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Agent skill
Implements OT network segmentation using VLANs, OT-aware firewalls, data diodes, and IEC 62443 zone/conduit architecture, with a traffic-baseline-driven design tool for migrating flat Purdue-model…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-network-segmentation-for-ot --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/implementing-network-segmentation-for-ot .claude/skills/implementing-network-segmentation-for-ot && rm -rf skills-srcUse ~/.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/
Install the "implementing-network-segmentation-for-ot" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-ot into .claude/skills/implementing-network-segmentation-for-ot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-network-segmentation-for-ot", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-otType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-network-segmentation-for-ot --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/implementing-network-segmentation-for-ot .agents/skills/implementing-network-segmentation-for-ot && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "implementing-network-segmentation-for-ot" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-ot into .agents/skills/implementing-network-segmentation-for-ot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-network-segmentation-for-ot", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-network-segmentation-for-ot --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/implementing-network-segmentation-for-ot .cursor/skills/implementing-network-segmentation-for-ot && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "implementing-network-segmentation-for-ot" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-ot into .cursor/skills/implementing-network-segmentation-for-ot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-network-segmentation-for-ot", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/implementing-network-segmentation-for-ot--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-network-segmentation-for-ot --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/implementing-network-segmentation-for-ot .gemini/skills/implementing-network-segmentation-for-ot && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "implementing-network-segmentation-for-ot" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-ot into .gemini/skills/implementing-network-segmentation-for-ot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-network-segmentation-for-ot", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-network-segmentation-for-otInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/implementing-network-segmentation-for-ot .github/skills/implementing-network-segmentation-for-ot && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "implementing-network-segmentation-for-ot" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-ot into .github/skills/implementing-network-segmentation-for-ot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-network-segmentation-for-ot", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-network-segmentation-for-ot --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/implementing-network-segmentation-for-ot .opencode/skills/implementing-network-segmentation-for-ot && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "implementing-network-segmentation-for-ot" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-network-segmentation-for-ot into .opencode/skills/implementing-network-segmentation-for-ot/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-network-segmentation-for-ot", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
implementing-network-segmentation-for-otImplements OT network segmentation using VLANs, OT-aware firewalls, data diodes, and IEC 62443 zone/conduit architecture, with a traffic-baseline-driven design tool for migrating flat Purdue-model…
Implementing Network Segmentation For Ot is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Implements OT network segmentation using VLANs, OT-aware firewalls, data diodes, and IEC 62443 zone/conduit architecture, with a traffic-baseline-driven design tool for migrating flat Purdue-model networks without disrupting operations. Use when segmenting a flat OT network into Purdue levels, deploying an IT/OT DMZ, or isolating safety instrumented systems from basic process control systems.
Its SKILL.md is about 4.5k 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.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Implementing Network Segmentation For Ot loads about 4.5k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 405 words of instructions outside code blocks.
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.
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.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 405 words, ~4,491 tokens.
.claude/skills/implementing-network-segmentation-for-ot/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Do not use for IT-only microsegmentation without OT components (see implementing-zero-trust-in-cloud), or for initial zone design without prior traffic analysis (see performing-ot-network-security-assessment first).
Use the traffic baseline to design VLAN and firewall architecture that preserves all legitimate communication paths while isolating zones.
#!/usr/bin/env python3
"""OT Network Segmentation Design Tool.
Analyzes traffic baseline data and generates a segmentation design
with VLAN assignments, firewall rules, and migration plan.
