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Asset Inventory

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BagelHole
asset-inventory

Maintain IT asset inventory and configuration management database. Track hardware, software, and cloud resources. Use when managing IT assets.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill nameasset-inventory
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Asset Inventory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/compliance/governance/asset-inventory
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/compliance/governance/asset-inventory .claude/skills/asset-inventory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Asset Inventory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Asset Inventory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Asset Inventory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Asset Inventory

Maintain comprehensive IT asset inventory using automated discovery, AWS Config rules, cloud asset discovery scripts, CMDB integration, and tagging enforcement for compliance and operational visibility.

When to Use

  • Building or maintaining an IT asset inventory for compliance frameworks (ISO 27001, SOC 2, FedRAMP)
  • Implementing automated cloud resource discovery across accounts and regions
  • Enforcing tagging standards for cost allocation, ownership, and data classification
  • Integrating asset data with a CMDB for operational workflows
  • Preparing for audits that require a complete system component inventory

Asset Categories and Schema

yaml
asset_categories:
  compute:
    cloud:
      - EC2 instances / Azure VMs / GCE instances
      - Lambda functions / Azure Functions / Cloud Functions
      - ECS/EKS clusters and tasks
      - Container images in registries
    on_premise:
      - Physical servers
      - Virtual machines (VMware, Hyper-V)

  storage:
    - S3 buckets / Azure Storage / GCS buckets
    - EBS volumes / Managed Disks / Persistent Disks
    - RDS instances / Azure SQL / Cloud SQL
    - DynamoDB tables / Cosmos DB / Firestore
    - EFS / Azure Files / Filestore

  network:
    - VPCs / VNets / VPC Networks
    - Load balancers (ALB, NLB, Azure LB, GCP LB)
    - DNS zones and records
    - VPN gateways and connections
    - CDN distributions

  security:
    - IAM users, roles, and policies
    - KMS keys / Key Vault keys
    - Certificates (ACM, Key Vault, Certificate Manager)
    - Security groups / NSGs / Firewall rules
    - WAF configurations

  applications:
    - SaaS subscriptions
    - Licensed software
    - Custom applications
    - APIs and integrations

  endpoints:
    - Laptops and desktops
    - Mobile devices
    - Printers and peripherals

asset_record_schema:
  required_fields:
    asset_id: "Unique identifier (auto-generated)"
    name: "Human-readable name"
    type: "Category from above taxonomy"
    provider: "AWS / Azure / GCP / On-Premise / SaaS"
    account_or_subscription: "Cloud account ID"
    region: "Deployment region/location"
    owner: "Team or individual responsible"
    data_classification: "Public / Internal / Confidential / Restricted"
    environment: "Production / Staging / Development / Sandbox"
    status: "Active / Decommissioning / Retired"
    created_date: "When the asset was provisioned"
    last_seen: "Last automated discovery timestamp"

  optional_fields:
    cost_center: "For cost allocation"
    compliance_scope: "SOC2 / HIPAA / PCI / None"
    backup_policy: "Backup schedule reference"
    dr_tier: "Critical / Essential / Standard / Non-essential"
    expiration_date: "For time-limited resources"
    tags: "Key-value pairs from cloud provider"
    dependencies: "Upstream and downstream services"

AWS Resource Discovery Script

bash
#!/usr/bin/env bash
# aws-asset-discovery.sh - Discover and inventory all AWS resources

OUTPUT_DIR="./asset-inventory/aws/$(date +%Y-%m-%d)"
mkdir -p "$OUTPUT_DIR"
ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)

echo "=== AWS Asset Discovery for Account $ACCOUNT_ID ==="

