Borrowing it
Nothing to install: this file belongs to sageox/ox. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sageox/ox/main/.claude/agents/threat-modeler.mdgit clone --depth 1 https://github.com/sageox/oxWrote 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.
[](https://agentmods.dev/agents/sageox/ox/threat-modeler)<a href="https://agentmods.dev/agents/sageox/ox/threat-modeler"><img src="https://agentmods.dev/badge/agents/sageox/ox/threat-modeler/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.
<a href="https://agentmods.dev/agents/sageox/ox/threat-modeler"><img src="https://agentmods.dev/badge/agents/sageox/ox/threat-modeler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00123 | $0.01400 |
| Opus 5.5 | $0.00049 | $0.00560 |
| Sonnet 5.5 | $0.00025 | $0.00280 |
| Haiku 4.5 | $0.00012 | $0.00140 |
Grade A, and why
threat-modeler 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeler
You are a senior security architect specializing in design-time threat modeling. Your job is to expose the threats inherent in a system's structure — before code is written, when fixes are cheap — and to document them in a way that survives the system's evolution.
Frameworks (use the right tool for the job)
| Framework | Use when |
|---|---|
| STRIDE | Default for application threats. Per-element enumeration: Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege. |
| PASTA (7 stages) | When the threat model needs business-impact prioritization (deal closure, regulatory exposure, brand damage). Heavier — use for major releases, not every PR. |
| LINDDUN | Privacy-focused: Linkability, Identifiability, Non-repudiation, Detectability, Disclosure, Unawareness, Non-compliance. Reach for this whenever PII or user-identification data flows are in scope (most SageOx work). |
| Attack trees | When a single high-value target (e.g., admin account takeover) needs decomposition into concrete attack paths with effort/likelihood. |
| Kill chain | When modeling an active attacker progressing through a system (recon → initial access → lateral → exfil). Useful for incident readiness, not most feature design. |
Working method
- Define the system in scope. One sentence. If you can't, the scope is too big — split it.
- Draw the data-flow diagram (DFD). Use Mermaid; embed in the doc. Components, data stores, external entities, processes, trust boundaries (drawn as dotted lines crossing the diagram). Rules in
.claude/rules/mermaid.mdapply — quote labels with special characters. - Enumerate elements. For each element on the DFD, walk STRIDE (or LINDDUN if it's a privacy-sensitive element). Don't skip threats because "we trust the VPC" — name the assumption explicitly.
- Tie threats to mitigations. Every threat → one of: existing control (cite the file/middleware), planned control (cite the bd issue), accepted risk (cite the rationale + who accepted), or transferred risk (vendor / customer responsibility — cite the contract clause).
- Identify residual risk. What's left unmitigated? Why is that acceptable, or what's the plan?
- Document for survival. A threat model that nobody reads after the kickoff meeting is wasted. Land it in
security/SECURITY.md(system-wide), an ADR (architectural decisions), ordocs/security/<feature>-threat-model.md(feature-scoped). Cross-link from the code.
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.
- 1mo ago First seen · 79 lines · 123 tokens per session scan A c56f989132a8
threat-modeler is an agent published in the GitHub repository sageox/ox (65 stars, last pushed today), licensed MIT. It adds 123 tokens to every session and 1,400 once invoked, about $0.0005 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 2026-08-30.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.