performing-ics-asset-discovery-with-claroty

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-ics-asset-discovery-with-claroty
0 commentsdiscussion
summary

Perform comprehensive ICS/OT asset discovery using Claroty xDome platform, leveraging passive monitoring, Claroty Edge active queries, and integration ecosystem to gain full visibility into industrial control system assets including PLCs, RTUs, HMIs, and network infrastructure across Purdue Model levels.

skill.md
name
performing-ics-asset-discovery-with-claroty
description
'Perform comprehensive ICS/OT asset discovery using Claroty xDome platform, leveraging passive monitoring, Claroty Edge active queries, and integration ecosystem to gain full visibility into industrial control system assets including PLCs, RTUs, HMIs, and network infrastructure across Purdue Model levels. '
domain
cybersecurity
subdomain
ot-ics-security
tags
- ot-security - ics - asset-discovery - claroty - xdome - scada - network-visibility - iec62443
version
'1.0'
author
mahipal
license
Apache-2.0
nist_ai_rmf
- MEASURE-2.7 - MAP-5.1 - MANAGE-2.4
atlas_techniques
- AML.T0070 - AML.T0066 - AML.T0082
nist_csf
- PR.IR-01 - DE.CM-01 - ID.AM-05 - GV.OC-02

Performing ICS Asset Discovery with Claroty

When to Use

  • When gaining initial visibility into an OT environment with unknown or poorly documented assets
  • When preparing for an IEC 62443 risk assessment requiring a complete asset inventory
  • When onboarding Claroty xDome into a brownfield industrial environment
  • When validating existing asset inventory against actual network communications
  • When identifying shadow OT devices or unauthorized connections in the control network

Do not use for IT-only asset discovery (use tools like Nessus or Qualys), for active scanning of sensitive PLC networks without vendor approval, or for environments where Claroty is not the deployed platform (see implementing-ot-network-traffic-analysis-with-nozomi).

Prerequisites

  • Claroty xDome SaaS subscription or on-premises deployment
  • Network TAP or SPAN port configured at OT network boundaries (Levels 1-3 of Purdue Model)
  • Claroty Edge collector deployed for safe active querying of hard-to-reach network segments
  • Integration credentials for CMDB tools (ServiceNow, BMC) if used
  • Network architecture diagram showing VLANs, switches, and firewall zones

Workflow

Step 1: Configure Passive Network Monitoring

Deploy Claroty sensors on SPAN ports to passively observe all OT network traffic without impacting operations.

#!/usr/bin/env python3
"""Claroty xDome Asset Discovery Configuration and Reporting Tool.

Automates the configuration of passive monitoring sensors and generates
asset inventory reports from Claroty xDome API.
"""

import json
import sys
import csv
from datetime import datetime
from typing import Optional

try:
    import requests
except ImportError:
    print("Install requests: pip install requests")
    sys.exit(1)


class ClarotyAssetDiscovery:
    """Interface with Claroty xDome API for ICS asset discovery."""

    def __init__(self, base_url: str, api_token: str, verify_ssl: bool = True):
        self.base_url = base_url.rstrip("/")
        self.session = requests.Session()
        self.session.headers.update({
            "Authorization": f"Bearer {api_token}",
            "Content-Type": "application/json",
            "Accept": "application/json",
        })
        self.session.verify = verify_ssl

    def get_sites(self):
        """Retrieve all monitored sites."""
        resp = self.session.get(f"{self.base_url}/api/v1/sites")
        resp.raise_for_status()
        return resp.json().get("sites", [])

    def get_assets(self, site_id: Optional[str] = None, asset_type: Optional[str] = None):
        """Retrieve discovered assets with optional filtering.

