/analyzing-threat-landscape-with-misp
Query a MISP (Malware Information Sharing Platform) instance via PyMISP
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-threat-landscape-with-misp --agent claude-codeHow it fires
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/analyzing-threat-landscape-with-misp
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Query a MISP (Malware Information Sharing Platform) instance via PyMISP
SKILL.md
analyzing-threat-landscape-with-misp.SKILL.mdname: analyzing-threat-landscape-with-misp
description: Query a MISP (Malware Information Sharing Platform) instance via PyMISP
to compute event statistics, IOC type breakdowns, threat actor galaxy clusters,
and tag trends, and generate threat landscape reports with temporal trends. Use
when asked to analyze threat intelligence data, summarize top threat actors or
malware families, or produce a CTI landscape report from MISP events.
domain: cybersecurity
subdomain: threat-intelligence
tags:
- threat-intelligence
- misp
- threat-landscape
- ioc-analysis
- cti
- threat-sharing
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- File Metadata Consistency Validation
- Application Protocol Command Analysis
- Identifier Analysis
- Content Format Conversion
- Message Analysis
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
mitre_attack:
- T1566
- T1071.001
- T1568
- T1583.001
- T1102
Analyzing Threat Landscape with MISP
When to Use
- When investigating security incidents that require analyzing threat landscape with misp
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with threat intelligence concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
1. Install dependencies: `pip install pymisp` 2. Configure MISP URL and API key. 3. Run the agent to generate threat landscape analysis:
- Pull event statistics by threat level and date range
- Analyze attribute type distributions (IP, domain, hash, URL)
- Identify top MITRE ATT&CK techniques from event tags
- Track threat actor activity via galaxy clusters
- Generate temporal trend analysis of IOC submissions
python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json
Examples
Threat Landscape Summary
Period: Last 90 days
Events analyzed: 1,247
Top threat level: High (43%)
Top attribute type: ip-dst (31%), domain (22%), sha256 (18%)
Top MITRE technique: T1566 Phishing (89 events)
Top threat actor: APT28 (34 events)
Read more
name: analyzing-threat-landscape-with-misp description: Query a MISP (Malware Information Sharing Platform) instance via PyMISP to compute event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends, and generate threat landscape reports with temporal trends. Use when asked to analyze threat intelligence data, summarize top threat actors or malware families, or produce a CTI landscape report from MISP events. domain: cybersecurity subdomain: threat-intelligence tags: - threat-intelligence - misp - threat-landscape - ioc-analysis - cti - threat-sharing version: '1.0' author: mahipal license: Apache-2.0 d3fend_techniques: - File Metadata Consistency Validation - Application Protocol Command Analysis - Identifier Analysis - Content Format Conversion - Message Analysis nist_csf: - ID.RA-01 - ID.RA-05 - DE.CM-01 - DE.AE-02 mitre_attack: - T1566 - T1071.001 - T1568 - T1583.001 - T1102
Analyzing Threat Landscape with MISP
When to Use
- When investigating security incidents that require analyzing threat landscape with misp
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with threat intelligence concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
1. Install dependencies: `pip install pymisp` 2. Configure MISP URL and API key. 3. Run the agent to generate threat landscape analysis:
- Pull event statistics by threat level and date range
- Analyze attribute type distributions (IP, domain, hash, URL)
- Identify top MITRE ATT&CK techniques from event tags
- Track threat actor activity via galaxy clusters
- Generate temporal trend analysis of IOC submissions
python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json
Examples
Threat Landscape Summary
Period: Last 90 days Events analyzed: 1,247 Top threat level: High (43%) Top attribute type: ip-dst (31%), domain (22%), sha256 (18%) Top MITRE technique: T1566 Phishing (89 events) Top threat actor: APT28 (34 events)
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
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