Threat Model Sidecar logo

Threat Model Sidecar

Organization
alpha-omega-security
threat-model-sidecar

Emit and validate the §1.19 machine-readable companions: threat-model.yaml using schema threat-model-sidecar/v2, and the flat threat-model.json export conforming to schema.json. USE WHEN an orchestrated threat model is ready for publication, automated or AI-assisted triage, dependency compatibility analysis, or companion regeneration after prose changes. Projects provenance- backed components, input obligations, contract dimensions, outputs, adversaries, dependencies, configurations, properties, misuses, non-findings, and the closed disposition enum. Prose remains canonical; authority order is prose > yaml > json. DO NOT USE FOR: writing prose or routing a finding.

Overview

Publisheralpha-omega-security
Repositorythreat-model
Skill namethreat-model-sidecar
Stars
60
Forks
10
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by alpha-omega-security on GitHub. Read the source before you install it.

Installation

Install the Threat Model Sidecar 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/alpha-omega-security/threat-model/tree/main/skills/threat-model-sidecar
  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/alpha-omega-security/threat-model.git /tmp/threat-model
mkdir -p .claude/skills
cp -r /tmp/threat-model/skills/threat-model-sidecar .claude/skills/threat-model-sidecar
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threat Model Sidecar 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 Model Sidecar 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 Model Sidecar 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 Model — Sidecar (machine-readable companions)

Owns §1.19: emit threat-model.yaml and threat-model.json alongside the prose document so shared triage tooling can consume the model without parsing prose. Follow the schema in sidecar-schema.md for the YAML and the mapping in json-report-schema.md for the JSON exactly — companions are only useful to tooling if they are structurally uniform across projects. Authority order: prose > yaml > json. The JSON is a flat, lossy export for consumers of schema.json; it is never a triage input.

Principles

  • Prose is canonical; the sidecar is a derived index. Do not put anything in the sidecar that is not already asserted in the prose. If the two disagree, the sidecar is wrong.
  • Record provenance of derivation — set prose_version to the canonical relative prose path plus SHA-256 of its exact UTF-8 bytes, and regenerate whenever the prose changes.
  • Uniform shape only — use the schema: threat-model-sidecar/v2 fields as given; extend with project-specific keys only under an x- prefix. The JSON schema forbids extensions entirely; what does not fit stays in the YAML.
  • Never upgrade provenance. The JSON collapses four provenance kinds into two: documented and maintainer become documented; inferred and assumption become inferred. The collapse only goes down — a record whose sidecar provenance is inferred or assumption must never surface in the JSON as documented. That direction hands a JSON-only consumer a licence the model never granted.

Procedure

  1. Confirm the prose document is at least a complete draft (all sections substantive or N/A). If §1.7/§1.8/§1.11/§1.17 are incomplete, stop and hand back — the sidecar cannot be faithfully derived from a partial model.
  2. Project each prose section into its sidecar block:
    • §1.2/§1.3/§1.4 → components (scope: in|out, out-reason, and per- component reachability precondition).
    • §1.5 → host_side_effects[] — explicit present/absent/conditional host effects with components, conditions, and provenance.
    • §1.7 → entry_points[].parameters[] — every attacker-controllable parameter must have a non-empty caller_must_enforce and at least one value in its control_kinds array.
    • §1.7-§1.12 → contract_dimensions[] — all eight required dimensions for every in-scope component, including explicit not-applicable rows; claimed/disclaimed rows reference stable property IDs and unresolved rows reference §1.18 question IDs.
    • §1.8 → outputs[] (taint: same-as-input | sanitized | constrained, in-scope component, separately provenanced invariants, and downstream must-not-assume records).
    • §1.10 → adversaries[] (in/out of scope, capabilities, excluded capabilities, goals, provenance).
    • §1.9 → dependency_policy + dependencies[], including stable relied_on_properties, acknowledged obligation IDs, adversary capabilities actually forwarded, and output channels/taint handling; an empty list plus zero_runtime_dependencies: true is the explicit zero-dependency claim, not an omission. Leave outputs_consumed: [] unless the project holds an output-sanitization-kind property about a dependency's output — each entry's supports_property_id must reference such a property. If the project passes a dependency's output straight through and disclaims sanitization (the common case), the list stays empty; the passthrough taint is already recorded in §1.8 outputs[] and the §1.12 disclaimers. Do not point supports_property_id at a behavioral, atomicity, or probabilistic property.
    • §1.6 → build_policy + build_flags[] (default, security_relevant, support stance, affected property IDs/effects, provenance).
    • §1.11 → properties_claimed[] (kind, components, tier, conditions, violation symptoms, provenance).
    • §1.12 → properties_disclaimed[] (components, conditions, false_friend: true|false, provenance).
    • §1.13 → downstream_responsibilities[] linked to obligation/property IDs.
    • §1.14 → known_misuses[], the structured basis for VALID-HARDENING.
    • §1.15 → known_non_findings[], with component/sink, conditions, and discharged_by stable IDs sufficient for exact (not fuzzy) matching.
    • §1.17 → dispositions plus disposition_precedence — the fixed closed enum and first-match order, verbatim.
  3. Normalize the prose status to the schema enum (draft, unratified-draft, under-review, accepted) using the mapping in sidecar-schema.md; set confidence to match the §1.1 counts exactly. Project the header's triage policy to the top-level triage_policy (strict default / relaxed). Carry tier (security-critical | correctness-only) on every properties_disclaimed[] entry so a consumer can enforce the assumption security-critical floor. Project the §1.1 generation metadata to the top-level generation block (model, effort, plugins[]); omit the block only when the prose header records a fully human-authored model.
  4. Project the validated YAML to threat-model.json per json-report-schema.md:
    • Collapse provenance downward: documented/maintainer → documented, inferred/assumption → inferred. A JSON block built from many sidecar records takes the weakest of the set. Collapse confidence the same way: documented + maintainer, inferred + assumption.
    • Emit all nine schema disposition values, verbatim. OUT-OF-MODEL: dependency-contract has no JSON value; a JSON-only consumer falls through to model_gap, which escalates. Do not invent a label for it.
    • Flatten entry_points to one row per (entry point × parameter). An attacker_controllable: conditional row must carry a non-empty condition.
    • For each known non-finding, name the covered in-scope components and the discharged symptom in why_safe, and point cites at the discharging entry in the same document (properties_provided[3] style). The JSON drops the sidecar's components and symptom fields, so why_safe is the only place left to carry the scope.
    • Record repository, commit (git rev-parse HEAD in the modeled tree), date, scope_subpath, and a short §1.2 description.

