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 brand-researchgit 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/brand-research)<a href="https://agentmods.dev/skills/joaoguirunas/team-os/brand-research"><img src="https://agentmods.dev/badge/skills/joaoguirunas/team-os/brand-research/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/brand-research"><img src="https://agentmods.dev/badge/skills/joaoguirunas/team-os/brand-research.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 | $0.00089 | $0.01474 |
| Opus 5.5 | $0.00036 | $0.00590 |
| Sonnet 5.5 | $0.00018 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00147 |
Grade A, and why
brand-research 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 20d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Research — a marca como ela é, antes da marca como vai ser
Quando usar esta vs. deep-research: esta é o método de pesquisa de marca (auditoria, concorrentes, públicos, benchmarks) que alimenta o reposicionamento. Para pesquisa profunda sobre qualquer outro tema com rastreio de citações, use deep-research.
Reposicionar sem diagnóstico é trocar um problema que você não conhece por outro que você inventou. A pesquisa de marca responde a quatro perguntas, com fonte: o que a marca promete hoje em cada canal (e onde se contradiz), como é percebida por quem está fora, quem ocupa os territórios vizinhos, e como o público fala da categoria com as próprias palavras. Quem pesquisa entrega achados; quem decide o território é outro.
1. Achado vs. recomendação
| É achado (entra) | É recomendação (não entra) |
|---|---|
| "Território {X} sem ocupante claro entre os 6 concorrentes mapeados" | "A marca deveria ocupar o território X" |
| "5 de 7 canais prometem 'agilidade'; o site promete 'profundidade'" | "A promessa deveria ser profundidade" |
| "12 reviews (2026) citam 'caro' — 9 associam a 'vale'" | "Manter o preço premium" |
| "Concorrente A usa azul institucional + serifa; B e C usam preto + sans" | "Ir de cor quente para diferenciar" |
Regra: achado é verificável e datado; recomendação é escolha — e escolha é da strategist. Se a leitura pede uma inferência ("isso sugere que…"), marque-a como leitura, separada do fato.
2. Auditoria da marca atual
2.1 Inventário de pontos de contato
Liste cada canal vivo: site (páginas-chave), perfis sociais (bio + últimos 12 posts), materiais de venda (propostas, decks), e-mail (assinatura, newsletter), apresentações, embalagem/papelaria se houver, marketplace/perfis de terceiros. Para cada um: link + data de acesso + captura (texto limpo via /dev-defuddle, screenshot quando o visual importa).
2.2 Promessa por canal
| Canal | Promessa observada (literal) | Para quem fala | Tom (3 adjetivos) | Visual (cor/tipo/imagem) | Capturado em |
|---|
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.
- 20d ago Changed · +2 lines · -19 tokens per session cd7af2fda399
- 29d ago First seen · 69 lines · 108 tokens per session scan A 514c7f446aff
brand-research is a skill published in the GitHub repository joaoguirunas/team-os (4 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 1,474 once invoked, about $0.0004 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 2026-09-17.
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