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.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and 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.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "cold-email-reply-scoring". Reply: Please send a calendar link. What is the price? Received 27 minutes after sending. No routing configuration supplied.
Expected outcome:
- 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.
Using "cold-email-reply-scoring". Reply: I am out of office until Monday. Received two minutes after sending.
Expected outcome:
- Suggested category: auto-reply.
- Evidence: explicit out-of-office language and rapid response timing.
- Recommended routing: exclude from normal reply routing.
Using "cold-email-reply-scoring". Reply: Maybe later. No additional conversation context supplied.
Expected outcome:
- 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.
Security Audit
SafeAll 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.
Risk Factors
⚙️ External commands (18)
📁 Filesystem access (3)
Share & cite this report
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Copy report link
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>Embed card
<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>Academic citations (APA · BibTeX · CFF)
APA citation
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 citation
@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
Why this score Evidence Confidence: MediumWhat You Can Build
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.
Try These 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.
Best Practices
- 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.
Avoid
- 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.