        asset_type: PLC, RTU, HMI, DCS, Engineering_Workstation,
                    Historian, Network_Device, IO_Module, Safety_Controller
        """
        params = {}
        if site_id:
            params["site_id"] = site_id
        if asset_type:
            params["type"] = asset_type

        resp = self.session.get(f"{self.base_url}/api/v1/assets", params=params)
        resp.raise_for_status()
        return resp.json().get("assets", [])

    def get_asset_detail(self, asset_id: str):
        """Retrieve detailed asset information including firmware, modules, and CVEs."""
        resp = self.session.get(f"{self.base_url}/api/v1/assets/{asset_id}")
        resp.raise_for_status()
        return resp.json()

    def get_communication_map(self, site_id: str):
        """Retrieve communication relationships between assets."""
        resp = self.session.get(
            f"{self.base_url}/api/v1/sites/{site_id}/communications"
        )
        resp.raise_for_status()
        return resp.json().get("communications", [])

    def get_vulnerabilities(self, site_id: Optional[str] = None, severity: str = "critical"):
        """Retrieve vulnerabilities for discovered assets."""
        params = {"min_severity": severity}
        if site_id:
            params["site_id"] = site_id

        resp = self.session.get(f"{self.base_url}/api/v1/vulnerabilities", params=params)
        resp.raise_for_status()
        return resp.json().get("vulnerabilities", [])

    def export_asset_inventory(self, output_file: str, site_id: Optional[str] = None):
        """Export full asset inventory to CSV for compliance reporting."""
        assets = self.get_assets(site_id=site_id)
        if not assets:
            print("[!] No assets found")
            return

        fieldnames = [
            "asset_id", "name", "type", "vendor", "model", "firmware_version",
            "ip_address", "mac_address", "serial_number", "purdue_level",
            "zone", "protocol", "first_seen", "last_seen", "risk_score",
            "cve_count", "site_name",
        ]

        with open(output_file, "w", newline="") as f:
            writer = csv.DictWriter(f, fieldnames=fieldnames)
            writer.writeheader()
            for asset in assets:
                writer.writerow({
                    "asset_id": asset.get("id", ""),
                    "name": asset.get("name", "Unknown"),
                    "type": asset.get("type", ""),
                    "vendor": asset.get("vendor", ""),
                    "model": asset.get("model", ""),
                    "firmware_version": asset.get("firmware_version", ""),
                    "ip_address": asset.get("ip_address", ""),
                    "mac_address": asset.get("mac_address", ""),
                    "serial_number": asset.get("serial_number", ""),
                    "purdue_level": asset.get("purdue_level", ""),
                    "zone": asset.get("zone", ""),
                    "protocol": ", ".join(asset.get("protocols", [])),
                    "first_seen": asset.get("first_seen", ""),
                    "last_seen": asset.get("last_seen", ""),
                    "risk_score": asset.get("risk_score", 0),
                    "cve_count": asset.get("cve_count", 0),
                    "site_name": asset.get("site_name", ""),
                })

        print(f"[+] Exported {len(assets)} assets to {output_file}")

    def generate_purdue_level_report(self, site_id: str):
        """Generate asset distribution report by Purdue Model level."""
        assets = self.get_assets(site_id=site_id)
        levels = {0: [], 1: [], 2: [], 3: [], 3.5: [], 4: [], 5: []}

        for asset in assets:
            level = asset.get("purdue_level", -1)
            if level in levels:
                levels[level].append(asset)

        print(f"\n{'='*65}")
        print("PURDUE MODEL ASSET DISTRIBUTION REPORT")
        print(f"{'='*65}")
        print(f"Site: {site_id}")
        print(f"Total Assets Discovered: {len(assets)}")
        print(f"Report Generated: {datetime.now().isoformat()}")
        print(f"{'-'*65}")

        level_names = {
            0: "Level 0 - Physical Process (Sensors/Actuators)",
            1: "Level 1 - Basic Control (PLCs/RTUs)",
            2: "Level 2 - Supervisory Control (HMI/SCADA)",
            3: "Level 3 - Site Operations (Historian/MES)",
            3.5: "Level 3.5 - IT/OT DMZ",
            4: "Level 4 - Enterprise IT",
            5: "Level 5 - Enterprise Network/Internet",
        }

