cold-email-reply-scoring
Classify Cold Email Replies and Prioritize Follow-Up
Cold email reply queues mix buying interest, uncertainty, rejection, and automated responses. This skill provides classification rules and routing guidance, with deterministic execution requiring a separate classifier script.
Instalar com meu Agente
Copie esta solicitação para o seu Agente. Ela inclui a página canônica do Skill e o manifesto.
Review the Skillstore skill "cold-email-reply-scoring" from https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring.md and its manifest at https://skillstore.io/api/skills/cmj-hub-cold-email-reply-scoring/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Seu Agente ainda deve apresentar o plano e solicitar qualquer confirmação exigida pela política de segurança.
Recursos legíveis por agentes
Use estes links quando um agente de IA, crawler ou script precisar de contexto limpo em vez de ler a página completa.
Testar
Usando "cold-email-reply-scoring". Reply: Please send a calendar link. What is the price? Received 27 minutes after sending. No routing configuration supplied.
Resultado esperado:
- Suggested category: buy-signal.
- Evidence: explicit calendar and pricing requests.
- Numeric confidence: unavailable without the classifier.
- Recommended routing: request the brand routing configuration before selecting a destination.
Usando "cold-email-reply-scoring". Reply: I am out of office until Monday. Received two minutes after sending.
Resultado esperado:
- Suggested category: auto-reply.
- Evidence: explicit out-of-office language and rapid response timing.
- Recommended routing: exclude from normal reply routing.
Usando "cold-email-reply-scoring". Reply: Maybe later. No additional conversation context supplied.
Resultado esperado:
- Suggested category: neutral, pending review.
- Evidence: delayed interest without a commitment.
- Review required: the short reply lacks enough context for confident interpretation.
- Numeric confidence: unavailable without the classifier.
Auditoria de segurança
SeguroAll 21 static findings are false positives involving Markdown delimiters or fixed project-relative references. No evidence found of prompt injection, credential theft, or unauthorized data transfer in SKILL.md. The referenced classifier and supporting dependencies are absent from the audited package, so their behavior and safety remain unverified.
Fatores de risco
⚙️ Comandos externos (18)
📁 Acesso ao sistema de arquivos (3)
Compartilhar e citar este relatório
Compartilhe o relatório de avaliação versionado, o badge neutro, o cartão incorporável e as citações. A Skillstore apresenta evidências sem decidir se este Skill é seguro.
Copiar link do relatório
https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportBadge Markdown
[](https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring?utm_source=security_passport_badge)Badge HTML
<a href="https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/cmj-hub-cold-email-reply-scoring/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Cartão incorporável
<iframe src="https://skillstore.io/embed/skills/cmj-hub-cold-email-reply-scoring.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Citações acadêmicas (APA · BibTeX · CFF)
Citação APA
cmj-hub. (2026). cold-email-reply-scoring security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring/audits/1Citação BibTeX
@techreport{cmj-hub-cmj-hub-cold-email-reply-scoring-2026,
author = {cmj-hub},
title = {cold-email-reply-scoring security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring/audits/1},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "cold-email-reply-scoring security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
- name: "cmj-hub"
date-released: "2026-10-03"
url: "https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring/audits/1"
identifiers:
- type: other
value: "skillstore:cmj-hub-cold-email-reply-scoring:audit:1"
description: "Skillstore immutable audit report identifier"
Pontuação do Skillstore
Por que esta pontuação Confiança das evidências: MédioO que você pode criar
Prioritize Sales Replies
Identify explicit meeting and pricing requests, then recommend follow-up using the supplied brand routing rules.
Review a Reply Queue
Apply the documented batch workflow when dependencies are available, separating automated replies and uncertain classifications.
Check Campaign Responses
Distinguish interest, delayed decisions, and rejection while retaining evidence for human review.
Teste estes prompts
Classify this reply using the documented categories: [reply]. Explain the matched signals. Do not invent a numeric score without the classifier.
Assess [reply], received [minutes] after sending, using [routing rules]. Explain the recommended category and route. Report any unavailable dependencies.
Review [replies] for ambiguity, automated responses, and unsubscribe requests. Explain category candidates and flag uncertain cases for human review.
With the reviewed classifier and dependencies available, score [local batch file] using [routing rules]. Summarize results and flag confidence below 0.6.
Boas práticas
- Verify the referenced classifier and supporting files before executing the batch command.
- Supply reply timing and brand routing rules, and remove unnecessary personal information from shared examples.
- Review uncertain classifications manually and honor explicit unsubscribe requests.
Evitar
- Presenting illustrative confidence values or accuracy claims as verified classifier results.
- Assuming routing recommendations automatically send messages or create CRM records.
- Automatically routing ambiguous or non-English replies without human review.