What is threat-modeling?

A security analysis of an application, software pipeline, or AI system. It maps how data moves, who can attack it, and which risks need attention.

Creating data-flow and threat diagrams, STRIDE or PASTA risk analyses, penetration-testing plans, mitigation reviews, compliance gap checks, and risk registers or reports.

It brings information from project-management, deployment, service-management, and code systems into one risk review. This helps teams find security gaps and decide which protections and tests are needed.

threat-modeling — Skill for Claude Code by suyogpawar88/Threat-Model

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/suyogpawar88/Threat-Model

agentmods

npx agentmods add skills/suyogpawar88/threat-model/threat-modeling

Made for: Claude Code.

Or install Threat-Model Agent, the plugin that ships this one along with the rest of its 1 skill, 4 MCP servers.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/suyogpawar88/threat-model/threat-modeling/github.svg)](https://agentmods.dev/skills/suyogpawar88/threat-model/threat-modeling)

Your own site

<a href="https://agentmods.dev/skills/suyogpawar88/threat-model/threat-modeling"><img src="https://agentmods.dev/badge/skills/suyogpawar88/threat-model/threat-modeling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/suyogpawar88/threat-model/threat-modeling"><img src="https://agentmods.dev/badge/skills/suyogpawar88/threat-model/threat-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 346 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,299 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00346	$0.04299
Opus 5.5	$0.00138	$0.01720
Sonnet 5.5	$0.00069	$0.00860
Haiku 4.5	$0.00035	$0.00430

Measured 2mo ago against content hash b096f8282d83, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

threat-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/threat-modeling/SKILL.md · 264 lines

How it starts

The opening of the file, as written. The whole thing — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Threat Modeling Skill

Pulls live context from Jira, Jenkins, ServiceNow, and a code repository via this plugin's MCP connectors, then runs an end-to-end threat model covering both conventional application/API systems and AI/ML/LLM infrastructure: a data flow diagram, trust boundaries and threat actors, a STRIDE threat register scored for likelihood and business impact (optionally the full 7-stage PASTA process for higher-rigor engagements), a Page/API/Module-scoped AppSec / penetration-testing test case plan (see references/appsec-pentest-test-cases.md), a compensating-controls and mitigation-assurance assessment, a compliance gap analysis, and every threat and attack-chain step mapped to the relevant OWASP Top 10 list(s) — OWASP Top 10 (web AppSec), OWASP API Security Top 10, and OWASP Top 10 for LLM Applications — plus MITRE ATT&CK / MITRE ATLAS technique IDs. Deliverables: a draw.io-compatible DFD, a draw.io-compatible threat model diagram (trust boundaries + threat actors), a Word report, and/or an Excel risk register — whichever output formats the user asks for. Need just a standalone pentest checklist without a full threat model? Run scripts/build_pentest_checklist.py directly against references/appsec_pentest_test_cases_library.json (or a scoped copy of it) for a ready-to-use Excel tracker.

Why pull from tools instead of asking for a description: a ticket or repo almost always contains more architectural truth than a user can recite from memory — auth middleware in the repo, actual deploy steps in Jenkins, prior incidents in ServiceNow, or AI-framework usage (LangChain, a vector DB client, a model-serving container) that tells you this is an AI-scoped system before the user even says so. Grounding the model in that material produces a more accurate DFD and catches things a manual description would miss.

Why Sonnet by default: scripts/summarize.py is called to compress large raw payloads (console logs, big ticket threads, large repo files) before they enter this session's context. It defaults to claude-sonnet-5 — a strong cost/quality balance for extractive summarization — via the MODEL_NAME env var. Point it at another available model (see config/model-config.example.json) if the user wants higher-fidelity summaries (claude-opus-4-8) or lower cost on high-volume pulls (claude-haiku-4-5-20251001). This only affects the summarization helper — the main threat-modeling reasoning always runs on whichever model is driving the current session.

Running this outside Claude Code / Cowork: this skill's instructions, reference docs, and Python scripts are plain markdown/JSON/Python with no Claude-specific dependencies, so the same workflow runs under OpenAI Codex CLI (via the root AGENTS.md) and Cursor (via .cursor/rules/threat-modeling.mdc and .cursor/mcp.json) — see README.md's "Using this plugin outside Claude Code" section for setup per tool. Whichever agent is driving, follow this file and its references/ the same way.

Read the full file on GitHub · 264 lines

Files

What ships with it

17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 264 lines · 346 tokens per session scan A b096f8282d83

Subscribe to this mod's changes

threat-modeling is a skill published in the GitHub repository suyogpawar88/Threat-Model (6 stars, last pushed 3mo ago), licensed MIT. It adds 346 tokens to every session and 4,299 once invoked, about $0.0014 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.