detection-engineering

detection-engineering is a skill for Claude Code, Codex from HermeticOrmus/LibreSecOps-Claude-Code. It costs 0 tokens per session (3,059 once invoked), scanned B, original, MIT.

A security workflow for studying threats, writing detection rules, and testing them. Sigma and YARA are rule formats used to identify suspicious activity in logs, files, or systems.

In plain words
What is it for?
It helps map threats to techniques and data sources, create Sigma or YARA rules, test alert quality, deploy rules, and reduce false alarms.
Why use it?
It treats detection rules as items that need validation, tuning, monitoring, and eventual retirement rather than writing them once and forgetting them.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/hermeticormus/libresecops-claude-code/detection-engineering
Any agent
npx skills add HermeticOrmus/LibreSecOps-Claude-Code --skill detection-engineering
Clone the repo
git clone --depth 1 https://github.com/HermeticOrmus/LibreSecOps-Claude-Code

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for detection-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/detection-engineering.svg)](https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/detection-engineering)
Your own site
<a href="https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/detection-engineering"><img src="https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/detection-engineering.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,059 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.03059
Opus 5 $0.00000 $0.01529
Sonnet 5 $0.00000 $0.00612
Haiku 4.5 $0.00000 $0.00306

Measured 3d ago against content hash cfd838e67ef5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

detection-engineering scanned grade B with 2 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 3d 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.

Asks for rootmediumPrivilege escalation

A mod that escalates privileges can change anything on the machine, not only the project.

run: sudo apt-get install -y yara

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

$d6 = "wget" ascii wide nocase
plugins/blue-team-detection/skills/detection-engineering/SKILL.md · 371 lines

How it starts

The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Detection Engineering

Sigma rule syntax, YARA rule writing, detection-as-code workflows, and the methodology of building reliable, maintainable detection rules.

Knowledge Base

The Detection Engineering Lifecycle

Threat Intelligence --> Technique Analysis --> Rule Writing --> Testing
         |                                                        |
         v                                                        v
    ATT&CK Mapping --> Data Source Validation --> Deployment --> Tuning
                                                                  |
                                                                  v
                                                  Monitoring --> Retirement

Detection rules are not write-once artifacts. They require ongoing tuning, testing, and retirement as adversary behavior evolves and environments change.

Detection Quality Metrics

Metric Definition Target
True Positive Rate Alerts that are actual threats > 80% for high-fidelity rules
False Positive Rate Alerts that are benign < 20% (ideally < 5%)
Mean Time to Detect (MTTD) Time from adversary action to alert < 15 minutes
Detection Coverage % of ATT&CK techniques with detections Context-dependent
Rule Stability Rules that do not require monthly tuning > 90%

Patterns

Pattern 1: Sigma Rule Syntax Reference

# Complete Sigma rule example: Detecting LSASS Memory Access
title: LSASS Memory Access by Non-System Process
id: 0d894093-71bc-43c3-8985-a9f63f0c7a76
status: stable
description: |
    Detects process access to LSASS memory which is typically used by
    credential dumping tools like Mimikatz. This rule monitors Sysmon
    EventID 10 (ProcessAccess) targeting lsass.exe.
references:
    - https://attack.mitre.org/techniques/T1003/001/
    - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md
author: LibreSecOps Detection Engineering
date: 2024/01/15
modified: 2024/06/20
tags:
    - attack.credential_access
    - attack.t1003.001
logsource:
    category: process_access
    product: windows
detection:
    selection:
        TargetImage|endswith: '\lsass.exe'
        GrantedAccess|contains:
            - '0x1010'    # PROCESS_VM_READ | PROCESS_QUERY_INFORMATION
            - '0x1410'    # PROCESS_VM_READ | PROCESS_QUERY_LIMITED_INFORMATION
            - '0x1038'    # PROCESS_VM_READ | PROCESS_VM_WRITE | PROCESS_VM_OPERATION
            - '0x40'      # PROCESS_DUP_HANDLE
            - '0x1fffff'  # PROCESS_ALL_ACCESS
    filter_system:
        SourceImage|startswith:
            - 'C:\Windows\System32\'
            - 'C:\Windows\SysWOW64\'
    filter_av:
        SourceImage|contains:
            - '\MsMpEng.exe'         # Windows Defender
            - '\csfalconservice.exe' # CrowdStrike
            - '\cb.exe'              # Carbon Black
    filter_lsass_self:
        SourceImage|endswith: '\lsass.exe'
    condition: selection and not (filter_system or filter_av or filter_lsass_self)
falsepositives:
    - Legitimate security tools performing credential management
    - Windows Credential Manager operations
    - Some backup solutions that access LSASS
level: high

Read the full file on GitHub · 371 lines

Changes

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.

  1. 3d ago First seen · 371 lines · 0 tokens per session scan B cfd838e67ef5

Subscribe to this mod's changes

detection-engineering is a skill published in the GitHub repository HermeticOrmus/LibreSecOps-Claude-Code (4 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,059 tokens. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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