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Attack Tree Construction
Generate structured, scored attack trees with AND/OR logic to visualize threat paths and identify security gaps.
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See it in action
You say
Build an attack tree for the attacker goal "Exfiltrate sensitive customer data from a cloud-hosted CRM application". Include sub-goals, leaf node conditions, AND/OR node logic, mitigation mappings, and probability annotations.
Your agent does
The skill produces a structured attack tree with the root goal decomposed into sub-goal branches covering credential compromise, session hijacking, API abuse, and insider threat vectors. Each branch is resolved to leaf node conditions with AND/OR logic annotations. Mitigation mappings are provided for each leaf node with effectiveness and implementation notes. Probability and cost annotations are applied where estimable from the scenario context. The output is formatted for direct use in a threat model document or risk register entry.
What you get
About this skill
Systematic Threat Path Visualization
Modern security architecture requires more than just a list of risks; it requires a deep understanding of the logical paths an attacker takes to reach a goal. This skill automates the construction of structured attack trees, allowing developers and security engineers to decompose complex threats into atomic, measurable steps.
What it does
- Logical Decomposition: Breaks down high-level objectives into sub-goals using strict AND/OR node logic.
- Attribute Scoring: Evaluates every leaf node based on time, cost, technical skill required, and detection probability.
- Path Analysis: Automatically identifies the "Path of Least Resistance" (lowest cost) and the "Stealthiest Path" (lowest detection).
- Defensive Mapping: Overlays existing controls onto the tree to highlight gaps where no mitigations exist.
Why use this skill?
Manually drawing attack trees is time-consuming and prone to logic errors. This skill ensures consistency in your threat models by enforcing atomic leaf nodes and valid Boolean logic. It serves as a bridge between high-level risk assessments and technical red-teaming, providing a clear visual or data-driven output (JSON, Mermaid, or Python models) that can be integrated into CI/CD security reviews or architecture documentation.
Supported Outputs
The skill can produce structured text trees, Mermaid.js diagrams for GitHub/Notion, JSON for programmatic analysis, or Python data models for Monte Carlo simulations.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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