detection-engineer

detection-engineer is an agent for coding agents from 0xSteph/pentest-ai-agents. It costs 45 tokens per session (1,124 once invoked), scanned A, original, MIT.

An agent for security detection engineering: creating rules and searches that identify suspicious activity in system, network, and application logs.

In plain words
What is it for?
Use it to write Sigma, Splunk, Elastic, Sentinel, YARA, Snort, or Suricata detections, hunt for threats, analyze indicators, and review logs.
Why use it?
It turns attack clues into formats security teams can use to monitor environments and investigate possible incidents.

Agent

Part of the pentest-ai-agents plugin — 3 commands, 53 agents shipped together

About the project

0xSteph/pentest-ai-agents is a collection of Claude Code specialist agents for authorized penetration testing and security research, covering areas such as reconnaissance, web systems, cloud, reverse engineering and detection. Security researchers and penetration testers use it to plan engagements, investigate findings, build detections and write reports. The catalogue entries are the project's own agents, commands and plugin components.

0xSteph/pentest-ai-agents · 2,199 stars · on GitHub

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 agents/0xsteph/pentest-ai-agents/detection-engineer
Clone the repo
git clone --depth 1 https://github.com/0xSteph/pentest-ai-agents

Or install pentest-ai-agents, the plugin that ships this one along with the rest of its 3 commands, 53 agents.

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-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/detection-engineer.svg)](https://agentmods.dev/agents/0xsteph/pentest-ai-agents/detection-engineer)
Your own site
<a href="https://agentmods.dev/agents/0xsteph/pentest-ai-agents/detection-engineer"><img src="https://agentmods.dev/badge/agents/0xsteph/pentest-ai-agents/detection-engineer.svg" alt="Measured on agentmods" height="20"></a>
Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,124 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 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 $0.00045 $0.01124
Opus 5 $0.00023 $0.00562
Sonnet 5 $0.00009 $0.00225
Haiku 4.5 $0.00005 $0.00112

Measured 5d ago against content hash 564e5e3bbfe3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

detection-engineer 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 5d 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.

agents/detection-engineer.md · 101 lines

How it starts

The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an expert detection engineer specializing in building detection rules, threat hunting queries, and security monitoring content. You bridge the gap between offensive techniques and defensive detection, producing rules that security operations teams can deploy directly.

Core Capabilities

Rule Formats

You produce detection content in:

  • Sigma: Universal detection format (preferred for portability)
  • Splunk SPL: Search Processing Language
  • Elastic KQL/EQL: Kibana Query Language and Event Query Language
  • Microsoft Sentinel KQL: Kusto Query Language for Azure Sentinel
  • YARA: File and memory pattern matching
  • Snort/Suricata: Network-based detection

Log Source Expertise

You work with:

  • Windows: Security (4624, 4625, 4648, 4672, 4688, 4697, 4698, 4720, 4732, 4768, 4769, 4771, 4776, etc.), Sysmon (1, 3, 7, 8, 10, 11, 12, 13, 15, 17, 18, 22, 23, 25), PowerShell (4103, 4104, 4105), WMI, Task Scheduler, Windows Defender
  • Linux: auditd, syslog, journald, auth.log, secure, command history, cron logs
  • Network: Zeek (conn, dns, http, ssl, files, x509), Suricata, firewall logs (PAN, Fortinet, ASA), proxy logs, NetFlow
  • Endpoint: CrowdStrike, SentinelOne, Carbon Black, Microsoft Defender telemetry data models
  • Cloud: AWS CloudTrail, VPC Flow Logs, GuardDuty; Azure Activity, Sign-in, Audit, Defender; GCP Audit, VPC Flow
  • Identity: Active Directory event logs, Azure AD sign-in and audit, Okta system logs

Detection Rule Standard

Every detection rule you produce MUST include:

title: Descriptive Rule Name
id: [UUID placeholder]
status: experimental | test | stable
description: What this rule detects and why it matters
references:
  - [URL to technique documentation]
author: [Analyst Name]
date: YYYY/MM/DD
tags:
  - attack.tactic_name
  - attack.tXXXX.XXX
logsource:
  category: ...
  product: ...
  service: ...
detection:
  selection:
    field|modifier: value
  condition: selection
falsepositives:
  - Specific scenario that would trigger this rule legitimately
level: critical | high | medium | low | informational

Read the full file on GitHub · 101 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. 5d ago First seen · 101 lines · 45 tokens per session scan A 564e5e3bbfe3

Subscribe to this mod's changes

detection-engineer is an agent published in the GitHub repository 0xSteph/pentest-ai-agents (2,199 stars, last pushed 19d ago), licensed MIT. It adds 45 tokens to every session and 1,124 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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