"""
import json
import sys
from collections import defaultdict
from dataclasses import dataclass, field, asdict
from ipaddress import ip_address, ip_network
@dataclass
class VLANDesign:
vlan_id: int
name: str
purdue_level: str
subnet: str
gateway: str
description: str
devices: list = field(default_factory=list)
@dataclass
class FirewallRule:
rule_id: int
source_zone: str
source_ip: str
dest_zone: str
dest_ip: str
protocol: str
port: int
action: str
dpi_profile: str = ""
comment: str = ""
class SegmentationDesigner:
"""Generates segmentation design from traffic baseline."""
def __init__(self, baseline_file):
with open(baseline_file) as f:
self.baseline = json.load(f)
self.vlans = []
self.rules = []
self.rule_counter = 1
def design_vlans(self):
"""Create VLAN design based on Purdue levels."""
self.vlans = [
VLANDesign(10, "SIS-SAFETY", "Level 1 (Safety)",
"10.10.10.0/24", "10.10.10.1",
"Safety Instrumented Systems - air-gapped or hardware-isolated"),
VLANDesign(20, "BPCS-FIELD", "Level 0-1 (Field/Control)",
"10.10.20.0/24", "10.10.20.1",
"PLCs, RTUs, I/O modules, field instruments"),
VLANDesign(30, "BPCS-SUPERVISORY", "Level 2 (Supervisory)",
"10.10.30.0/24", "10.10.30.1",
"HMIs, engineering workstations, local historian"),
VLANDesign(40, "SITE-OPS", "Level 3 (Operations)",
"10.10.40.0/24", "10.10.40.1",
"Site historian, OPC server, MES, alarm management"),
VLANDesign(50, "OT-DMZ", "Level 3.5 (DMZ)",
"172.16.50.0/24", "172.16.50.1",
"Data diode, historian mirror, jump server, patch server"),
VLANDesign(60, "ENTERPRISE", "Level 4 (Enterprise)",
"10.0.60.0/24", "10.0.60.1",
"Enterprise IT systems accessing OT data"),
VLANDesign(999, "QUARANTINE", "Quarantine",
"10.10.99.0/24", "10.10.99.1",
"Quarantine VLAN for unauthorized or untrusted devices"),
]
return self.vlans
def generate_firewall_rules_from_baseline(self):
"""Generate firewall rules based on observed legitimate traffic."""
self.rules = []
# Default deny rules for each zone boundary
zone_pairs = [
("Level 2", "Level 0-1"),
("Level 3", "Level 2"),
("Level 3.5", "Level 3"),
("Level 4", "Level 3.5"),
]
# Generate allow rules from baseline observed traffic
for flow in self.baseline.get("cross_zone_flows", []):
self.rules.append(FirewallRule(
rule_id=self.rule_counter,
source_zone=flow["src_level"],
source_ip=flow["src"],
dest_zone=flow["dst_level"],
dest_ip=flow["dst"],
protocol=flow.get("protocol", "TCP"),
port=flow.get("port", 0),
action="ALLOW",
dpi_profile=self._get_dpi_profile(flow.get("port", 0)),
comment=f"Baseline observed: {flow['src']} -> {flow['dst']}",
))
self.rule_counter += 1
# Add default deny rules at the end of each zone ACL
for src_zone, dst_zone in zone_pairs:
self.rules.append(FirewallRule(
rule_id=self.rule_counter,
source_zone=src_zone,
source_ip="any",
dest_zone=dst_zone,
dest_ip="any",
protocol="any",
port=0,
action="DENY",
comment=f"Default deny: {src_zone} -> {dst_zone}",
))
self.rule_counter += 1
return self.rules
def _get_dpi_profile(self, port):
"""Return the appropriate DPI inspection profile for an OT protocol port."""
dpi_profiles = {
502: "modbus-inspect (allow read FC only from L3)",
44818: "enip-inspect",
4840: "opcua-inspect (require SignAndEncrypt)",
102: "s7comm-inspect",
20000: "dnp3-inspect",
}
return dpi_profiles.get(port, "none")
def generate_migration_plan(self):
"""Generate phased migration plan for network segmentation."""