        for level, name in level_names.items():
            device_list = levels.get(level, [])
            print(f"\n  {name}")
            print(f"    Count: {len(device_list)}")
            if device_list:
                vendors = set(a.get("vendor", "Unknown") for a in device_list)
                types = set(a.get("type", "Unknown") for a in device_list)
                print(f"    Vendors: {', '.join(vendors)}")
                print(f"    Types: {', '.join(types)}")
                high_risk = [a for a in device_list if a.get("risk_score", 0) >= 7]
                if high_risk:
                    print(f"    High-Risk Assets: {len(high_risk)}")
                    for a in high_risk[:5]:
                        print(f"      - {a['name']} (Risk: {a.get('risk_score')})")


if __name__ == "__main__":
    discovery = ClarotyAssetDiscovery(
        base_url="https://your-claroty-instance.claroty.cloud",
        api_token="your-api-token-here",
        verify_ssl=True,
    )

    print("[*] Fetching sites...")
    sites = discovery.get_sites()
    for site in sites:
        print(f"  Site: {site['name']} (ID: {site['id']})")

    if sites:
        site_id = sites[0]["id"]
        print(f"\n[*] Generating Purdue level report for {sites[0]['name']}...")
        discovery.generate_purdue_level_report(site_id)

        print(f"\n[*] Exporting asset inventory...")
        discovery.export_asset_inventory(
            f"asset_inventory_{datetime.now().strftime('%Y%m%d')}.csv",
            site_id=site_id,
        )

        print(f"\n[*] Checking critical vulnerabilities...")
        vulns = discovery.get_vulnerabilities(site_id=site_id, severity="critical")
        print(f"  Critical vulnerabilities: {len(vulns)}")
        for v in vulns[:10]:
            print(f"    - {v.get('cve_id')}: {v.get('description', '')[:80]}")

Step 2: Configure Active Discovery with Claroty Edge

Claroty Edge performs safe, targeted queries of OT devices using native industrial protocols (not IT scanning) to extract detailed asset information from devices that passive monitoring alone cannot fully identify.

# Claroty Edge Active Discovery Configuration
# Safe active queries using native industrial protocols

edge_configuration:
  deployment_mode: "on-premises"
  collection_schedule:
    frequency: "weekly"
    maintenance_window: "Sunday 02:00-06:00"
    max_concurrent_queries: 5

  protocol_queries:
    siemens_s7:
      enabled: true
      target_subnets: ["10.10.1.0/24", "10.10.2.0/24"]
      ports: [102]
      query_type: "SZL_read"
      information_collected:
        - "Module identification"
        - "Firmware version"
        - "Hardware configuration"
        - "Protection level"

    rockwell_cip:
      enabled: true
      target_subnets: ["10.10.3.0/24"]
      ports: [44818]
      query_type: "CIP_identity"
      information_collected:
        - "Product name and revision"
        - "Serial number"
        - "Device type"
        - "Vendor ID"

    modbus:
      enabled: true
      target_subnets: ["10.10.4.0/24"]
      ports: [502]
      query_type: "read_device_identification"
      function_code: 43
      information_collected:
        - "Vendor name"
        - "Product code"
        - "Firmware revision"

    bacnet:
      enabled: true
      target_subnets: ["10.10.5.0/24"]
      ports: [47808]
      query_type: "who_is"
      information_collected:
        - "Device name"
        - "Vendor identifier"
        - "Model name"
        - "Application software version"

  safety_controls:
    excluded_subnets: ["10.10.100.0/24"]  # SIS network - never active scan
    rate_limiting: true
    max_packets_per_second: 10
    timeout_seconds: 5
    retry_count: 1
    abort_on_device_error: true

Step 3: Validate and Enrich Asset Data

Cross-reference discovered assets against known inventories and enrich with vulnerability data.

#!/usr/bin/env python3
"""Asset Validation and Enrichment Tool.

