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 adriannoes/awesome-agentic-ai --skill analyzing-azure-activity-logs-for-threatsgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/analyzing-azure-activity-logs-for-threats)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/analyzing-azure-activity-logs-for-threats"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/analyzing-azure-activity-logs-for-threats/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/adriannoes/awesome-agentic-ai/analyzing-azure-activity-logs-for-threats"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/analyzing-azure-activity-logs-for-threats.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 | $0.00068 | $0.00546 |
| Opus 5 | $0.00034 | $0.00273 |
| Sonnet 5 | $0.00014 | $0.00109 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
analyzing-azure-activity-logs-for-threats 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 13d 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
100% identical to analyzing-azure-activity-logs-for-threats — 0 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.
What it actually says
Analyzing Azure Activity Logs for Threats
When to Use
- When investigating security incidents that require analyzing azure activity logs for threats
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
Use azure-monitor-query to execute KQL queries against Azure Log Analytics workspaces, detecting suspicious admin operations and sign-in anomalies.
from azure.identity import DefaultAzureCredential
from azure.monitor.query import LogsQueryClient
from datetime import timedelta
credential = DefaultAzureCredential()
client = LogsQueryClient(credential)
response = client.query_workspace(
workspace_id="WORKSPACE_ID",
query="AzureActivity | where OperationNameValue has 'MICROSOFT.AUTHORIZATION/ROLEASSIGNMENTS/WRITE' | take 10",
timespan=timedelta(hours=24),
)
Key detection queries:
- Role assignment changes (privilege escalation)
- Resource group and subscription modifications
- Key vault secret access from new IPs
- Network security group rule changes
- Conditional access policy modifications
Examples
# Detect new Global Admin role assignments
query = '''
AuditLogs
| where OperationName == "Add member to role"
| where TargetResources[0].modifiedProperties[0].newValue has "Global Administrator"
'''
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.
- 13d ago First seen · 88 lines · 68 tokens per session scan A f4b3c4570a86
analyzing-azure-activity-logs-for-threats is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 68 tokens to every session and 546 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyzing-azure-activity-logs-for-threats, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-azure-activity-logs-for-threats
Use when queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity…
analyzing-azure-activity-logs-for-threats
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or…
analyzing-azure-activity-logs-for-threats
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or…
analyzing-azure-activity-logs-for-threats
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or…
analyzing-azure-activity-logs-for-threats
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or…
detecting-azure-lateral-movement
Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.