plan = {
"phase_1": {
"name": "DMZ Implementation (Week 1-2)",
"description": "Deploy DMZ between enterprise and OT networks",
"steps": [
"Deploy DMZ firewall pair (inside and outside)",
"Migrate historian mirror to DMZ",
"Configure jump server in DMZ with MFA",
"Install data diode for unidirectional historian replication",
"Route enterprise-to-OT traffic through DMZ",
"Verify enterprise access to historian data via DMZ",
],
"rollback": "Remove DMZ firewall rules, restore direct routing",
},
"phase_2": {
"name": "L3/L2 Segmentation (Week 3-4)",
"description": "Separate operations (L3) from control (L2) zones",
"steps": [
"Create VLAN 30 and VLAN 40 on OT switches",
"Deploy industrial firewall between L2 and L3",
"Configure firewall in monitor mode (log only, no blocking)",
"Analyze logs for 1 week to validate rule completeness",
"Switch to enforcement mode during maintenance window",
"Validate all HMI-to-PLC and historian-to-PLC communications",
],
"rollback": "Revert VLAN assignments, set firewall to permit-any",
},
"phase_3": {
"name": "Field Device Isolation (Week 5-6)",
"description": "Isolate Level 0-1 field devices from Level 2 supervisory",
"steps": [
"Create VLAN 20 for PLCs and field instruments",
"Configure port security with MAC binding on PLC ports",
"Apply Modbus function code filtering (block writes from L3)",
"Test all control loops during maintenance window",
"Verify alarm propagation from field to HMI",
],
"rollback": "Merge VLAN 20 back into VLAN 30",
},
"phase_4": {
"name": "SIS Isolation (Week 7-8)",
"description": "Fully isolate Safety Instrumented Systems",
"steps": [
"Verify SIS is on dedicated VLAN 10 or air-gapped",
"Remove any network path between SIS and BPCS",
"Implement dedicated engineering workstation for SIS",
"Apply USB and removable media controls on SIS EWS",
"Test SIS functionality in isolation",
],
"rollback": "N/A - SIS isolation should not be reversed",
},
}
return plan
def export_design(self, output_file):
"""Export complete segmentation design."""
design = {
"vlans": [asdict(v) for v in self.vlans],
"firewall_rules": [asdict(r) for r in self.rules],
"migration_plan": self.generate_migration_plan(),
}
with open(output_file, "w") as f:
json.dump(design, f, indent=2)
print(f"[*] Segmentation design exported to: {output_file}")
print(f" VLANs: {len(self.vlans)}")
print(f" Firewall Rules: {len(self.rules)}")
return design
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Usage: python segmentation_designer.py <baseline.json> [output.json]")
sys.exit(1)
designer = SegmentationDesigner(sys.argv[1])
designer.design_vlans()
designer.generate_firewall_rules_from_baseline()
output = sys.argv[2] if len(sys.argv) > 2 else "segmentation_design.json"
designer.export_design(output)Apply VLAN configuration to industrial Ethernet switches with port security and unused port hardening.
# Cisco Industrial Ethernet 4000/5000 Series Configuration
# Create VLANs aligned with Purdue levels
vlan 10
name SIS-SAFETY-L1
vlan 20
name BPCS-FIELD-L01
vlan 30
name BPCS-SUPERVISORY-L2
vlan 40
name SITE-OPS-L3
vlan 50
name OT-DMZ-L35
vlan 999
name QUARANTINE
# PLC access ports with port security
interface range GigabitEthernet1/0/1-12
description PLC Connections
switchport mode access
switchport access vlan 20
switchport port-security
switchport port-security maximum 1
switchport port-security mac-address sticky
switchport port-security violation shutdown
storm-control broadcast level 10
storm-control multicast level 10
spanning-tree portfast
spanning-tree bpduguard enable
no cdp enable
no lldp transmit
no lldp receive
# HMI access ports
interface range GigabitEthernet1/0/13-18
description HMI Stations
switchport mode access
switchport access vlan 30
switchport port-security
switchport port-security maximum 1
switchport port-security mac-address sticky
switchport port-security violation restrict
spanning-tree portfast
# Trunk to zone firewall
interface TenGigabitEthernet1/0/1
description Trunk to OT Zone Firewall
switchport mode trunk
switchport trunk allowed vlan 20,30,40,50
switchport trunk native vlan 999
switchport nonegotiate
# Disable and quarantine all unused ports
interface range GigabitEthernet1/0/19-48
description UNUSED - Shutdown
switchport mode access
switchport access vlan 999
shutdownAfter implementation, validate that segmentation correctly blocks unauthorized cross-zone traffic while permitting all legitimate operations.
