min &redef;
}

event zeek_init()
{
    local r1 = SumStats::Reducer(
        $stream="lateral.smb",
        $apply=set(SumStats::UNIQUE)
    );

    SumStats::create([
        $name="detect-smb-lateral",
        $epoch=smb_time_window,
        $reducers=set(r1),
        $threshold_val(key: SumStats::Key, result: SumStats::Result) = {
            return result["lateral.smb"]$unique + 0.0;
        },
        $threshold=smb_host_threshold + 0.0,
        $threshold_crossed(key: SumStats::Key, result: SumStats::Result) = {
            NOTICE([
                $note=SMB_Lateral_Spread,
                $msg=fmt("Host %s connected to %d SMB hosts in %s",
                         key$str, result["lateral.smb"]$unique, smb_time_window),
                $identifier=key$str
            ]);
        }
    ]);
}

event connection_state_remove(c: connection)
{
    if ( c$id$resp_p == 445/tcp && c$id$resp_h in Site::local_nets )
    {
        SumStats::observe("lateral.smb",
            [$str=cat(c$id$orig_h)],
            [$str=cat(c$id$resp_h)]
        );
    }
}
ZEEKEOF

sudo zeekctl deploy
Step 4: Threat Hunting for Lateral Movement Indicators
bash
# Hunt for authentication anomalies in Windows logs
# Splunk query: Users authenticating from unusual source hosts
# index=wineventlog EventCode=4624 LogonType=3
# | stats values(IpAddress) as source_ips dc(IpAddress) as source_count by TargetUserName
# | where source_count > 5
# | sort -source_count

# Hunt for service accounts used interactively
# index=wineventlog EventCode=4624 (LogonType=2 OR LogonType=10)
# | where match(TargetUserName, "^svc-.*")
# | table _time ComputerName TargetUserName IpAddress LogonType

# Network flow analysis for lateral movement patterns
# Look for hosts that suddenly start communicating with many internal hosts
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h | \
  awk '{
    key = $2
    targets[key][$3] = 1
  }
  END {
    for (src in targets) {
      count = 0
      for (dst in targets[src]) count++
      if (count > 20) print src, count
    }
  }' | sort -k2 -rn

# Detect credential dumping artifacts (large LSASS reads)
# Look for connections from hosts that suddenly pivot
cat /opt/zeek/logs/current/conn.log | \
  zeek-cut ts id.orig_h id.resp_h id.resp_p orig_bytes | \
  awk '$4 == 445 && $5 > 10000000' | sort -t$'\t' -k5 -rn

# Timeline analysis: map the attack path
# index=wineventlog (EventCode=4624 OR EventCode=7045)
# | eval stage=case(
#     EventCode=4624 AND LogonType=3, "Network Logon",
#     EventCode=4624 AND LogonType=10, "RDP Logon",
#     EventCode=7045, "Service Creation"
#   )
# | timechart span=5m count by stage
Step 5: Automated Response and Containment
bash
# SOAR playbook for lateral movement response (pseudocode)
# When lateral movement alert triggers:

# 1. Enrich the alert with context
# - Query AD for user group membership and role
# - Check if source IP is a known admin workstation
# - Look up recent vulnerability scan results for affected hosts

# 2. Automated containment actions
# Option A: Isolate the host via switch port shutdown
# ssh admin@switch "conf t; interface Gi1/0/5; shutdown"

# Option B: Quarantine via VLAN change (less disruptive)
# ssh admin@switch "conf t; interface Gi1/0/5; switchport access vlan 999"

# Option C: Block at firewall
sudo iptables -I FORWARD -s 10.10.5.23 -j DROP

# 3. Disable the compromised account
# PowerShell: Disable-ADAccount -Identity compromised_user

# 4. Force password reset
# PowerShell: Set-ADAccountPassword -Identity compromised_user -Reset

# 5. Collect forensic evidence before full containment
# velociraptor artifact collect Windows.KapeFiles.Targets --target BasicCollection
Step 6: Build Detection Dashboard
bash
# Elastic Kibana dashboard queries for lateral movement monitoring

# Panel 1: Authentication heatmap (source vs destination)
# Aggregation: Terms on source.ip (rows) and destination.ip (columns)
# Metric: Count of event.code:4624

# Panel 2: SMB connections between internal hosts
# Filter: destination.port:445 and source.ip:10.0.0.0/8
# Aggregation: Top 20 source IPs by unique destination count

# Panel 3: RDP sessions timeline
# Filter: destination.port:3389 and event.code:4624 and winlog.event_data.LogonType:10
# Visualization: Timeline by source.ip

# Panel 4: New service installations
# Filter: event.code:7045
# Aggregation: Terms on winlog.event_data.ServiceName

# Panel 5: Failed authentication spike detection
# Filter: event.code:4625
# Aggregation: Date histogram with anomaly detection

# Export Kibana dashboard
# curl -X GET "elastic-siem:5601/api/saved_objects/_export" \
#   -H "kbn-xsrf: true" \
#   -d '{"type":"dashboard","objects":[{"id":"lateral-movement-dashboard","type":"dashboard"}]}' \
#   > lateral_movement_dashboard.ndjson