# EC2 Instances
echo "--- EC2 Instances ---"
aws ec2 describe-instances \
  --query 'Reservations[*].Instances[*].{
    InstanceId:InstanceId,
    Type:InstanceType,
    State:State.Name,
    AZ:Placement.AvailabilityZone,
    VpcId:VpcId,
    PrivateIP:PrivateIpAddress,
    PublicIP:PublicIpAddress,
    LaunchTime:LaunchTime,
    Name:Tags[?Key==`Name`].Value|[0],
    Owner:Tags[?Key==`Owner`].Value|[0],
    Environment:Tags[?Key==`Environment`].Value|[0]
  }' --output json | jq 'flatten' > "$OUTPUT_DIR/ec2-instances.json"

# RDS Databases
echo "--- RDS Instances ---"
aws rds describe-db-instances \
  --query 'DBInstances[*].{
    DBInstanceId:DBInstanceIdentifier,
    Engine:Engine,
    EngineVersion:EngineVersion,
    Class:DBInstanceClass,
    Status:DBInstanceStatus,
    MultiAZ:MultiAZ,
    Encrypted:StorageEncrypted,
    Endpoint:Endpoint.Address,
    BackupRetention:BackupRetentionPeriod
  }' --output json > "$OUTPUT_DIR/rds-instances.json"

# S3 Buckets
echo "--- S3 Buckets ---"
aws s3api list-buckets --query 'Buckets[*].{Name:Name,Created:CreationDate}' --output json | \
  jq -c '.[]' | while read -r bucket; do
    name=$(echo "$bucket" | jq -r '.Name')
    region=$(aws s3api get-bucket-location --bucket "$name" --query 'LocationConstraint' --output text 2>/dev/null)
    encryption=$(aws s3api get-bucket-encryption --bucket "$name" 2>/dev/null | jq -r '.ServerSideEncryptionConfiguration.Rules[0].ApplyServerSideEncryptionByDefault.SSEAlgorithm' 2>/dev/null)
    versioning=$(aws s3api get-bucket-versioning --bucket "$name" --query 'Status' --output text 2>/dev/null)
    echo "{\"Name\":\"$name\",\"Region\":\"${region:-us-east-1}\",\"Encryption\":\"${encryption:-none}\",\"Versioning\":\"${versioning:-Disabled}\"}"
  done | jq -s '.' > "$OUTPUT_DIR/s3-buckets.json"

# Lambda Functions
echo "--- Lambda Functions ---"
aws lambda list-functions \
  --query 'Functions[*].{
    Name:FunctionName,
    Runtime:Runtime,
    MemorySize:MemorySize,
    Timeout:Timeout,
    LastModified:LastModified,
    CodeSize:CodeSize
  }' --output json > "$OUTPUT_DIR/lambda-functions.json"

# VPCs and Security Groups
echo "--- VPCs ---"
aws ec2 describe-vpcs \
  --query 'Vpcs[*].{
    VpcId:VpcId,
    CidrBlock:CidrBlock,
    State:State,
    IsDefault:IsDefault,
    Name:Tags[?Key==`Name`].Value|[0]
  }' --output json > "$OUTPUT_DIR/vpcs.json"

echo "--- Security Groups ---"
aws ec2 describe-security-groups \
  --query 'SecurityGroups[*].{
    GroupId:GroupId,
    GroupName:GroupName,
    VpcId:VpcId,
    Description:Description,
    IngressRuleCount:length(IpPermissions),
    EgressRuleCount:length(IpPermissionsEgress)
  }' --output json > "$OUTPUT_DIR/security-groups.json"

# IAM Users and Roles
echo "--- IAM Users ---"
aws iam list-users \
  --query 'Users[*].{UserName:UserName,Created:CreateDate,PasswordLastUsed:PasswordLastUsed}' \
  --output json > "$OUTPUT_DIR/iam-users.json"

echo "--- IAM Roles ---"
aws iam list-roles \
  --query 'Roles[*].{RoleName:RoleName,Created:CreateDate,LastUsed:RoleLastUsed.LastUsedDate}' \
  --output json > "$OUTPUT_DIR/iam-roles.json"

