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 Youngmaidainon/Agent-Level-Up --skill building-detection-rules-with-sigmagit clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-UpWrote 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/youngmaidainon/agent-level-up/building-detection-rules-with-sigma)<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/building-detection-rules-with-sigma"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/building-detection-rules-with-sigma/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/youngmaidainon/agent-level-up/building-detection-rules-with-sigma"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/building-detection-rules-with-sigma.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 | $0.00077 | $0.02653 |
| Opus 5 | $0.00039 | $0.01326 |
| Sonnet 5 | $0.00015 | $0.00531 |
| Haiku 4.5 | $0.00008 | $0.00265 |
Grade A, and why
building-detection-rules-with-sigma 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 6d 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 building-detection-rules-with-sigma — 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.
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building Detection Rules with Sigma
When to Use
Use this skill when:
- SOC engineers need to create detection rules portable across multiple SIEM platforms
- Threat intelligence reports describe TTPs requiring new detection coverage
- Existing vendor-specific rules need standardization into a shareable format
- The team adopts Sigma as a detection-as-code standard in CI/CD pipelines
Do not use for real-time streaming detection (Sigma is for batch/scheduled searches) or when the target SIEM has native detection features that Sigma cannot express (e.g., Splunk RBA risk scoring).
Prerequisites
- Python 3.8+ with
pySigmaand appropriate backend (pySigma-backend-splunk,pySigma-backend-elasticsearch,pySigma-backend-microsoft365defender) - Sigma rule repository cloned:
git clone https://github.com/SigmaHQ/sigma.git - MITRE ATT&CK framework knowledge for technique mapping
- Understanding of target SIEM log source field mappings
Workflow
Step 1: Define Detection Logic from Threat Intelligence
Start with a threat report or ATT&CK technique. Example: detecting Mimikatz credential dumping (T1003.001 — LSASS Memory):
title: Mimikatz Credential Dumping via LSASS Access
id: 0d894093-71bc-43c3-8d63-bf520e73a7c5
status: stable
level: high
description: Detects process accessing lsass.exe memory, indicative of credential dumping tools like Mimikatz
references:
- https://attack.mitre.org/techniques/T1003/001/
- https://github.com/gentilkiwi/mimikatz
author: mahipal
date: 2024/03/15
modified: 2024/03/15
tags:
- attack.credential_access
- attack.t1003.001
logsource:
category: process_access
product: windows
detection:
selection:
TargetImage|endswith: '\lsass.exe'
GrantedAccess|contains:
- '0x1010'
- '0x1038'
- '0x1fffff'
- '0x40'
filter_main_svchost:
SourceImage|endswith: '\svchost.exe'
filter_main_csrss:
SourceImage|endswith: '\csrss.exe'
filter_main_wininit:
SourceImage|endswith: '\wininit.exe'
condition: selection and not 1 of filter_main_*
falsepositives:
- Legitimate security tools accessing LSASS
- Windows Defender scanning
- CrowdStrike Falcon sensor
What ships with it
2 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.
- 6d ago First seen · 339 lines · 77 tokens per session scan A 37f59e2eb077
building-detection-rules-with-sigma is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 15d ago), licensed MIT. It adds 77 tokens to every session and 2,653 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to building-detection-rules-with-sigma, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
building-detection-rules-with-sigma
Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into…
building-detection-rules-with-sigma
Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into…
building-detection-rules-with-sigma
Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into…
building-detection-rules-with-sigma
Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into…
building-detection-rules-with-sigma
Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into…
Log Analysis & SIEM Integration
Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel.