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Mahipal
2fb6a9faff
Each rewritten description now states both what the skill does (concrete capability, named tools/artifacts) and an explicit when-to-use trigger, improving agent discovery/activation. Grounded in each skill's own body; changes confined to the `description` field only (bodies and all other frontmatter untouched). Produced by a gated audit->rewrite->recheck loop (548 -> 0 flagged) with a sampled anti-invention check (0 ungrounded). Schema: 817/817 pass. Framework-ID gate: 0 defects.
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2.3 KiB
name, description, domain, subdomain, tags, version, author, license, d3fend_techniques, nist_csf, mitre_attack
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| analyzing-threat-landscape-with-misp | 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. | cybersecurity | threat-intelligence |
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1.0 | mahipal | Apache-2.0 |
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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
- Install dependencies:
pip install pymisp - Configure MISP URL and API key.
- 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)