# EKS Clusters
echo "--- EKS Clusters ---"
aws eks list-clusters --query 'clusters' --output json | jq -r '.[]' | while read -r cluster; do
  aws eks describe-cluster --name "$cluster" \
    --query 'cluster.{Name:name,Version:version,Status:status,Endpoint:endpoint,Created:createdAt}'
done | jq -s '.' > "$OUTPUT_DIR/eks-clusters.json" 2>/dev/null

# KMS Keys
echo "--- KMS Keys ---"
aws kms list-keys --query 'Keys[*].KeyId' --output text | tr '\t' '\n' | while read -r key_id; do
  aws kms describe-key --key-id "$key_id" \
    --query 'KeyMetadata.{KeyId:KeyId,Description:Description,State:KeyState,Created:CreationDate,Manager:KeyManager}' 2>/dev/null
done | jq -s '.' > "$OUTPUT_DIR/kms-keys.json"

# Generate summary
echo "=== Inventory Summary ==="
echo "EC2 Instances: $(jq 'length' "$OUTPUT_DIR/ec2-instances.json")"
echo "RDS Instances: $(jq 'length' "$OUTPUT_DIR/rds-instances.json")"
echo "S3 Buckets: $(jq 'length' "$OUTPUT_DIR/s3-buckets.json")"
echo "Lambda Functions: $(jq 'length' "$OUTPUT_DIR/lambda-functions.json")"
echo "VPCs: $(jq 'length' "$OUTPUT_DIR/vpcs.json")"
echo "Security Groups: $(jq 'length' "$OUTPUT_DIR/security-groups.json")"
echo "IAM Users: $(jq 'length' "$OUTPUT_DIR/iam-users.json")"
echo "IAM Roles: $(jq 'length' "$OUTPUT_DIR/iam-roles.json")"

echo "Inventory saved to $OUTPUT_DIR"

AWS Config Rules for Inventory Compliance

bash
# Enable AWS Config recorder
aws configservice put-configuration-recorder \
  --configuration-recorder name=default,roleARN=arn:aws:iam::123456789012:role/aws-config-role \
  --recording-group allSupported=true,includeGlobalResourceTypes=true

# Start recording
aws configservice start-configuration-recorder --configuration-recorder-name default

# Enable required-tags Config rule
aws configservice put-config-rule --config-rule '{
  "ConfigRuleName": "required-tags",
  "Source": {
    "Owner": "AWS",
    "SourceIdentifier": "REQUIRED_TAGS"
  },
  "InputParameters": "{\"tag1Key\":\"Owner\",\"tag2Key\":\"Environment\",\"tag3Key\":\"CostCenter\",\"tag4Key\":\"DataClassification\"}",
  "Scope": {
    "ComplianceResourceTypes": [
      "AWS::EC2::Instance",
      "AWS::RDS::DBInstance",
      "AWS::S3::Bucket",
      "AWS::Lambda::Function"
    ]
  }
}'

# Config rule for encryption compliance
aws configservice put-config-rule --config-rule '{
  "ConfigRuleName": "encrypted-volumes",
  "Source": {
    "Owner": "AWS",
    "SourceIdentifier": "ENCRYPTED_VOLUMES"
  }
}'

aws configservice put-config-rule --config-rule '{
  "ConfigRuleName": "rds-storage-encrypted",
  "Source": {
    "Owner": "AWS",
    "SourceIdentifier": "RDS_STORAGE_ENCRYPTED"
  }
}'

aws configservice put-config-rule --config-rule '{
  "ConfigRuleName": "s3-bucket-server-side-encryption-enabled",
  "Source": {
    "Owner": "AWS",
    "SourceIdentifier": "S3_BUCKET_SERVER_SIDE_ENCRYPTION_ENABLED"
  }
}'

# Query AWS Config for all resources of a type
aws configservice list-discovered-resources --resource-type AWS::EC2::Instance
aws configservice list-discovered-resources --resource-type AWS::RDS::DBInstance

# Advanced query with AWS Config SQL
aws configservice select-resource-config \
  --expression "SELECT resourceId, resourceType, tags, configuration.instanceType
                WHERE resourceType = 'AWS::EC2::Instance'
                AND tags.tag('Environment') = 'production'"

