threat-modeler
Generate threat models using STRIDE, PASTA, or VAST methodologies
npx skills add a5c-ai/babysitter --skill threat-modeler --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Threat Modeler Skill ## Overview Generates threat models using STRIDE, PASTA, or VAST methodologies with attack tree generation, data flow diagram analysis, and threat prioritization using DREAD. ## Capabilities - Generate STRIDE threat models - PASTA methodology support - VAST methodology support - Attack tree generation - Data flow diagram analysis - Threat prioritization (DREAD) - Microsoft Threat Modeling Tool integration - Mitigation recommendations ## Target Processes - security-architecture-review - api-design-specification ## Input Schema ```json { "type": "object", "required": ["system"], "properties": { "system": { "type": "object", "properties": { "name": { "type": "string" }, "description": { "type": "string" }, "dataFlows": { "type": "array" }, "assets": { "type": "array" }, "trustBoundaries": { "type": "array" }, "externalEntities": { "type": "array" } } }, "methodology": { "type": "string", "enum": ["STRIDE", "PASTA", "VAST"], "default": "STRIDE" }, "options": { "type": "object", "properties": { "prioritization": { "type": "string", "enum": ["DREAD", "CVSS", "custom"], "default": "DREAD" }, "generateAttackTrees": { "type": "boolean", "default": true }, "outputForma
- Overview
- Capabilities
- Target Processes
- Input Schema
- Output Schema
- Usage Example
What does the threat-modeler skill do?
Generate threat models using STRIDE, PASTA, or VAST methodologies
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill threat-modeler --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.