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 Kaademos/secure-sdlc-agents --skill threat-modelinggit clone --depth 1 https://github.com/Kaademos/secure-sdlc-agentsWrote 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/kaademos/secure-sdlc-agents/threat-modeling)<a href="https://agentmods.dev/skills/kaademos/secure-sdlc-agents/threat-modeling"><img src="https://agentmods.dev/badge/skills/kaademos/secure-sdlc-agents/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/kaademos/secure-sdlc-agents/threat-modeling"><img src="https://agentmods.dev/badge/skills/kaademos/secure-sdlc-agents/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 | $0.00049 | $0.01582 |
| Opus 5 | $0.00024 | $0.00791 |
| Sonnet 5 | $0.00010 | $0.00316 |
| Haiku 4.5 | $0.00005 | $0.00158 |
Grade A, and why
threat-modeling 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 9d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling
Overview
This skill runs a structured threat model against a proposed design or architecture.
It applies STRIDE (and LINDDUN for privacy) to enumerate what can go wrong before
any code exists to exploit. The output is a docs/threat-model.md that every other
agent can reference throughout the SDLC.
The discipline: spec the threats before you write the code. AI agents that skip this step produce features that are locally correct but architecturally broken.
When to Use
- Starting a new feature or service
- Changing authentication, authorisation, or data access patterns
- Adding a third-party integration or external data source
- Designing a new API surface
- Before a penetration test (to scope it correctly)
- When a security incident reveals a design-level gap
Process
Step 1 — Define the scope
Document:
- Feature summary: what it does, for whom, and why
- Data flows: where data enters, where it goes, where it is stored, where it exits
- Trust boundaries: which components trust which other components, and why
- External dependencies: APIs, databases, third-party services, user inputs
Draw or describe a simple data flow diagram. Even ASCII is sufficient.
Step 2 — Enumerate STRIDE threats
For each component and data flow, systematically enumerate threats across all six categories:
| Category | Question to ask |
|---|---|
| Spoofing | Can an attacker pretend to be a legitimate user, service, or system? |
| Tampering | Can data be modified in transit, in storage, or during processing? |
| Repudiation | Can a user deny having performed an action — and would logs prove otherwise? |
| Information Disclosure | Can data be accessed by an unauthorised party or leaked in error messages? |
| Denial of Service | Can an attacker exhaust resources — compute, memory, storage, rate limits? |
| Elevation of Privilege | Can a lower-privilege user or process gain higher-privilege access? |
Produce a threat table:
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.
- 9d ago First seen · 152 lines · 49 tokens per session scan A 40bc625638ae
threat-modeling is a skill published in the GitHub repository Kaademos/secure-sdlc-agents (13 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,582 once invoked, about $0.0002 per session on Opus 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.
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