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.
git clone --depth 1 https://github.com/briiirussell/cybersecurity-skillsWrote 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/rules/briiirussell/cybersecurity-skills/siem-detection)<a href="https://agentmods.dev/rules/briiirussell/cybersecurity-skills/siem-detection"><img src="https://agentmods.dev/badge/rules/briiirussell/cybersecurity-skills/siem-detection/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/rules/briiirussell/cybersecurity-skills/siem-detection"><img src="https://agentmods.dev/badge/rules/briiirussell/cybersecurity-skills/siem-detection.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 | $0.00128 | $0.02500 |
| Opus 5 | $0.00064 | $0.01250 |
| Sonnet 5 | $0.00026 | $0.00500 |
| Haiku 4.5 | $0.00013 | $0.00250 |
Grade A, and why
siem-detection 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 11d 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SIEM Detection — Detection Engineering
Build, audit, and maintain SIEM detection content — the rules that fire alerts. Distinct from incident-triage (responds when alerts fire) and from soc-operations (runs the SOC that triages alerts). This skill is the engineering layer: log coverage, rule authoring, tuning, and detection-as-code workflows.
Cross-references: incident-triage for what happens after the alert, threat-hunting for proactive hypothesis-driven hunts that often graduate into detection rules, breach-patterns for detection ideas pulled from public breach disclosures, soc-operations for the alert-triage operations on top of the detections engineered here.
Scope
This skill covers:
- Log source coverage assessment ("are we even collecting the events we'd need to detect X?")
- Rule authoring across major SIEM query languages (Sigma, KQL, SPL, Elastic ES|QL, Chronicle YARA-L)
- MITRE ATT&CK mapping — every rule tagged with technique IDs for coverage analysis
- Detection-as-code workflows (rules in Git, CI tests, deployment automation)
- Alert tuning workflow — reducing false positives without losing true positives
- Coverage gap analysis using ATT&CK Navigator
This skill does NOT cover:
- Live alert triage (that's
incident-triage) - Building a SOC team or alert escalation criteria (
soc-operations) - Active threat hunting (
threat-hunting)
Methodology
Step 1: Map log sources to ATT&CK coverage
Before writing any rule, audit what you can detect.
Categorize log sources by what they observe:
| Category | Sources | Observes |
|---|---|---|
| Endpoint | EDR (CrowdStrike, SentinelOne, Defender), Sysmon, osquery | Process exec, file write, network, registry, parent-child |
| Network | Zeek/Bro, Suricata, NSM, firewall, DNS query logs | Connections, protocols, DNS queries, TLS metadata |
| Identity | Okta, Entra ID, AD, Auth0, GCP/AWS sign-in | Authentications, MFA, group changes, role assignments |
| Cloud | CloudTrail (AWS), Audit Logs (GCP), Activity Log (Azure) | API calls — what was created/changed/deleted |
| Application | App logs, WAF logs, load balancer logs, gateway logs | Request URLs, status codes, auth outcomes |
| SaaS | Google Workspace, M365, Salesforce, GitHub audit | Admin actions, sharing, sensitive doc access |
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.
- 11d ago First seen · 239 lines · 128 tokens per session scan A 5bcb968b0355
siem-detection is a cursor rule published in the GitHub repository briiirussell/cybersecurity-skills (384 stars, last pushed 3mo ago), licensed MIT. It adds 128 tokens to every session and 2,500 once invoked, about $0.0006 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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