ThreatModel logo

ThreatModel

CommunityPopular
danielmiessler
ThreatModel

Defensive threat modeling and risk management for your own estate — map where sensitive data lives across your asset graph, run compromise scenarios (what a hacked asset exposes, its blast radius, how you'd respond), and maintain a persistent risk register with likelihood×impact scoring, owners, mitigations, and review cadence via a deterministic CLI. All real data stays in your private USER tree; this skill is code only. USE WHEN threat model, threat modeling, risk register, risk assessment, what if X got hacked, compromise scenario, blast radius, sensitive data map, where is our sensitive data, data classification, risk review, add a risk, accept a risk, security risk posture. NOT FOR active pentesting or exploitation (use an offensive-security skill), world-scale futures stress-testing of ideas (use WorldThreatModel), or executing incident response (use your incident-response runbooks — this skill plans them).

Overview

Publisherdanielmiessler
RepositoryLifeOS
Skill nameThreatModel
Stars
19K
Forks
2.5K
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

    Published by danielmiessler on GitHub. Read the source before you install it.

Installation

Install the ThreatModel 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.
  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/danielmiessler/LifeOS.git /tmp/LifeOS
mkdir -p .claude/skills
cp -r /tmp/LifeOS/LifeOS/install/skills/ThreatModel .claude/skills/ThreatModel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable ThreatModel 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 ThreatModel 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 ThreatModel 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.

ThreatModel

Threat modeling for the estate you actually run. Three moves: classify where sensitive data lives, simulate compromise of the assets that hold it, and keep the resulting risks in a register that gets reviewed instead of forgotten.

Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/ThreatModel/

If this directory exists, load and apply PREFERENCES.md (data locations, sensitive-data class priorities, response runbook cross-references). If not, proceed with defaults.

Data/Code Separation (safety gate)

This skill directory is public code. It must never contain data.

  • Every artifact a workflow produces — scenario docs, data classifications, register entries — is written to the private data directory, never into this skill tree.
  • Default data dir: ~/.claude/LIFEOS/USER/SECURITY/THREATMODEL/ (release-excluded USER tree). Override with THREATMODEL_DATA_DIR.
  • Tools/RiskRegister.ts structurally refuses any data dir that resolves inside a skills/ path.
  • Register entries reference credentials by env-var NAME only — never values. No tokens, keys, or cookies anywhere in threat-model output.

Voice Notification

When executing a workflow, do BOTH:

  1. Send voice notification:

    bash
    curl -s -X POST http://localhost:31337/notify \
      -H "Content-Type: application/json" \
      -d '{"message": "Running WORKFLOWNAME in ThreatModel"}' \
      > /dev/null 2>&1 &
  2. Output text notification:

    Running **WorkflowName** in **ThreatModel**...

Workflow Routing

WorkflowTriggerFile
SensitiveDataMap"where is our sensitive data", "data classification", "which assets hold sensitive data"Workflows/SensitiveDataMap.md
CompromiseScenario"what if X got hacked", "compromise scenario", "blast radius of X"Workflows/CompromiseScenario.md
ThreatModelTarget"threat model X", "threat model the estate", "risk assessment of X"Workflows/ThreatModelTarget.md
RiskRegister"risk register", "add a risk", "risk review", "accept risk", "close risk"Workflows/RiskRegister.md

Asset Graph Integration

If the install has Atlas (~/.claude/LIFEOS/ATLAS/Atlas.ts), workflows use it as the current-state source of truth:

bash
bun ~/.claude/LIFEOS/ATLAS/Atlas.ts blast <key>     # what relies on this asset
bun ~/.claude/LIFEOS/ATLAS/Atlas.ts owns <key>      # what deleting/losing it orphans
bun ~/.claude/LIFEOS/ATLAS/Atlas.ts exposed <key>   # which credentials it would leak, priority-ordered
bun ~/.claude/LIFEOS/ATLAS/Atlas.ts sql "SELECT ..."  # read-only census queries

exposed makes the "one hop of trust" step deterministic instead of a judgment call: it returns the credentials an asset holds, transitively through what it owns, compromise-tier first. Pair it with a DEPENDS_ON query for the data stores an asset can reach:

bash
bun ~/.claude/LIFEOS/ATLAS/Atlas.ts sql "SELECT a.kind, a.canonical_key FROM edge e JOIN asset a ON a.id=e.dst WHERE e.kind='DEPENDS_ON' AND e.status='active' AND e.src=(SELECT id FROM asset WHERE canonical_key='<key>')"

