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.
Mit meinem Agent installieren
Kopieren Sie diese Anfrage in Ihren Agent. Sie enthält die maßgebliche Skill-Seite und das Manifest.
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.Ihr Agent sollte weiterhin seinen Plan anzeigen und alle von der Sicherheitsrichtlinie verlangten Bestätigungen anfordern.
Agent-lesbare Ressourcen
Verwenden Sie diese Links, wenn ein KI-Agent, Crawler oder Skript sauberen Kontext benötigt, statt die vollständige Seite zu lesen.
Testen
„cold-email-reply-scoring“ wird verwendet. Reply: Please send a calendar link. What is the price? Received 27 minutes after sending. No routing configuration supplied.
Erwartetes Ergebnis:
- 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.
„cold-email-reply-scoring“ wird verwendet. Reply: I am out of office until Monday. Received two minutes after sending.
Erwartetes Ergebnis:
- Suggested category: auto-reply.
- Evidence: explicit out-of-office language and rapid response timing.
- Recommended routing: exclude from normal reply routing.
„cold-email-reply-scoring“ wird verwendet. Reply: Maybe later. No additional conversation context supplied.
Erwartetes Ergebnis:
- 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.
Sicherheitsaudit
SicherAll 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.
Risikofaktoren
⚙️ Externe Befehle (18)
📁 Dateisystemzugriff (3)
Diesen Bericht teilen & zitieren
Teile den versionierten Bewertungsbericht, das neutrale Badge, die Einbettungskarte und Zitate. Skillstore berichtet Nachweise, ohne zu entscheiden, ob dieser Skill sicher ist.
Berichtslink kopieren
https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown-Badge
[](https://skillstore.io/skills/cmj-hub-cold-email-reply-scoring?utm_source=security_passport_badge)HTML-Badge
<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>Einbettungskarte
<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>Wissenschaftliche Zitate (APA · BibTeX · CFF)
APA-Zitat
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/1BibTeX-Zitat
@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"
Skillstore-Score
Warum dieser Score Evidenzvertrauen: MittelWas Sie erstellen können
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.
Diese Prompts ausprobieren
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.
Bewährte Praktiken
- 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.
Vermeiden
- 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.