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 navikt/copilot --skill threat-modelgit clone --depth 1 https://github.com/navikt/copilotWrote 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/navikt/copilot/threat-model)<a href="https://agentmods.dev/skills/navikt/copilot/threat-model"><img src="https://agentmods.dev/badge/skills/navikt/copilot/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/navikt/copilot/threat-model"><img src="https://agentmods.dev/badge/skills/navikt/copilot/threat-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 264 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00034 | $0.04160 |
| Opus 5 | $0.00017 | $0.02080 |
| Sonnet 5 | $0.00007 | $0.00832 |
| Haiku 4.5 | $0.00003 | $0.00416 |
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 5d 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 — 492 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Model — STRIDE-A Analysis
Systematic threat identification for NAIS microservices using the STRIDE-A methodology. Produces a data flow diagram, structured threats table, prioritized mitigations, and residual risk summary.
When to Use
- Before launching a new service
- Major architecture changes (new data stores, auth mechanism changes)
- New data flows (especially involving PII)
- External integrations (partner APIs, third-party services)
- API exposure (new public or internal endpoints)
- Regulatory or compliance reviews
- Post-incident analysis to update existing threat models
Step 1: Define Scope
Start by answering these questions to establish the threat model boundary:
Service Identity
- What does the service do? (one sentence — e.g., "Processes dagpenger applications")
- What team owns it?
- What cluster and namespace does it run in? (dev-gcp, prod-gcp)
Data Classification
- What data does the service process?
- PII classification level:
- Strengt fortrolig — FNR, health data, criminal records
- Fortrolig — name, address, phone, email
- Intern — case IDs, team metadata
- Åpen — public statistics, documentation
Consumers and Dependencies
- Who consumes this service? (end users, other services, external partners)
- What does this service depend on? (databases, Kafka topics, upstream APIs)
- What auth mechanisms are in play? (ID-porten, Azure AD, TokenX, Maskinporten)
Deployment Context
- Nais cluster: dev-gcp / prod-gcp
- Ingress type: intern.nav.no (internal) / nav.no (public) / none
- Has egress to external services? Which ones?
Step 2: Data Flow Diagram (DFD)
Map the system using these element types:
Element Types
| Symbol | Element | Example |
|---|---|---|
[External Entity] |
User or external system | [Citizen Browser], [Partner API] |
(Process) |
Your service or component | (dp-soknad), (dp-behandling) |
{Data Store} |
Database, topic, bucket | {PostgreSQL}, {kafka: dp.soknad.v1}, {GCS Bucket} |
--> |
Data flow | [User] --> (API) |
== boundary == |
Trust boundary | == Internet/Ingress == |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed 70bd2db3d455
- 9d ago First seen · 492 lines · 34 tokens per session scan A 347f2167d828
threat-model is a skill published in the GitHub repository navikt/copilot (54 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 4,160 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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