Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add autohandai/community-skills --skill building-cloud-siem-with-sentinelgit clone --depth 1 https://github.com/autohandai/community-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/autohandai/community-skills/building-cloud-siem-with-sentinel)<a href="https://agentmods.dev/skills/autohandai/community-skills/building-cloud-siem-with-sentinel"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/building-cloud-siem-with-sentinel/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/autohandai/community-skills/building-cloud-siem-with-sentinel"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/building-cloud-siem-with-sentinel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.02898 |
| Opus 5 | $0.00039 | $0.01449 |
| Sonnet 5 | $0.00016 | $0.00580 |
| Haiku 4.5 | $0.00008 | $0.00290 |
Grade A, and why
building-cloud-siem-with-sentinel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to building-cloud-siem-with-sentinel — 42 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building Cloud SIEM with Sentinel
When to Use
- When establishing a centralized security operations center for multi-cloud environments
- When migrating from legacy SIEM platforms (Splunk, QRadar) to cloud-native architecture
- When building automated incident response workflows for cloud-specific threats
- When performing large-scale threat hunting across petabytes of security telemetry
- When integrating threat intelligence feeds with cloud security log analysis
Do not use for AWS-only environments where Security Hub and GuardDuty suffice, for endpoint detection requiring EDR capabilities (use Defender for Endpoint), or for compliance posture monitoring (see building-cloud-security-posture-management).
Prerequisites
- Azure subscription with Microsoft Sentinel enabled on a Log Analytics workspace
- Data connector permissions for target log sources (AWS CloudTrail, Azure Activity, GCP)
- Logic Apps or Azure Functions for automated response playbooks
- KQL (Kusto Query Language) proficiency for writing detection rules and hunting queries
Workflow
Step 1: Provision Sentinel Workspace and Data Connectors
Create a Log Analytics workspace optimized for security data and enable data connectors for multi-cloud ingestion.
# Create Log Analytics workspace
az monitor log-analytics workspace create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--location eastus \
--retention-time 365 \
--sku PerGB2018
# Enable Microsoft Sentinel on the workspace
az sentinel onboarding-state create \
--resource-group security-rg \
--workspace-name sentinel-workspace
# Enable AWS CloudTrail connector
az sentinel data-connector create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--data-connector-id aws-cloudtrail \
--kind AmazonWebServicesCloudTrail \
--aws-cloud-trail-data-connector '{
"awsRoleArn": "arn:aws:iam::123456789012:role/SentinelCloudTrailRole",
"dataTypes": {"logs": {"state": "Enabled"}}
}'
# Enable Azure AD sign-in and audit logs
az sentinel data-connector create \
--resource-group security-rg \
--workspace-name sentinel-workspace \
--data-connector-id azure-ad \
--kind AzureActiveDirectory \
--azure-active-directory '{
"dataTypes": {
"alerts": {"state": "Enabled"},
"signinLogs": {"state": "Enabled"},
"auditLogs": {"state": "Enabled"}
}
}'
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 300 lines · 78 tokens per session scan A ba98aba831f8
building-cloud-siem-with-sentinel is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 2,898 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to building-cloud-siem-with-sentinel, differing in 42 lines, and is treated as a copy.
Other skills, from other repositories
building-cloud-siem-with-sentinel
This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake…
building-cloud-siem-with-sentinel
This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake…
building-cloud-siem-with-sentinel
This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake…
building-cloud-siem-with-sentinel
Deploy Microsoft Sentinel as a cloud-native SIEM/SOAR by configuring multi-cloud data connectors (AWS, Azure, GCP), writing KQL detection and hunting queries, and building automated Logic Apps response playbooks. Use when establishing a centralized SOC for multi-cloud environments, migrating from a legacy SIEM, or…
building-cloud-siem-with-sentinel
This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake…
building-cloud-siem-with-sentinel
This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake…