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 Srajangpt1/ai-security-crew --skill threat-modelgit clone --depth 1 https://github.com/Srajangpt1/ai-security-crewWrote 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/srajangpt1/ai-security-crew/threat-model)<a href="https://agentmods.dev/skills/srajangpt1/ai-security-crew/threat-model"><img src="https://agentmods.dev/badge/skills/srajangpt1/ai-security-crew/threat-model/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/srajangpt1/ai-security-crew/threat-model"><img src="https://agentmods.dev/badge/skills/srajangpt1/ai-security-crew/threat-model.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 | $0.00153 | $0.01353 |
| Opus 5 | $0.00077 | $0.00677 |
| Sonnet 5 | $0.00031 | $0.00271 |
| Haiku 4.5 | $0.00015 | $0.00135 |
Grade A, and why
threat-model 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 11d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perform a threat model for the following feature or component:
$ARGUMENTS
What to do
Produce a developer-focused threat model. If no description was provided in the arguments, ask:
- What are you building? (feature name + description)
- What tech stack is involved?
- Do you have code snippets, data flows, or architecture notes to analyze?
Write threats in plain language — concrete attack scenarios a developer would understand, not abstract security categories. Every threat should link to specific evidence from the provided context.
Step 1 — Understand the Feature
Extract:
- Feature/component name — what is this?
- Description — what does it do, what problem does it solve?
- Tech stack — languages, frameworks, databases, cloud services
- Data touched — credentials, PII, payment data, tokens, internal config, etc.
- System boundaries — what calls this? what does it call? external vs. internal?
- Trust model — who are the actors? (users, admins, anonymous, third-party services)
Step 2 — Identify Attack Surfaces
Scan for:
- Authentication endpoints (login, registration, password reset, OAuth callbacks)
- File upload / download handlers
- External API integrations (third-party services, webhooks, callbacks)
- Admin / privileged operations
- Data exports or bulk operations
- Cross-tenant operations in multi-tenant systems
- Unauthenticated or public endpoints
- Async jobs or background workers that consume external data
And sensitive data patterns:
- Credentials and secrets (passwords, API keys, tokens, private keys)
- PII (names, emails, phone numbers, addresses, SSNs)
- Financial data (card numbers, account numbers, transaction history)
- Health data (PHI, medical records, diagnoses)
- Internal configuration or infrastructure details
Step 3 — Generate Threats
Consider these attack dimensions:
- Spoofing — Can an attacker impersonate a user, service, or system?
- Tampering — Can data be modified in transit or at rest without detection?
- Repudiation — Can users deny actions due to missing audit trails?
- Information Disclosure — Can sensitive data leak through errors, logs, or responses?
- Denial of Service — Can an attacker exhaust resources or disrupt availability?
- Elevation of Privilege — Can a low-privilege user gain higher access?
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.
- 11d ago First seen · 145 lines · 153 tokens per session scan A 73c999fd93d1
threat-model is a skill published in the GitHub repository Srajangpt1/ai-security-crew (68 stars, last pushed 4mo ago), licensed MIT. It adds 153 tokens to every session and 1,353 once invoked, about $0.0008 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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