Cross-references Claroty discovery results against existing CMDB
and enriches with NVD vulnerability data.
"""

import json
import csv
import sys
from datetime import datetime

try:
    import requests
except ImportError:
    print("Install requests: pip install requests")
    sys.exit(1)


class AssetValidator:
    """Validates and enriches OT asset inventory."""

    def __init__(self, inventory_file: str):
        self.discovered_assets = []
        self.load_inventory(inventory_file)
        self.discrepancies = []

    def load_inventory(self, filepath: str):
        """Load Claroty-discovered asset inventory."""
        with open(filepath, "r") as f:
            reader = csv.DictReader(f)
            self.discovered_assets = list(reader)
        print(f"[*] Loaded {len(self.discovered_assets)} discovered assets")

    def compare_with_cmdb(self, cmdb_file: str):
        """Compare discovered assets against CMDB records."""
        with open(cmdb_file, "r") as f:
            cmdb_assets = {row["ip_address"]: row for row in csv.DictReader(f)}

        discovered_ips = {a["ip_address"] for a in self.discovered_assets if a["ip_address"]}
        cmdb_ips = set(cmdb_assets.keys())

        shadow_devices = discovered_ips - cmdb_ips
        missing_devices = cmdb_ips - discovered_ips

        print(f"\n{'='*60}")
        print("ASSET INVENTORY VALIDATION REPORT")
        print(f"{'='*60}")
        print(f"Discovered assets: {len(discovered_ips)}")
        print(f"CMDB records: {len(cmdb_ips)}")
        print(f"Shadow OT devices (not in CMDB): {len(shadow_devices)}")
        print(f"Missing devices (in CMDB, not seen): {len(missing_devices)}")

        if shadow_devices:
            print(f"\n  SHADOW DEVICES (Unauthorized/Undocumented):")
            for ip in sorted(shadow_devices):
                asset = next((a for a in self.discovered_assets if a["ip_address"] == ip), {})
                print(f"    - {ip} | {asset.get('vendor', 'Unknown')} {asset.get('model', '')} | Type: {asset.get('type', 'Unknown')}")
                self.discrepancies.append({
                    "type": "SHADOW_DEVICE",
                    "severity": "HIGH",
                    "ip": ip,
                    "detail": f"Undocumented {asset.get('type', 'device')} from {asset.get('vendor', 'unknown vendor')}",
                })

        if missing_devices:
            print(f"\n  MISSING DEVICES (Expected but not seen):")
            for ip in sorted(missing_devices):
                cmdb = cmdb_assets[ip]
                print(f"    - {ip} | {cmdb.get('name', 'Unknown')} | Last CMDB update: {cmdb.get('last_updated', 'N/A')}")
                self.discrepancies.append({
                    "type": "MISSING_DEVICE",
                    "severity": "MEDIUM",
                    "ip": ip,
                    "detail": f"CMDB asset {cmdb.get('name', ip)} not seen on network",
                })

    def check_firmware_vulnerabilities(self, asset):
        """Check NVD for known vulnerabilities matching asset firmware."""
        vendor = asset.get("vendor", "").lower()
        model = asset.get("model", "").lower()
        firmware = asset.get("firmware_version", "")

        if not vendor or not model:
            return []

        search_term = f"{vendor} {model}"
        try:
            resp = requests.get(
                "https://services.nvd.nist.gov/rest/json/cves/2.0",
                params={"keywordSearch": search_term, "resultsPerPage": 10},
                timeout=15,
            )
            if resp.status_code == 200:
                data = resp.json()
                return data.get("vulnerabilities", [])
        except requests.RequestException:
            pass
        return []

    def generate_risk_summary(self):
        """Generate risk-prioritized summary of findings."""
        print(f"\n{'='*60}")
        print("RISK SUMMARY")
        print(f"{'='*60}")

        high_risk = [a for a in self.discovered_assets if float(a.get("risk_score", 0)) >= 7]
        end_of_life = [a for a in self.discovered_assets if a.get("firmware_version", "").startswith("v1.")]
        no_encryption = [a for a in self.discovered_assets if "modbus" in a.get("protocol", "").lower()]

        print(f"  High-risk assets (score >= 7): {len(high_risk)}")
        print(f"  Potentially end-of-life firmware: {len(end_of_life)}")
        print(f"  Assets using unencrypted protocols: {len(no_encryption)}")
        print(f"  Inventory discrepancies: {len(self.discrepancies)}")


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python validate_assets.py <claroty_export.csv> [cmdb_export.csv]")
        sys.exit(1)

    validator = AssetValidator(sys.argv[1])
    if len(sys.argv) >= 3:
        validator.compare_with_cmdb(sys.argv[2])
    validator.generate_risk_summary()

