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 security-reviewgit 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/security-review)<a href="https://agentmods.dev/skills/navikt/copilot/security-review"><img src="https://agentmods.dev/badge/skills/navikt/copilot/security-review/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/security-review"><img src="https://agentmods.dev/badge/skills/navikt/copilot/security-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Output Handling · line 148 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
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 | $0.00024 | $0.01872 |
| Opus 5 | $0.00012 | $0.00936 |
| Sonnet 5 | $0.00005 | $0.00374 |
| Haiku 4.5 | $0.00002 | $0.00187 |
Grade A, and why
security-review 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Review Skill
This skill provides pre-commit and pre-PR security checks for Nav applications. Covers secret scanning, vulnerability scanning, and Nav-specific requirements.
For architecture questions, threat modeling, or compliance decisions, use @security-champion instead.
Automated Scans
Run with run_in_terminal:
# Scan repo for known vulnerabilities and secrets
trivy repo .
# Scan Docker image for HIGH/CRITICAL CVEs
trivy image <image-name> --severity HIGH,CRITICAL
# Scan GitHub Actions workflows for insecure patterns
zizmor .github/workflows/
# Quick search for secrets in git history
git log -p --all -S 'password' -- '*.kt' '*.ts' | head -100
git log -p --all -S 'secret' -- '*.kt' '*.ts' | head -100
Parameterized SQL (Never Concatenate)
// ✅ Correct – parameterized query
fun findBruker(fnr: String): Bruker? =
jdbcTemplate.queryForObject(
"SELECT * FROM bruker WHERE fnr = ?",
brukerRowMapper,
fnr
)
// ❌ Wrong – SQL injection risk
fun findBrukerUnsafe(fnr: String): Bruker? =
jdbcTemplate.queryForObject(
"SELECT * FROM bruker WHERE fnr = '$fnr'",
brukerRowMapper
)
No PII in Logs
// ✅ Correct – log correlation ID, not PII
log.info("Behandler sak for bruker", kv("sakId", sak.id), kv("tema", sak.tema))
// ❌ Wrong – never log FNR, name, or other PII
log.info("Behandler sak for bruker ${bruker.fnr}") // GDPR violation
log.info("Navn: ${bruker.navn}") // GDPR violation
Secrets from Environment, Never Hardcoded
// ✅ Correct – read from environment (Nais injects via Secret)
val dbPassword = System.getenv("DB_PASSWORD")
?: throw IllegalStateException("DB_PASSWORD mangler")
// ❌ Wrong – hardcoded secret
val dbPassword = "supersecret123"
Network Policy (Nais)
Only expose what must be exposed:
spec:
accessPolicy:
inbound:
rules:
- application: frontend-app # only explicitly named callers
outbound:
rules:
- application: pdl-api
namespace: pdl
cluster: prod-gcp
external:
- host: api.external-service.no # only if strictly necessary
What ships with it
1 file 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 bcb38bec7d38
- 9d ago First seen · 255 lines · 24 tokens per session scan A d6a6bbddfc7f
security-review is a skill published in the GitHub repository navikt/copilot (54 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 1,872 once invoked, about $0.0001 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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