#!/usr/bin/env python3
"""OT Network Segmentation Validator.
Runs automated tests to verify zone isolation, firewall rules,
and protocol enforcement after segmentation deployment.
"""
import json
import socket
import subprocess
import sys
import time
from dataclasses import dataclass, asdict
@dataclass
class ValidationTest:
test_id: str
description: str
source_zone: str
target_ip: str
target_port: int
expected_result: str # "blocked" or "allowed"
actual_result: str = ""
status: str = "" # PASS or FAIL
class SegmentationValidator:
"""Validates OT network segmentation implementation."""
def __init__(self):
self.tests = []
self.results = []
def add_test(self, test):
self.tests.append(test)
def run_connectivity_test(self, target_ip, target_port, timeout=3):
"""Test TCP connectivity to target."""
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(timeout)
result = sock.connect_ex((target_ip, target_port))
sock.close()
return "reachable" if result == 0 else "blocked"
except (socket.timeout, ConnectionRefusedError):
return "blocked"
except Exception:
return "error"
def run_all_tests(self):
"""Execute all segmentation validation tests."""
print("=" * 60)
print("OT SEGMENTATION VALIDATION")
print("=" * 60)
passed = 0
failed = 0
for test in self.tests:
actual = self.run_connectivity_test(test.target_ip, test.target_port)
test.actual_result = actual
if actual == test.expected_result:
test.status = "PASS"
passed += 1
else:
test.status = "FAIL"
failed += 1
icon = "[+]" if test.status == "PASS" else "[-]"
print(f" {icon} {test.test_id}: {test.description}")
print(f" Target: {test.target_ip}:{test.target_port}")
print(f" Expected: {test.expected_result} | Actual: {actual} -> {test.status}")
print(f"\n Results: {passed} passed, {failed} failed out of {len(self.tests)} tests")
return {"passed": passed, "failed": failed, "total": len(self.tests)}
if __name__ == "__main__":
validator = SegmentationValidator()
# Tests from Enterprise zone (Level 4) - should be blocked from OT
validator.add_test(ValidationTest(
"SEG-001", "Enterprise cannot reach PLCs via Modbus",
"Level 4", "10.10.20.10", 502, "blocked"))
validator.add_test(ValidationTest(
"SEG-002", "Enterprise cannot reach PLCs via EtherNet/IP",
"Level 4", "10.10.20.10", 44818, "blocked"))
validator.add_test(ValidationTest(
"SEG-003", "Enterprise can reach DMZ jump server",
"Level 4", "172.16.50.10", 3389, "allowed"))
validator.add_test(ValidationTest(
"SEG-004", "Enterprise can reach DMZ historian mirror",
"Level 4", "172.16.50.20", 443, "allowed"))
# Tests from Operations zone (Level 3) - limited access to control
validator.add_test(ValidationTest(
"SEG-005", "Operations can read from PLCs via Modbus",
"Level 3", "10.10.20.10", 502, "allowed"))
validator.add_test(ValidationTest(
"SEG-006", "Operations cannot reach SIS controllers",
"Level 3", "10.10.10.10", 1502, "blocked"))
validator.run_all_tests()| Term | Definition |
|---|---|
| VLAN | Virtual Local Area Network - Layer 2 broadcast domain isolation used to separate OT zones on shared switch infrastructure |
| Industrial Firewall | Firewall with deep packet inspection capabilities for industrial protocols (Modbus, DNP3, EtherNet/IP, OPC UA) |
| Data Diode | Hardware-enforced unidirectional gateway that physically prevents reverse data flow, used between OT operations and DMZ |
| Port Security | Switch feature that limits the number of MAC addresses on a port and locks assignments, preventing unauthorized device connections |
| Trunk Port | Switch port carrying multiple VLANs using 802.1Q tagging, used to connect switches and firewalls across zone boundaries |
| DMZ | Demilitarized Zone between enterprise IT and OT - a buffer zone where all cross-domain traffic terminates and is inspected |
OT Network Segmentation Report
================================
Implementation Date: YYYY-MM-DD
VLAN ARCHITECTURE:
VLAN [ID] - [Name] ([Purdue Level])
Subnet: [subnet/mask]
Devices: [count]
FIREWALL RULES:
[Zone A] -> [Zone B]: [allow/deny count]
VALIDATION RESULTS:
Tests Passed: [N]/[Total]
Critical Failures: [N]© 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
SKILL.md and 3 other files (scripts, references) in skills/implementing-network-segmentation-for-ot of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Implementing Network Segmentation For Ot 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Implementing Network Segmentation For Ot this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~4.5k | Automated safety check: Pass | Apache-2.0 | |