# Get compliance summary
aws configservice get-compliance-summary-by-config-rule
aws configservice get-compliance-summary-by-resource-type

Tagging Enforcement

yaml
# AWS Tag Policy (applied via AWS Organizations)
tag_policy:
  tags:
    Owner:
      tag_key:
        "@@assign": "Owner"
      enforced_for:
        "@@assign":
          - "ec2:instance"
          - "rds:db"
          - "s3:bucket"
          - "lambda:function"

    Environment:
      tag_key:
        "@@assign": "Environment"
      tag_value:
        "@@assign":
          - "production"
          - "staging"
          - "development"
          - "sandbox"

    DataClassification:
      tag_key:
        "@@assign": "DataClassification"
      tag_value:
        "@@assign":
          - "public"
          - "internal"
          - "confidential"
          - "restricted"

    CostCenter:
      tag_key:
        "@@assign": "CostCenter"
hcl
# Terraform - Enforce tags on all resources with default_tags
provider "aws" {
  region = "us-east-1"

  default_tags {
    tags = {
      ManagedBy          = "terraform"
      Environment        = var.environment
      Owner              = var.team_name
      CostCenter         = var.cost_center
      DataClassification = var.data_classification
    }
  }
}

Multi-Cloud Discovery

bash
#!/usr/bin/env bash
# multi-cloud-discovery.sh - Discover assets across AWS, Azure, and GCP

OUTPUT_DIR="./asset-inventory/multi-cloud/$(date +%Y-%m-%d)"
mkdir -p "$OUTPUT_DIR"

echo "=== Multi-Cloud Asset Discovery ==="

# AWS - using Resource Groups Tagging API
echo "--- AWS Resources ---"
aws resourcegroupstaggingapi get-resources \
  --query 'ResourceTagMappingList[*].{ARN:ResourceARN,Tags:Tags}' \
  --output json > "$OUTPUT_DIR/aws-all-resources.json"
echo "AWS resources: $(jq 'length' "$OUTPUT_DIR/aws-all-resources.json")"

# Azure - using Resource Graph
echo "--- Azure Resources ---"
az graph query -q "Resources | project name, type, location, resourceGroup, subscriptionId, tags" \
  --output json > "$OUTPUT_DIR/azure-all-resources.json" 2>/dev/null

# GCP - using Cloud Asset Inventory
echo "--- GCP Resources ---"
gcloud asset search-all-resources \
  --scope="organizations/ORG_ID" \
  --format=json > "$OUTPUT_DIR/gcp-all-resources.json" 2>/dev/null

# Find untagged resources
echo "=== Untagged Resources ==="
jq '[.[] | select(.Tags == null or .Tags == [])] | length' "$OUTPUT_DIR/aws-all-resources.json"

echo "Discovery complete. Results in $OUTPUT_DIR"

CMDB Integration

python
"""
CMDB sync script - Normalize cloud assets and push to CMDB API.
"""
import json
import requests
from datetime import datetime, timezone


class CMDBSync:
    def __init__(self, cmdb_url, api_token):
        self.cmdb_url = cmdb_url
        self.headers = {
            "Authorization": f"Bearer {api_token}",
            "Content-Type": "application/json",
        }

    def normalize_aws_instance(self, instance):
        """Convert AWS EC2 instance to common asset schema."""
        tags = {t["Key"]: t["Value"] for t in (instance.get("Tags") or [])}
        return {
            "asset_id": f"aws:{instance['InstanceId']}",
            "name": tags.get("Name", instance["InstanceId"]),
            "type": "compute",
            "provider": "aws",
            "region": instance.get("AZ", "unknown")[:-1],
            "configuration": {
                "instance_type": instance.get("Type"),
                "state": instance.get("State"),
                "vpc_id": instance.get("VpcId"),
            },
            "owner": tags.get("Owner", "unassigned"),
            "environment": tags.get("Environment", "unknown"),
            "data_classification": tags.get("DataClassification", "unknown"),
            "status": "active" if instance.get("State") == "running" else "stopped",
            "last_seen": datetime.now(timezone.utc).isoformat(),
        }

