Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/threat-detection
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization.
📊 Stats
Stars23,062
Forks3,139
LanguagePython
LicenseMIT
📦 Ships with claude-skills
</> SKILL.md
threat-detection.SKILL.md
---name: "threat-detection"
description: "Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC sweep generation, z-score anomaly detection, and MITRE ATT&CK-mapped signal prioritization."
---# Threat Detection
Threat detection skill for proactive discovery of attacker activity through hypothesis-driven hunting, IOC analysis, and behavioral anomaly detection. This is NOT incident response (see incident-response) or red team operations (see red-team) — this is about finding threats that have evaded automated controls.
---## Table of Contents
- [Overview](#overview)
- [Threat Signal Analyzer](#threat-signal-analyzer)
- [Threat Hunting Methodology](#threat-hunting-methodology)
- [IOC Analysis](#ioc-analysis)
- [Anomaly Detection](#anomaly-detection)
- [MITRE ATT&CK Signal Prioritization](#mitre-attck-signal-prioritization)
- [Deception and Honeypot Integration](#deception-and-honeypot-integration)
- [Workflows](#workflows)
- [Anti-Patterns](#anti-patterns)
- [Cross-References](#cross-references)
---## Overview
### What This Skill Does
This skill provides the methodology and tooling for **proactive threat detection** — finding attacker activity through structured hunting hypotheses, IOC analysis, and statistical anomaly detection before alerts fire.
Read access to SIEM/EDR telemetry, endpoint logs, and network flow data. IOC feeds require freshness within 30 days to avoid false positives. Hunting hypotheses must be scoped to the environment before execution.
---
## Threat Signal Analyzer
The `threat_signal_analyzer.py` tool supports three modes: `hunt` (hypothesis scoring), `ioc` (sweep generation), and `anomaly` (statistical detection).
```bash
# Hunt mode: score a hypothesis against MITRE ATT&CK coverage
IOCs older than their threshold are flagged as `stale` and excluded from sweep target generation. Running sweeps against stale IOCs inflates false positive rates and reduces SOC credibility. Refresh IOC feeds from threat intelligence platforms (MISP, OpenCTI, commercial TI) before every hunt cycle.
---
## Anomaly Detection
Statistical anomaly detection identifies behavior that deviates from established baselines without relying on known-bad signatures.
1. **Hunting without a hypothesis** — Running broad queries across all telemetry without a focused question generates noise, not signal. Every hunt must start with a testable hypothesis scoped to one or two ATT&CK techniques.
2. **Using stale IOCs** — IOCs older than 30 days generate false positives that train analysts to ignore alerts. Always check IOC freshness before sweeping; exclude stale indicators from automated sweeps.
3. **Skipping baseline establishment** — Anomaly detection without a valid baseline produces alerts on normal high-volume days. Require 14+ days of baseline data before enabling statistical alerting on any entity type.
4. **Hunting only known techniques** — Hunting exclusively against documented ATT&CK techniques misses novel adversary behavior. Regularly include open-ended anomaly analysis that can surface unknown TTPs.
5. **Not closing the feedback loop to detection engineering** — Hunt findings that confirm malicious behavior must produce new detection rules. Hunting that doesn't improve detection coverage has no lasting value.
6. **Treating every anomaly as a confirmed threat** — High z-scores indicate deviation from baseline, not confirmed malice. All anomalies require human triage to confirm or dismiss before escalation.
7. **Ignoring honeypot alerts** — Any interaction with a deception asset is a high-fidelity signal. Treating honeypot alerts as noise invalidates the entire deception investment.
---
## Cross-References
| Skill | Relationship |
|-------|-------------|
| [incident-response](../incident-response/SKILL.md) | Confirmed threats from hunting escalate to incident-response for triage and containment |
| [red-team](../red-team/SKILL.md) | Red team exercises generate realistic TTPs that inform hunt hypothesis prioritization |
| [cloud-security](../cloud-security/SKILL.md) | Cloud posture findings (open S3, IAM wildcards) create hunting targets for data exfiltration TTPs |
| [security-pen-testing](../security-pen-testing/SKILL.md) | Pen test findings identify attack surfaces that threat hunting should monitor post-remediation |
