building-detection-rules-with-sigma

Overview of building-detection-rules-with-sigma skill

What this skill does

The building-detection-rules-with-sigma skill helps you turn threat intelligence or an existing vendor rule into portable Sigma detections that can be converted for SIEMs like Splunk, Elastic, and Microsoft Sentinel. It is best for analysts who need one rule authoring format across tools, not a one-off prompt for a single query language.

Who should use it

Use the building-detection-rules-with-sigma skill if you are a SOC engineer, detection engineer, or building-detection-rules-with-sigma for Security Audit work and need reusable detections with MITRE ATT&CK alignment. It is a strong fit when you want to standardize rules, review coverage, or move from ad hoc searches to detection-as-code.

What makes it useful

This skill is more decision-oriented than a generic Sigma prompt: it emphasizes when to use Sigma, what data you need up front, and how to convert rules into backend-specific queries. The repo also includes a practical Python agent and an API reference, which makes the building-detection-rules-with-sigma skill useful for both manual rule writing and automation.

How to Use building-detection-rules-with-sigma skill

Install and prepare the context

Use the building-detection-rules-with-sigma install flow with the directory’s standard command, then inspect skills/building-detection-rules-with-sigma/SKILL.md first. After that, read references/api-reference.md for pySigma usage and scripts/agent.py for the validation/conversion path. The repo is small, so the fastest way to understand the skill is to follow the rule lifecycle rather than browse every file.

Give the skill the right input

The building-detection-rules-with-sigma usage works best when your prompt includes: the threat behavior, the log source, the target SIEM, and any known constraints like exclusions or environment-specific fields. Good input looks like: “Build a Sigma rule for suspicious PowerShell download cradle activity from Windows process creation logs, map it to ATT&CK, and make it convertible to Splunk and Sentinel.”

Follow a practical workflow

Start with the detection idea, then define the observable fields, then write the Sigma rule, and only then convert it to backend queries. If you are adapting an existing rule, ask for normalization first: “Convert this vendor-specific detection into Sigma, preserve the logic, and note any fields that cannot be translated cleanly.” That order avoids brittle rules and unclear mappings.

Read these files first

For the building-detection-rules-with-sigma guide, prioritize SKILL.md for scope and constraints, references/api-reference.md for rule fields and backend examples, and scripts/agent.py for validation and conversion behavior. The script is especially helpful because it shows the intended path from YAML rule to backend output and reveals what the skill expects in a working rule.

building-detection-rules-with-sigma skill FAQ

Is this only for Sigma experts?

No. The building-detection-rules-with-sigma skill is useful if you understand basic detection logic, even if you are new to Sigma syntax. It will be more effective if you can name the event source and target platform, but you do not need to memorize backend details before using it.

When should I not use it?

Do not use building-detection-rules-with-sigma when you need real-time streaming detection logic that depends on native SIEM features Sigma cannot express well, or when the target platform requires a non-portable capability such as vendor-specific risk scoring. In those cases, a direct platform-native rule is usually a better fit.

How is it different from a normal prompt?

A normal prompt can draft a rule, but the building-detection-rules-with-sigma skill is structured around portability, ATT&CK mapping, and conversion to backends like Splunk or Elastic. That matters when your goal is repeatable detection engineering rather than a single search string.

What is the main adoption risk?

The most common risk is asking for a rule before you know the log source, field names, or backend target. Sigma can describe logic cleanly, but it cannot fix missing telemetry. If those inputs are vague, the output will be too generic to deploy.

How to Improve building-detection-rules-with-sigma skill

Provide the observables, not just the intent

The best building-detection-rules-with-sigma skill results come when you describe concrete signals: process names, command-line fragments, parent-child relationships, registry paths, file writes, or network indicators. “Detect malware activity” is too broad; “detect encoded PowerShell with web download behavior in Windows process creation logs” gives the model something it can actually encode.

State the backend and data model early

Tell the skill which SIEM you want first, because conversion quality depends on field mappings and backend support. For example, “Author in Sigma, convert to Splunk SPL, and call out any field mapping assumptions for Sysmon Event ID 1” is much better than a backend-agnostic request.

Ask for validation and edge cases

When refining building-detection-rules-with-sigma usage, ask for false-positive considerations, required exclusions, and any fields that are optional versus mandatory. Good prompts also request a quick test plan, such as sample telemetry patterns or expected matches, so you can verify the rule before rollout.

Iterate after the first draft

Treat the first output as a draft detection spec, not final production content. Tighten it by adding exclusions, lowering noise, or splitting overly broad logic into separate rules. If conversion is the goal, ask the skill to preserve intent while noting where Sigma-to-backend translation may change semantics.