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 joaoguirunas/team-os --skill security-threat-modelinggit clone --depth 1 https://github.com/joaoguirunas/team-osWrote 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/joaoguirunas/team-os/security-threat-modeling)<a href="https://agentmods.dev/skills/joaoguirunas/team-os/security-threat-modeling"><img src="https://agentmods.dev/badge/skills/joaoguirunas/team-os/security-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/joaoguirunas/team-os/security-threat-modeling"><img src="https://agentmods.dev/badge/skills/joaoguirunas/team-os/security-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 | $0.00079 | $0.01008 |
| Opus 5.5 | $0.00032 | $0.00403 |
| Sonnet 5.5 | $0.00016 | $0.00202 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
security-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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modelagem de ameaças
O objetivo não é um documento bonito: é uma lista curta de requisitos testáveis que entram como critério de aceite nas stories antes de o código existir. Modelo que não vira requisito não protegeu nada.
Quando modelar
Feature nova ou mudança em: login/cadastro/sessão · papéis e permissões · pagamento · dado pessoal (lead, cliente, documento) · upload de arquivo · webhook ou integração de terceiro · chamada a modelo de IA com tools ou com dado do usuário · painel administrativo · API pública.
Mudança de texto, estilo ou página estática sem formulário: não precisa.
Passo 1 — Ativos (o que vale proteger)
| Ativo | Onde mora | Quem deveria acessar |
|---|---|---|
| ex.: leads (nome, e-mail, WhatsApp) | tabela leads |
só o painel interno |
Passo 2 — Fronteiras de confiança
Desenhe o caminho do dado e marque cada ponto onde ele muda de dono de confiança:
navegador (não confiável) → rota de API / server action → banco (RLS) → storage
→ provedor de IA (prompt) → tools (ações reais)
→ webhook de terceiro (não confiável) → rota de servidor
Em projeto Next.js + Supabase, as fronteiras típicas: tudo que roda no navegador (inclusive variável NEXT_PUBLIC_); a anon key + RLS; rotas de servidor com service_role; Storage; webhooks; chamada ao modelo.
Passo 3 — STRIDE em cada fronteira
| Letra | Pergunta | Controle típico |
|---|---|---|
| Spoofing | Alguém se passa por outro usuário ou sistema? | sessão do provedor de auth; assinatura de webhook |
| Tampering | Alguém altera dado que não é dele? | RLS com auth.uid(); validação no servidor; WITH CHECK |
| Repudiation | Alguém nega ter feito uma ação sensível? | log com requestId e userId em ação crítica |
| Information disclosure | Alguém lê o que não devia? | RLS de leitura; nada de segredo no cliente; erro sem stack trace |
| Denial of service | Alguém derruba ou encarece o serviço? | rate limit em login, formulário e rota de IA; limite de tamanho |
| Elevation of privilege | Alguém ganha papel que não tem? | papel em app_metadata ou tabela, nunca user_metadata; tools da IA com confirmação |
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 · 68 lines · 79 tokens per session scan A cbcca54ba05e
security-threat-modeling is a skill published in the GitHub repository joaoguirunas/team-os (4 stars, last pushed 2d ago), licensed MIT. It adds 79 tokens to every session and 1,008 once invoked, about $0.0003 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 2026-10-08.
Other skills, from other repositories
jsonl-log-analyzer
Analyze large JSONL log files using schema-aware partitioned analysis. Discovers field schema, generates tailored jq extraction recipes, and orchestrates parallel chunk analysts with synthesis. Use when processing JSONL logs exceeding context limits, performing log analytics, or investigating incident logs.
orchestration-patterns
Apply proven orchestration patterns for agent teams including parallel specialists, pipelines, swarms, research+implementation, plan approval, and multi-file refactoring. Use when choosing a team structure, designing workflows, or implementing specific coordination patterns.
task-system
Manage shared task lists for agent teams including creating tasks, setting dependencies, claiming work, and tracking progress. Use when creating work items, building task pipelines, coordinating task ownership, or managing task dependencies.
spawn-backends
Configure how teammate agents run including in-process, tmux, and iTerm2 backends. Use when choosing a display mode, setting up split panes, troubleshooting backend issues, or configuring teammateMode.
error-handling
Debug and recover from agent team errors including common errors, hooks for quality gates, known limitations, and recovery strategies. Use when encountering team errors, enforcing quality gates with hooks, understanding limitations, or debugging agent issues.
messaging
Send messages between agents using SendMessage including direct messages, broadcasts, shutdown requests/responses, and plan approvals. Use when communicating between agents, understanding message formats, or handling structured protocol messages.