Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add robisson/build-like-amazon-agent-skills --skill threat-modelinggit clone --depth 1 https://github.com/robisson/build-like-amazon-agent-skillsWrote 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/skills/robisson/build-like-amazon-agent-skills/threat-modeling)<a href="https://agentmods.dev/skills/robisson/build-like-amazon-agent-skills/threat-modeling"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/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.
<a href="https://agentmods.dev/skills/robisson/build-like-amazon-agent-skills/threat-modeling"><img src="https://agentmods.dev/badge/skills/robisson/build-like-amazon-agent-skills/threat-modeling.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 | $0.00040 | $0.03880 |
| Opus 5 | $0.00020 | $0.01940 |
| Sonnet 5 | $0.00008 | $0.00776 |
| Haiku 4.5 | $0.00004 | $0.00388 |
Grade B, and why
Threat Modeling scanned grade B with 1 finding 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 10d 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.
Cloud metadata endpointmediumServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
| Instance metadata accessible | SSRF → credential theft via 169.254.169.254 | IMDSv2 (require token); restrict network access to metadata | Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling
Overview
Threat modeling is the systematic identification of security threats to a system BEFORE the system is built. It answers four questions: What are we building? What can go wrong? What are we going to do about it? Did we do a good enough job? You cannot secure a system by adding security after the fact—security must be designed in. Threat modeling is how you design it in.
A threat model is not a penetration test. Penetration testing finds bugs in implementation. Threat modeling finds flaws in design. A perfectly implemented system with a flawed design is still insecure. You do threat modeling at design time; you do pen testing at implementation time. Both are required; neither replaces the other.
When to Use
- During the design phase of any new service or feature
- When adding new data flows (especially customer data or credentials)
- When changing authentication or authorization mechanisms
- When adding new external-facing endpoints (API, web, mobile)
- When integrating with a new third-party service
- When changing trust boundaries (new service-to-service communication)
- When an incident reveals a class of vulnerability (model similar systems)
- Before any Design Bar Raiser review for systems handling sensitive data
Agent Persona
Load agents/security-guardian.md when reviewing the threat model. Use it to challenge trust boundaries, data classification, least privilege, encryption, blast radius, and missing abuse cases.
Amazon Context
Threat models are living documents maintained alongside design documents. They are updated when the system changes, when new threat intelligence emerges, or when incidents reveal gaps. Every system that handles customer data, credentials, or payment information requires a formal threat model reviewed by security teams before launch.
The principle of "blast radius minimization" is paramount: design systems so that a compromise of one component cannot cascade to compromise the entire system. This drives decisions about service boundaries, IAM scope, encryption key separation, and network isolation.
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.
- 10d ago First seen · 276 lines · 40 tokens per session scan B ffe80e776e29
Threat Modeling is a skill published in the GitHub repository robisson/build-like-amazon-agent-skills (15 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 3,880 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (cloud metadata endpoint). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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