Key Concepts

TermDefinition
Lateral MovementMITRE ATT&CK tactic (TA0008) describing techniques attackers use to move through a network from one compromised system to another
Pass-the-Hash (T1550.002)Using captured NTLM password hashes to authenticate to remote systems without knowing the plaintext password
PsExec (T1569.002)Remote service execution tool that creates a temporary service on the target system, detectable by Event ID 7045
East-West TrafficNetwork communication between internal systems (as opposed to north-south traffic between internal and external networks)
Authentication AnomalyDeviation from baseline authentication patterns such as a user logging into systems they never accessed before
Kerberoasting (T1558.003)Requesting Kerberos service tickets for service accounts and cracking them offline, detectable via Event ID 4769 anomalies

Tools & Systems

  • Zeek: Network security monitor generating SMB, Kerberos, DCE-RPC, and connection logs for lateral movement analysis
  • Splunk/Elastic SIEM: Log aggregation platforms for correlating authentication events, network flows, and service creation across the enterprise
  • Sigma: Vendor-agnostic detection rule format for writing portable lateral movement detection rules across SIEM platforms
  • Velociraptor: Endpoint forensics tool for collecting evidence from hosts involved in lateral movement chains
  • BloodHound: Active Directory attack path analysis tool for identifying potential lateral movement routes before attackers exploit them
Show full SKILL.md (257 more words)Show less

Common Scenarios

Scenario: Detecting a Ransomware Operator's Lateral Movement

Context: The SOC receives an alert for PsExec service creation on a file server (10.10.20.15) at 2:00 AM. The alert triggers a lateral movement investigation. The organization has Zeek network monitoring and Windows Event Log forwarding to Splunk.

Approach:

  1. Query Splunk for Event ID 7045 (service creation) on 10.10.20.15 to confirm PsExec execution and identify the source IP (10.10.5.23)
  2. Trace authentication history for 10.10.5.23: find Event ID 4624 Type 3 logons, discovering the host authenticated to 8 servers in the past hour using NTLM (pass-the-hash pattern)
  3. Check Zeek conn.log for 10.10.5.23: identify SMB connections (port 445) to 12 internal hosts and large file transfers to an external IP
  4. Build the attack timeline: initial compromise via phishing at 1:15 AM, credential dumping at 1:25 AM, lateral movement to 8 servers between 1:30-2:00 AM
  5. Identify all compromised hosts by tracing authentication chains: 10.10.5.23 -> 10.10.20.15 -> 10.10.20.16 -> 10.10.20.17
  6. Contain by quarantining all identified hosts to VLAN 999, disabling the compromised account, and blocking the external C2 IP
  7. Report the complete attack chain with timeline, affected hosts, and detection gaps

Pitfalls:

  • Only investigating the single alert instead of tracing the full lateral movement chain across all hosts
  • Not checking for persistence mechanisms on each compromised host before declaring containment
  • Relying solely on Windows Event Logs without correlating network flow data, missing lateral movement via tools that do not generate Windows events
  • Not establishing a baseline of normal internal authentication patterns, making anomaly detection impossible

Output Format

## Lateral Movement Investigation Report

**Case ID**: IR-2024-0312
**Initial Alert**: PsExec on 10.10.20.15 at 02:00 UTC
**Investigation Period**: 2024-03-15 01:00 to 03:00 UTC

### Attack Timeline

| Time (UTC) | Source | Destination | Technique | Evidence |
|------------|--------|-------------|-----------|----------|
| 01:15 | External | 10.10.5.23 | Initial Access (Phishing) | Email log + HTTP download |
| 01:25 | 10.10.5.23 | Local | Credential Dumping | LSASS access (Sysmon EID 10) |
| 01:32 | 10.10.5.23 | 10.10.20.15 | Pass-the-Hash (SMB) | EID 4624 Type 3 NTLM |
| 01:38 | 10.10.5.23 | 10.10.20.16 | PsExec | EID 7045 + Zeek SMB |
| 01:45 | 10.10.20.16 | 10.10.20.17 | RDP | EID 4624 Type 10 |
| 02:00 | 10.10.20.17 | 10.10.20.15 | PsExec (triggered alert) | EID 7045 |
| 02:10 | 10.10.5.23 | 203.0.113.50 | Data Exfiltration | Zeek conn.log 2.3 GB |

### Affected Systems
- 10.10.5.23 (workstation-045) - Initial compromise
- 10.10.20.15 (file-server-01) - Data accessed
- 10.10.20.16 (app-server-02) - Pivoted through
- 10.10.20.17 (db-server-01) - Final target

### Detection Gaps
1. Initial phishing email not blocked by email gateway
2. Credential dumping not detected (no LSASS monitoring)
3. 30-minute gap between first lateral movement and alert

© 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-lateral-movement-in-network 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

Detecting Lateral Movement In Network next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Detecting Lateral Movement In Network compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Detecting Lateral Movement In Network this skillmukul975/Anthropic-Cybersecurity-Skills34k—~4.3kAutomated safety check: NotesApache-2.0
Councilwarpdotdev/common-skills6101 repos~1.8kAutomated safety check: PassMIT
Cybersecurityohmyjahh/xquads-squads277—~895Automated safety check: PassMIT
Rational Red Blue Debatedigoal/blog8.6k—~2.2kAutomated safety check: PassGPL-2.0
Secops Investigategoogle/skills21k1 repos~4.2kAutomated safety check: PassApache-2.0
Threat Huntinghypnguyen1209/offensive-claude388—~2.4kAutomated safety check: PassMIT

Similar skills

  • Council

    warpdotdev/common-skills

    Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation.

