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 felipenalves/InvOS --skill responder-avaliacoesgit clone --depth 1 https://github.com/felipenalves/InvOSWrote 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/felipenalves/invos/responder-avaliacoes)<a href="https://agentmods.dev/skills/felipenalves/invos/responder-avaliacoes"><img src="https://agentmods.dev/badge/skills/felipenalves/invos/responder-avaliacoes/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/felipenalves/invos/responder-avaliacoes"><img src="https://agentmods.dev/badge/skills/felipenalves/invos/responder-avaliacoes.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 | $0.00110 | $0.01200 |
| Opus 5 | $0.00055 | $0.00600 |
| Sonnet 5 | $0.00022 | $0.00240 |
| Haiku 4.5 | $0.00011 | $0.00120 |
Grade A, and why
responder-avaliacoes 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 11d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/responder-avaliacoes — Respostas pras avaliações do Google
Dependências
- Tom de voz:
_memoria/preferencias.md - Contexto do negócio:
_memoria/empresa.md
Padrão de resposta
Respostas são curtas (1 a 2 frases), pessoais e concretas. Nada de resposta automática de empresa grande.
Regras fixas
- Sempre citar o nome do cliente. Primeiro nome, com capitalização correta (mesmo que o perfil esteja em minúsculo, ex: "jj nascimento" → "JJ"; "alexandre fior" → "Alexandre"). Se o nome for ambíguo ou parecer username, usar um agradecimento genérico caloroso sem forçar o nome.
- Sempre agradecer. Variar: "Obrigado", "Muito obrigado", "Que bom", "Valeu" — pra não parecer robô.
- Frase concreta, não genérica. Puxar algo específico da review ou algo da empresa (produto, processo, cuidado, tradição). Evitar "seu feedback é muito importante pra nós", "estamos sempre à disposição", "agradecemos a preferência".
- Emoji no final — opcional, na maioria das vezes sim. Usar em reviews calorosas/elogiosas. Pular em reviews formais, curtas secas, ou críticas. Nunca mais de 1 emoji.
- Tom: seguir
_memoria/preferencias.md. Sem jargão de marketing. Sem "premium", "qualidade ímpar", "experiência diferenciada".
Exemplos genéricos (adaptar ao negócio)
- Cliente (5★ "Produto excelente, recomendo.") → "Obrigado [Nome]! Saber que o produto agradou é o nosso maior orgulho. 🤩"
- Cliente (5★ "Atendimento ótimo") → "Que bom que gostou do atendimento, [Nome]! Caprichamos em cada detalhe."
- Cliente (5★ sem texto) → "Muito obrigado pelo carinho, [Nome]."
Emojis do repertório
- Calorosos (elogio): 🤩 😊 🙏 ❤️
- Específicos do nicho: depende do negócio — comida 🔥👏🥩, beleza 💅✨ (com cuidado), serviços 👏🙌
- Evitar (tom de marketing genérico): ✨ 🎉 💯 🚀 (a menos que combine com o tom da marca em
_memoria/preferencias.md)
Workflow
Passo 1 — Receber a(s) avaliação(ões)
O usuário vai colar texto, print ou lista de reviews. Extrair pra cada uma:
- Nome do autor
- Nota (estrelas)
- Texto da review (se tiver)
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.
- 11d ago First seen · 101 lines · 110 tokens per session scan A 2531613a11ef
responder-avaliacoes is a skill published in the GitHub repository felipenalves/InvOS (6 stars, last pushed 8d ago), licensed MIT. It adds 110 tokens to every session and 1,200 once invoked, about $0.0006 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 2026-08-31.
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