    def sync_assets(self, assets):
        """Push normalized assets to CMDB."""
        results = {"created": 0, "updated": 0, "errors": 0}
        for asset in assets:
            try:
                resp = requests.get(
                    f"{self.cmdb_url}/assets/{asset['asset_id']}",
                    headers=self.headers,
                )
                if resp.status_code == 200:
                    requests.put(
                        f"{self.cmdb_url}/assets/{asset['asset_id']}",
                        headers=self.headers,
                        json=asset,
                    )
                    results["updated"] += 1
                else:
                    requests.post(
                        f"{self.cmdb_url}/assets",
                        headers=self.headers,
                        json=asset,
                    )
                    results["created"] += 1
            except Exception:
                results["errors"] += 1
        return results

Asset Inventory Checklist

yaml
asset_inventory_checklist:
  discovery:
    - [ ] Automated discovery scripts running for all cloud accounts
    - [ ] Discovery covers all resource types (compute, storage, network, IAM)
    - [ ] Multi-region discovery enabled
    - [ ] On-premise assets cataloged
    - [ ] SaaS subscriptions inventoried
    - [ ] Discovery runs daily (minimum weekly)

  classification:
    - [ ] Required tags defined (Owner, Environment, DataClassification, CostCenter)
    - [ ] Tag enforcement via AWS Organizations tag policies
    - [ ] Tag enforcement via Terraform default_tags
    - [ ] Tag enforcement via CI/CD policy checks (Checkov, OPA)
    - [ ] Untagged resource reports generated and tracked

  configuration_management:
    - [ ] AWS Config enabled in all regions
    - [ ] Config rules enforce encryption, tagging, and security baselines
    - [ ] Configuration compliance summary reviewed weekly
    - [ ] Drift detection enabled for IaC-managed resources

  cmdb:
    - [ ] CMDB sync automated from cloud discovery
    - [ ] Common schema defined across all providers
    - [ ] Reconciliation process identifies orphaned records
    - [ ] New resources auto-assigned default owner
    - [ ] Asset lifecycle tracked (created, active, decommissioning, retired)

  governance:
    - [ ] Asset owners assigned and current
    - [ ] Quarterly inventory reconciliation conducted
    - [ ] Compliance scope tagging accurate (SOC2, HIPAA, PCI)
    - [ ] Asset inventory available for auditor review
    - [ ] Decommissioned assets tracked for data retention compliance

Best Practices

  • Automate discovery rather than relying on manual inventory: cloud environments change too fast for spreadsheets
  • Use AWS Config, Azure Resource Graph, and GCP Cloud Asset Inventory as authoritative data sources
  • Enforce tagging at provisioning time through IaC defaults and policy-as-code guardrails
  • Assign every asset an owner: unowned resources become security and cost liabilities
  • Reconcile inventory regularly and investigate orphaned assets (CMDB record with no real resource and vice versa)
  • Track data classification as a mandatory tag to support compliance scoping decisions
  • Maintain asset lifecycle states to distinguish active resources from those being decommissioned
  • Integrate asset inventory with incident response to quickly identify affected systems during investigations
  • Export inventory data for compliance audits in accessible formats (CSV, JSON)
  • Review untagged and unclassified resource reports weekly to maintain inventory quality

Frequently asked questions

What does the Asset Inventory AI skill do?

Maintain IT asset inventory and configuration management database. Track hardware, software, and cloud resources. Use when managing IT assets.

Why use Asset Inventory on TypingMind?

Because you install it once and use it with any model. Asset Inventory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Asset Inventory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/compliance/governance/asset-inventory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Asset Inventory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Asset Inventory?

As many as you like. As long as a model supports skills, you can use Asset Inventory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Asset Inventory AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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