    610 GitHub starsUsed in 1 repo~1.8k tokens
    SecurityAuto-check passed
  • Cybersecurity

    ohmyjahh/xquads-squads

    Squad de 15 agentes de seguranca ofensiva e defensiva (Georgia Weidman, Peter Kim, Jim Manico, Chris Sanders, Omar Santos, Marcus Carey) cobrindo pentest, red team, blue team, AppSec, recon e…

    277 GitHub stars~895 tokensUpdated 11 days ago
    SecurityAuto-check passed
  • Answer general or cross-domain questions with a non-pleasing rational mode: adversarial red-team and blue-team expert analysis, mutually exclusive conclusions, up to five debate rounds, saved…

    8.6k GitHub stars~2.2k tokensUpdated 2 days ago
    SecurityAuto-check passed
  • Secops Investigate

    google/skills

    Official

    Expert guidance for deep security incident and entity investigations in Google SecOps.

    21k GitHub starsUsed in 1 repo~4.2k tokens
    SecurityAuto-check passed
  • Threat Hunting

    hypnguyen1209/offensive-claude

    A skill your agent uses when hunting threats or engineering detections — ATT&CK Detection-Strategies, Sigma + correlation with Detection-as-Code CI, Windows endpoint hunting…

    388 GitHub stars~2.4k tokensUpdated 13 days ago
    SecurityAuto-check passed
  • Purple Team

    gaasher/Agent-Loop-Skills

    A skill your agent uses when the user wants to automatically harden a guardrail, classifier, content filter, prompt, or API they own by running attack and defense together as a closed loop, not just…

    174 GitHub stars~2.6k tokensUpdated 3 mo ago
    SecurityAuto-check passed

More from mukul975/Anthropic-Cybersecurity-Skills

All 644 skills in this repo
  • Campaign Attribution Evidence Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.

    34k GitHub stars~2.3k tokensUpdated 1 mo ago
    Auto-check passed
  • Go Malware Analysis in Ghidra

    mukul975/Anthropic-Cybersecurity-Skills

    Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • LNK and Jump List Forensics

    mukul975/Anthropic-Cybersecurity-Skills

    Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • Malware Persistence Analysis with Autoruns

    mukul975/Anthropic-Cybersecurity-Skills

    Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.

    34k GitHub stars~1.2k tokensUpdated 1 mo ago
    Auto-check passed
  • NTFS MFT Deleted File Recovery

    mukul975/Anthropic-Cybersecurity-Skills

    Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.

    34k GitHub stars~2.7k tokensUpdated 1 mo ago
    Auto-check passed
  • Network Covert Channel Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.

    34k GitHub stars~2k tokensUpdated 1 mo ago
    Auto-check passed

Categories

Questions about Detecting Lateral Movement In Network

What does Detecting Lateral Movement In Network do?

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to…. Detecting Lateral Movement In Network is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

When should I use Detecting Lateral Movement In Network?

Detecting Lateral Movement In Network fits situations like: tasks that involve Red teaming and adversary simulation; tasks that involve Security operations.

How do I install Detecting Lateral Movement In Network in Claude Code?

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

How do I install Detecting Lateral Movement In Network in Codex?

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

Can I use Detecting Lateral Movement In Network 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-lateral-movement-in-network -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-lateral-movement-in-network, .gemini/skills/detecting-lateral-movement-in-network, .github/skills/detecting-lateral-movement-in-network and .opencode/skills/detecting-lateral-movement-in-network in your project.

What does Detecting Lateral Movement In Network need to run?

Going by SKILL.md and its folder, Detecting Lateral Movement In Network needs Python for the scripts in its folder and the command-line tools its instructions call (pip3). Our summary lists: Python 3.

Does Detecting Lateral Movement In Network 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 Lateral Movement In Network 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 Detecting Lateral Movement In Network use?

Detecting Lateral Movement In Network 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 Lateral Movement In Network use?

About 4.3k 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 694 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Lateral Movement In Network?

Skills that share tags, products or a category with Detecting Lateral Movement In Network: Council (warpdotdev/common-skills, 610 stars), Cybersecurity (ohmyjahh/xquads-squads, 277 stars), Rational Red Blue Debate (digoal/blog, 8.6k stars) and Secops Investigate (google/skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Lateral Movement In Network?

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.