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Threat Modeling

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nahid-sparktales
threat-modeling

Work out what is actually worth defending in this system — the trust boundaries data crosses, the assets behind them, what an attacker can already do, and the small number of threats that justify a control. Use when designing or changing a system's shape, before building auth, payments, uploads, file sharing or multi-tenancy, when adding an integration that crosses a boundary, or when asked whether a design is safe. Not for finding bugs in code that already exists (that is secure-code-review), not a compliance questionnaire, and not incident response.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill namethreat-modeling
Stars
58
Forks
5
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Threat Modeling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/security/threat-modeling
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/security/threat-modeling .claude/skills/threat-modeling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threat Modeling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Threat Modeling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Threat Modeling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Threat modeling

Most threat models fail by being complete. A list of every conceivable attack tells nobody what to build. The output that is worth having is short: a handful of threats with a capability behind them, each attached to one control, plus the assumptions that would invalidate the whole thing.

When this fires

A system's shape is being decided or changed — a new service, a new integration, a new class of data, a new kind of user. Also when someone asks "is this secure?" about a design rather than about code. It does not fire for an internal refactor that moves no data across a boundary.

Procedure

  1. Draw what exists, from the repository. Routes, jobs, queues, stores, third-party calls, clients. Follow the data, not the org chart. If the architecture doc disagrees with the code, the code is the system. Anything you cannot find, ask about rather than assume.
  2. Mark the trust boundaries. A boundary is any place data moves between different levels of trust: browser to server, service to service, one tenant to another, user content to a renderer, your code to a vendor API, CI to production, admin tooling to live data. Boundaries are where controls have to live; a flow that crosses none needs no model.
  3. Name the assets concretely, most attractive first. Credentials and tokens, then personal data, then money and entitlements, then the integrity and availability of records. Write the actual table, bucket, queue or environment variable. "User data" names nothing and defends nothing.
  4. Name attacker capability, not attacker identity. What can this adversary already do: an unauthenticated internet caller, a signed-up customer, a neighbouring tenant, someone holding a stolen session, a compromised dependency in the build, an employee with read access to the database. A threat with no capability behind it is fiction and will crowd out real work.
  5. Walk each boundary once against the recurring failure classes — spoofing identity, tampering with data in transit or at rest, actions that cannot be attributed afterwards, disclosure to the wrong party, exhaustion of a shared resource, and gaining privilege you were not granted. One pass per boundary, not per box, or the list explodes.
  6. Write each threat as one sentence: this capability can do this to this asset because this control is missing or weak. If it will not fit that sentence, it is a worry, not a threat. Delete it or find the missing half.
  7. Rank by consequence and reachability, not by ingenuity. A dull unauthenticated read of the whole customer table outranks an elegant chain requiring three prior compromises.
  8. Cut hard, and write down what you are accepting. Most models should end with a few threats worth building against. An accepted risk recorded with its reason is a decision; an unlisted one is an oversight nobody can find later.
  9. Attach each surviving threat to exactly one control, at one place. Name the layer and, where it exists, the file or component. Two controls that can disagree are worse than one in an awkward spot. Say what the control does not cover.
  10. Record the assumptions holding the model up — that this queue is written only by us, that this bucket is private, that this network is not reachable, that this vendor validates its input. Each assumption is something to re-check, and the first thing to test when the model later turns out to have been wrong.
  11. Hand off residual work as findings, not as homework. Threats that need code-level confirmation go to a code review; threats that need a running system go to whoever is authorized to test it. Modeling does not prove anything about the running system.
  12. Re-run on boundary change, not on a calendar. A new public endpoint, a new tenancy model, a new integration or a new data class invalidates the map. A sprint boundary does not.

Proving a threat is a separate, authorized act. Scanning, probing or exploiting a live system — even to confirm something in this model — stops and asks first, names the target, and never runs against production on the strength of a model alone.

Checklist

  • Diagram derived from the actual code, with gaps asked about rather than assumed
  • Every trust boundary marked, including service-to-service and tenant-to-tenant
  • Assets named as concrete stores, ordered by what an attacker wants
  • Attacker capabilities listed; every threat traces to one of them
  • Each boundary walked once against the recurring failure classes
  • Each threat written in the one-sentence form
  • Ranked by consequence and reachability
  • Accepted risks written down with their reason
  • Each surviving threat attached to one control at one place
  • Assumptions listed as things that can go stale
  • No claim made about the running system that only a test could support

Failure handling

  • The list will not stop growing — the boundaries are drawn too finely, or threats are being written without capabilities. Go back to step 4 and drop everything with no attacker behind it.
  • Nobody can say what the data is worth — that is a finding, not a blocker. Model on the strictest plausible reading, say which assumption you used, and name who can settle it.
  • The design has no boundary to defend — say so and stop. A model produced to satisfy a process teaches nothing and costs the next reader real time.
  • Code contradicts the intended design — report the gap as its own finding. Modeling the intended system while the deployed one differs is the most expensive way to be wrong.
  • A threat needs live evidence you are not authorized to gather — record it as unconfirmed with what evidence would settle it. Never upgrade a hypothesis to a vulnerability by reasoning.

Evidence to report

The boundary diagram or its written equivalent, with the files and routes it was derived from; the asset list; the attacker capabilities considered; each threat in sentence form with its rank; what was explicitly accepted and why; each control with its single location; the assumptions; and what remains unconfirmed and what would confirm it. A model with no accepted risks and no unknowns has not been finished, it has been formatted.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Threat Modeling AI skill do?

Work out what is actually worth defending in this system — the trust boundaries data crosses, the assets behind them, what an attacker can already do, and the small number of threats that justify a control. Use when designing or changing a system's shape, before building auth, payments, uploads, file sharing or multi-tenancy, when adding an integration that crosses a boundary, or when asked whether a design is safe. Not for finding bugs in code that already exists (that is secure-code-review), not a compliance questionnaire, and not incident response.

Why use Threat Modeling on TypingMind?

Because you install it once and use it with any model. Threat Modeling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Threat Modeling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/security/threat-modeling. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Threat Modeling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Threat Modeling?

As many as you like. As long as a model supports skills, you can use Threat Modeling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Threat Modeling AI skill free?

Yes. It is published on GitHub by nahid-sparktales under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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