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 GongShichen/LuminaCode --skill security-threat-modelgit clone --depth 1 https://github.com/GongShichen/LuminaCodeWrote 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/gongshichen/luminacode/security-threat-model)<a href="https://agentmods.dev/skills/gongshichen/luminacode/security-threat-model"><img src="https://agentmods.dev/badge/skills/gongshichen/luminacode/security-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/gongshichen/luminacode/security-threat-model"><img src="https://agentmods.dev/badge/skills/gongshichen/luminacode/security-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 | $0.00029 | $0.00265 |
| Opus 5.5 | $0.00012 | $0.00106 |
| Sonnet 5.5 | $0.00006 | $0.00053 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
Security 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 2mo 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.
What it actually says
Anchor the model to repository evidence instead of generic checklists. Start by identifying runtime components, entry points, external integrations, stateful stores, credentials, generated artifacts, logs, and privileged operations.
Separate runtime behavior from tests, examples, CI, and developer-only tooling. For each trust boundary, note the protocol or file interface, authentication, authorization, validation, serialization format, and relevant failure modes.
Produce a concise Markdown threat model with:
- scope and assumptions
- architecture summary grounded in files
- assets and attacker capabilities
- trust boundaries and entry points
- prioritized abuse paths with likelihood and impact
- existing controls with evidence
- recommended mitigations tied to concrete components
- open questions that materially affect priority
Prefer a small set of realistic threats over a long checklist. Ask focused questions only when missing deployment, authentication, exposure, or data-sensitivity context would materially change the priorities.
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.
- 2mo ago First seen · 25 lines · 29 tokens per session scan A 9f0650a58127
Security Threat Model is a skill published in the GitHub repository GongShichen/LuminaCode (9 stars, last pushed 4d ago), licensed Apache-2.0. It adds 29 tokens to every session and 265 once invoked, about $0.0001 per session on Opus 5.5. 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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