Key Concepts

TermDefinition
Passive MonitoringObserving mirrored network traffic via SPAN/TAP without injecting packets, safe for all OT devices
Active QueryingSending native protocol requests to extract detailed device information; requires careful scheduling
Claroty EdgeClaroty's safe active discovery collector that uses native industrial protocols rather than IT scanning
Purdue LevelHierarchical classification of industrial network assets from Level 0 (physical process) to Level 5 (enterprise)
Shadow OT DeviceAsset connected to the OT network that is not documented in the asset management system
xDomeClaroty's SaaS-based cyber-physical systems protection platform providing visibility, risk management, and threat detection

Common Scenarios

Scenario: Brownfield Factory Asset Discovery

Context: A manufacturing plant with 20 years of equipment additions needs a complete OT asset inventory for an IEC 62443 risk assessment. No accurate asset records exist.

Approach:

  1. Deploy Claroty sensors on SPAN ports at each major network segment (control, supervisory, DMZ)
  2. Allow passive monitoring for 2-4 weeks to capture all regular communication patterns
  3. Schedule Claroty Edge active queries during a planned maintenance window
  4. Export discovered inventory and categorize assets by Purdue level, vendor, and criticality
  5. Cross-reference against any existing documentation (P&ID diagrams, network drawings)
  6. Identify shadow devices and initiate a review process with plant operations
  7. Feed validated inventory into IEC 62443 zone and conduit risk assessment

Pitfalls: Do not rush active discovery before passive monitoring has captured baseline traffic patterns. Never use IT vulnerability scanners (Nessus active scans) directly against PLCs or RTUs -- this can crash legacy controllers. Always exclude Safety Instrumented Systems (SIS) from active queries.

Output Format

ICS ASSET DISCOVERY REPORT
============================
Date: YYYY-MM-DD
Platform: Claroty xDome
Site: [Site Name]

DISCOVERY SUMMARY:
  Total Assets Discovered: [count]
  New Assets (not in CMDB): [count]
  High-Risk Assets: [count]

PURDUE LEVEL DISTRIBUTION:
  Level 0 (Process): [count] assets
  Level 1 (Control): [count] assets
  Level 2 (Supervisory): [count] assets
  Level 3 (Operations): [count] assets
  Level 3.5 (DMZ): [count] assets
  Level 4-5 (Enterprise): [count] assets

TOP VENDORS:
  1. [Vendor] - [count] devices
  2. [Vendor] - [count] devices

CRITICAL FINDINGS:
  - [Shadow device description]
  - [End-of-life firmware finding]
  - [Unencrypted protocol concern]
how to use performing-ics-asset-discovery-with-claroty

How to use performing-ics-asset-discovery-with-claroty on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performing-ics-asset-discovery-with-claroty
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-ics-asset-discovery-with-claroty

The skills CLI fetches performing-ics-asset-discovery-with-claroty from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performing-ics-asset-discovery-with-claroty

Reload or restart Cursor to activate performing-ics-asset-discovery-with-claroty. Access the skill through slash commands (e.g., /performing-ics-asset-discovery-with-claroty) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

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Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.766 reviews
  • Advait Agarwal· Dec 28, 2024

    Registry listing for performing-ics-asset-discovery-with-claroty matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kofi Yang· Dec 28, 2024

    Useful defaults in performing-ics-asset-discovery-with-claroty — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Naina Garcia· Dec 28, 2024

    performing-ics-asset-discovery-with-claroty has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Alexander Choi· Dec 24, 2024

    performing-ics-asset-discovery-with-claroty fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Dec 16, 2024

    I recommend performing-ics-asset-discovery-with-claroty for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Michael Menon· Dec 16, 2024

    I recommend performing-ics-asset-discovery-with-claroty for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Aditi Torres· Dec 4, 2024

    Keeps context tight: performing-ics-asset-discovery-with-claroty is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ava Mensah· Nov 19, 2024

    performing-ics-asset-discovery-with-claroty reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kofi Chen· Nov 19, 2024

    We added performing-ics-asset-discovery-with-claroty from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Harper Khanna· Nov 19, 2024

    performing-ics-asset-discovery-with-claroty is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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