analyzing-threat-landscape-with-misp
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.
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Installation Guide
How to use analyzing-threat-landscape-with-misp on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
analyzing-threat-landscape-with-misp
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches analyzing-threat-landscape-with-misp from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate analyzing-threat-landscape-with-misp. Access via /analyzing-threat-landscape-with-misp in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
| name | analyzing-threat-landscape-with-misp |
| description | Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends. |
| domain | cybersecurity |
| subdomain | threat-intelligence |
| tags | - analyzing - threat - landscape - with |
| 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 |
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)
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- GGanesh Mohane★★★★★Dec 24, 2024
Solid pick for teams standardizing on skills: analyzing-threat-landscape-with-misp is focused, and the summary matches what you get after install.
- AAnaya Shah★★★★★Dec 8, 2024
analyzing-threat-landscape-with-misp reduced setup friction for our internal harness; good balance of opinion and flexibility.
- NNikhil Harris★★★★★Dec 4, 2024
analyzing-threat-landscape-with-misp has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAdvait Patel★★★★★Nov 27, 2024
I recommend analyzing-threat-landscape-with-misp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- CCamila Shah★★★★★Nov 23, 2024
analyzing-threat-landscape-with-misp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- SSakshi Patil★★★★★Nov 15, 2024
We added analyzing-threat-landscape-with-misp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- NNikhil Diallo★★★★★Nov 15, 2024
Keeps context tight: analyzing-threat-landscape-with-misp is the kind of skill you can hand to a new teammate without a long onboarding doc.
- NNikhil Rahman★★★★★Oct 18, 2024
Useful defaults in analyzing-threat-landscape-with-misp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- CCarlos Rahman★★★★★Oct 14, 2024
We added analyzing-threat-landscape-with-misp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- CChaitanya Patil★★★★★Oct 6, 2024
analyzing-threat-landscape-with-misp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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