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.
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 agentmods add agents/0xsteph/pentest-ai-agents/detection-engineergit clone --depth 1 https://github.com/0xSteph/pentest-ai-agentsWrote 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/agents/0xsteph/pentest-ai-agents/detection-engineer)<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>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 | $0.00045 | $0.01124 |
| Opus 5 | $0.00023 | $0.00562 |
| Sonnet 5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
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.
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
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.
- 5d ago First seen · 101 lines · 45 tokens per session scan A 564e5e3bbfe3
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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