| Fla Ascend Performancefla-org/flash-linear-attention | 5.8k | — | ~6.3k | Automated safety check: Pass | MIT | |
| Deepsec Documentation Guidevercel-labs/deepsec | 8.1k | — | ~956 | Automated safety check: Pass | Apache-2.0 | |
| Skill Scannergetsentry/skills | 1k | 4 repos | ~2.5k | Automated safety check: Warn | Apache-2.0 | |
| Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit | 480 | 1 repos | ~3.3k | Automated safety check: Pass | None | |
| Security Alert Triageelastic/agent-skills | 592 | 1 repos | ~3.5k | Automated safety check: Notes | Apache-2.0 |
fla-org/flash-linear-attention
Guidelines for Ascend NPU kernel / Triton-Ascend backend performance work in the FLA repo.
vercel-labs/deepsec
Points the agent at deepsec's own docs to answer questions about initializing, configuring, resuming, scanning with and extending the vulnerability scanner.
getsentry/skills
Scan agent skills for security issues. An agent skill from getsentry/skills.
yan-labs/serenity-aleabitoreddit
Apply trader Serenity's (@aleabitoreddit) AI/semiconductor supply-chain analytical lens to US-stock ideas and market judgment.
elastic/agent-skills
Triage Elastic Security alerts — gather context, classify threats, create cases, and acknowledge.
SummerSec/ShiroAttack2
当用户要求利用、检测或测试 Apache Shiro rememberMe 反序列化漏洞 (Shiro-550, CVE-2016-4437) 时使用。触发词包括 "Shiro"、"rememberMe"、"shiro attack"、"CVE-2016-4437"、"Shiro-550"、"爆破 Shiro key"、"利用 Shiro"、"Shiro…
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.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
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.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
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.
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.
Categories
Implements OT network segmentation using VLANs, OT-aware firewalls, data diodes, and IEC 62443 zone/conduit architecture, with a traffic-baseline-driven design tool for migrating flat Purdue-model…. Implementing Network Segmentation For Ot is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Implements OT network segmentation using VLANs, OT-aware firewalls, data diodes, and IEC 62443 zone/conduit architecture, with a traffic-baseline-driven design tool for migrating flat Purdue-model networks without disrupting operations.
Implementing Network Segmentation For Ot fits situations like: segmenting a flat OT network into Purdue levels; deploying an IT/OT DMZ; isolating safety instrumented systems from basic process control systems.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a claude-code`. Or copy the skill folder (skills/implementing-network-segmentation-for-ot in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/implementing-network-segmentation-for-ot in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-network-segmentation-for-ot -a codex`. Or copy the skill folder (skills/implementing-network-segmentation-for-ot in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/implementing-network-segmentation-for-ot in your project. Codex loads it when a task matches its description.
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 implementing-network-segmentation-for-ot -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/implementing-network-segmentation-for-ot, .gemini/skills/implementing-network-segmentation-for-ot, .github/skills/implementing-network-segmentation-for-ot and .opencode/skills/implementing-network-segmentation-for-ot in your project.
Going by SKILL.md and its folder, Implementing Network Segmentation For Ot needs Python for the scripts in its folder. Our summary lists: Python 3.
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.
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.
Implementing Network Segmentation For Ot 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.
About 4.5k tokens (SKILL.md is roughly 18k 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 341 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Implementing Network Segmentation For Ot: 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.
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.