Validation gate

Reject (and hand back) if any of these fail:

  • confidence equals the header's draft-confidence count.
  • model_status is the normalized schema value for the prose status.
  • prose_version has the required path-plus-SHA-256 form and its digest matches the prose bytes.
  • Every attacker-controllable input operand has a non-empty caller_must_enforce; explicit none — <property ID> means safe handling is project-owned, while every actual caller obligation has a stable ID.
  • Every parameter has a non-empty control_kinds array containing only valid values.
  • Every in-scope component × each required contract dimension is present exactly once, claimed/disclaimed property IDs resolve, and every unresolved row cites a §1.18 question ID.
  • Every closure-driving component, parameter trust decision, output invariant, adversary, dependency, build policy/flag, property, responsibility, misuse, and non-finding has provenance sufficient to enforce the closure constraint for every model status and triage policy.
  • dispositions is exactly the closed enum — no project-specific dispositions invented (a finding fitting none is MODEL-GAP, a prose revision, not a new label).
  • disposition_precedence equals §1.17's canonical first-match order.
  • Every properties_claimed[] entry has a tier and at least one violation_symptom; every claimed/disclaimed property and contract row has provenance.
  • Every dependency reliance, caller-obligation acknowledgement, output invariant, configuration effect, responsibility, and non-finding discharge reference resolves to a stable ID.
  • An accepted model has zero inferred and zero assumption claims; a model with any inferred or assumption record remains under-review or unratified-draft.
  • triage_policy is strict or relaxed (defaulting to strict when the header is silent); every assumption provenance record carries a question_id, and every properties_disclaimed[] entry carries a tier.
  • The generation block matches the §1.1 generation metadata (model, effort, and the plugins[] actually used), or is absent only when the prose records a fully human-authored model.
  • No key outside the schema except under an x- prefix.

For threat-model.json:

  • Validates against schema.json.
  • dispositions is exactly the nine schema values, verbatim.
  • Provenance is never upgraded — no record whose sidecar provenance is inferred or assumption appears as documented.
  • confidence is the collapsed sidecar count: documented + maintainer and inferred + assumption.
  • Every known_non_findings[] entry's why_safe names the in-scope components it covers and the symptom it discharges, and its cites resolves to the row of the claim that discharges it — a real but unrelated index does not pass.
  • commit is the real sha of the modeled tree, not a placeholder.

Output

threat-model.md, threat-model.yaml, and threat-model.json together at the modeled root (inside the subdirectory for a scoped run), plus a one-line note of the prose_version the YAML was derived from for the orchestrator's finalize gate. The JSON carries no prose binding — only commit and date — one more reason it is an export, not the model.

Frequently asked questions

What does the Threat Model Sidecar AI skill do?

Emit and validate the §1.19 machine-readable companions: threat-model.yaml using schema threat-model-sidecar/v2, and the flat threat-model.json export conforming to schema.json. USE WHEN an orchestrated threat model is ready for publication, automated or AI-assisted triage, dependency compatibility analysis, or companion regeneration after prose changes. Projects provenance- backed components, input obligations, contract dimensions, outputs, adversaries, dependencies, configurations, properties, misuses, non-findings, and the closed disposition enum. Prose remains canonical; authority order...

Why use Threat Model Sidecar on TypingMind?

Because you install it once and use it with any model. Threat Model Sidecar 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 Model Sidecar in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alpha-omega-security/threat-model/tree/main/skills/threat-model-sidecar. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Threat Model Sidecar?

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 Model Sidecar?

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

Is the Threat Model Sidecar AI skill free?

Yes. It is published on GitHub by alpha-omega-security 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 👇