Without Atlas, workflows fall back to what the user enumerates plus repo/config inspection — and say so in the output. Never invent an inventory.

Risk Scoring

score = likelihood (1-5) × impact (1-5)Low 1-4 · Medium 5-9 · High 10-14 · Critical 15-25.

Impact is anchored to data classes and blast radius, not vibes: an asset whose compromise exposes credentials or customer data starts at impact 4+. Likelihood is anchored to exposure (public URL, auth boundary, patch state, credential hygiene).

Gotchas

  • The register markdown is a generated view. The JSON store is the system of record; edit via the CLI, never by hand-editing the exported RiskRegister.md — the next export overwrites it.
  • Unclassified ≠ safe. An asset with no sensitive-data tag is unclassified, never clean. Absence of classification is not evidence of absence of data (the absence-metric rule). SensitiveDataMap output must list unclassified assets explicitly.
  • Graph blast radius is derived evidence. Asset-graph queries show what the graph knows; before treating a blast radius as complete for a high-stakes decision, confirm against the provider's authority API (the graph can lag or under-model edges).
  • Scenario impact includes what the asset can REACH, not just what it stores. A box with no data but with credentials/bindings to data-bearing systems inherits their impact. Walk the trust hop with atlas exposed (credentials) plus a DEPENDS_ON query (data stores) before scoring impact; don't eyeball it.
  • Credential concentration is its own finding, not a sum of parts. An asset holding two credential classes that each reach a different domain is worse than the two risks added together, because it collapses a boundary that was supposed to exist. Scenario writing should name concentration explicitly when exposed shows more than one compromise-tier class on one asset.
  • Don't let the register rot. Every risk gets a review_by date at creation; the review command lists overdue ones. A register nobody reviews is worse than none — it manufactures false assurance.

Examples

Example 1: Sensitive data sweep

User: "Which of our assets have sensitive data?"
→ SensitiveDataMap: census the asset graph, classify each data-bearing asset
→ Writes EstateDataMap.md to the private data dir
→ Returns the classified map + explicit unclassified list

Example 2: Compromise scenario

User: "What happens if our analytics worker gets popped?"
→ CompromiseScenario: blast radius via asset graph, data exposed, attacker next-steps,
  detection signals, response plan
→ Scenario doc to private data dir; risks added to register with scores

Example 3: Risk review

User: "Run a risk review"
→ RiskRegister: lists overdue + open risks by score, walks disposition
  (mitigate / accept / close), updates review dates

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 ThreatModel AI skill do?

Defensive threat modeling and risk management for your own estate — map where sensitive data lives across your asset graph, run compromise scenarios (what a hacked asset exposes, its blast radius, how you'd respond), and maintain a persistent risk register with likelihood×impact scoring, owners, mitigations, and review cadence via a deterministic CLI. All real data stays in your private USER tree; this skill is code only. USE WHEN threat model, threat modeling, risk register, risk assessment, what if X got hacked, compromise scenario, blast radius, sensitive data map, where is our sensitive...

Why use ThreatModel on TypingMind?

Because you install it once and use it with any model. ThreatModel 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 ThreatModel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielmiessler/LifeOS/tree/main/LifeOS/install/skills/ThreatModel. 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 ThreatModel?

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 ThreatModel?

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

Is the ThreatModel AI skill free?

Yes. It is published on GitHub by danielmiessler 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