# Threat Model Visualize

Turn an existing threat model into an interactive architecture and threat-path explorer.

- Repository: https://github.com/openai/codex-security (11278 stars)
- Category: Security
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add openai/codex-security --skill threat-model-visualize -g -a claude-code -y`
Codex: `npx skills add openai/codex-security --skill threat-model-visualize -g -a codex -y`
Cursor: `npx skills add openai/codex-security --skill threat-model-visualize -g -a cursor -y`
Gemini CLI: `npx skills add openai/codex-security --skill threat-model-visualize -g -a gemini-cli -y`

## Description

Turn an existing threat model into an interactive architecture and threat-path explorer. Resolve simple requests for a target's last scan; do not perform a new assessment.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Threat Model Visualize

Create a standalone explorer that shows application teams and leadership what can go wrong, how it could happen, why it matters, and where controls would stop or limit it. Use the shared workbench and role palette; do not use Mermaid or run a new scan.

A request such as “Visualize the threat model generated in the last scan of X” is sufficient. Resolve the target, latest assessment and snapshot. If the latest artifact is missing, disclose that before using an older fallback. The default input is the existing threat-model artifact, not an automatic import of a complete scan. Keep inputs immutable and do not silently combine snapshots. Do not inspect application code or invent topology, authority, controls, reachability or validation.

Capture source-supported deployment context: exposure, trust, tenant and process boundaries, identities, and conditional deployment assumptions. Do not treat repository defaults as proof of deployed configuration. Preserve uncertainty and the distinction between hypotheses, findings, declared controls and observed enforcement. For several explicitly supplied artifacts, pass one derived document to `--assessment`. Label verbatim excerpts with each source’s identity, snapshot and original finding status. Distinguish findings from hypotheses, identify local IDs, and preserve conflicting accounts and the original artifacts.

For a **fresh candidate or regeneration**, author a new view model; do not patch or copy a previous candidate model or HTML. Ordinary edits may reuse the existing model. Renderer-only polishing is a display update, not fresh generation.

## 1. Enumerate and validate the source

Complete this phase before planning any visualization, choosing display groupings or routes, assigning geometry, or authoring the explorer JSON. Save `source-inventory.md` in the workspace. Validate fidelity to the supplied assessment, not the application implementation; do not run a new assessment to fill gaps.

The rest: https://github.com/openai/codex-security/blob/HEAD/plugins/codex-security/skills/threat-model-visualize/SKILL.md