技能 cold-email-list-quality
📦
cold-email-list-quality
內容修訂 r1 安全 ⚙️ 外部命令📁 檔案系統存取
Score and Clean Prospect Lists
Duplicates, stale signals, and targeting mismatches waste outreach capacity. This skill guides CSV or JSONL list reviews with a weighted rubric, removal reasons, and prioritized cleanup recommendations.
支援: Claude Codex Code(CC)
使用我的智慧代理安裝
將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。
智能體請求
Review the Skillstore skill "cold-email-list-quality" from https://skillstore.io/skills/cmj-hub-cold-email-list-quality.md and its manifest at https://skillstore.io/api/skills/cmj-hub-cold-email-list-quality/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。
Agent 可讀資源
當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。
測試它
正在使用「cold-email-list-quality」。 Illustrative review: a list includes duplicate emails, signals older than 30 days, and prospects outside configured role targets.
預期結果:
- Illustrative score: 72/100. Deduplication: 16/20; role fit: 18/25; freshness: 10/20; address heuristics: 12/15; exclusions: 8/10; company stage: 8/10.
- Keep the oldest record for each duplicate email. Review near-duplicates manually.
- Refresh stale signals and review prospects outside role targets before import. Mailbox deliverability remains unverified.
正在使用「cold-email-list-quality」。 Illustrative review: ICP rules target revenue leaders at Series A or Series B companies; rows include role accounts and plus-addresses.
預期結果:
- Flag role-account addresses for removal under the documented B2B heuristic.
- Warn on plus-addresses without claiming they are invalid mailboxes.
- Compare inferred funding stages and revenue roles with the configured targets. Report uncertain company stages for review.
安全審計
安全v1 • 2026/10/3 開啟版本化報告
All 31 static findings are false positives: Markdown delimiters, fixed repository-relative paths, and list-quality terminology do not demonstrate the detected threats. No evidence found of credential theft, data exfiltration, prompt injection, or reconnaissance in the reviewed skill. The documented Python script is absent, so its execution safety cannot be assessed.
1
已掃描檔案
161
分析行數
0
複核項目
0
已忽略的誤報
風險因素
⚙️ 外部命令 (26)
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📁 檔案系統存取 (3)
最近一次完成的靜態與語意稽核未偵測到已確認的安全發現。這並不能證明該 Skill 沒有副作用。
審計單位: codex
分享與引用此報告
分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。
複製報告連結
https://skillstore.io/skills/cmj-hub-cold-email-list-quality/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown 徽章
[](https://skillstore.io/skills/cmj-hub-cold-email-list-quality?utm_source=security_passport_badge)HTML 徽章
<a href="https://skillstore.io/skills/cmj-hub-cold-email-list-quality?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/cmj-hub-cold-email-list-quality/security.svg" alt="Skillstore security assessment" loading="lazy"></a>嵌入卡片
<iframe src="https://skillstore.io/embed/skills/cmj-hub-cold-email-list-quality.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>學術引用 (APA · BibTeX · CFF)
APA 引用
cmj-hub. (2026). cold-email-list-quality security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/cmj-hub-cold-email-list-quality/audits/1BibTeX 引用
@techreport{cmj-hub-cmj-hub-cold-email-list-quality-2026,
author = {cmj-hub},
title = {cold-email-list-quality security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/cmj-hub-cold-email-list-quality/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-list-quality 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-list-quality/audits/1"
identifiers:
- type: other
value: "skillstore:cmj-hub-cold-email-list-quality:audit:1"
description: "Skillstore immutable audit report identifier"
Skillstore 評分
評分依據 證據置信度: 中 55
架構
85
可維護性
87
內容
65
社群
83
規範符合度
你可以打造什麼
Prepare a New Prospect List
Review an imported CSV or JSONL list for duplicates, address concerns, and records outside campaign requirements.
Refresh Weekly Outreach Data
Review signal age, role fit, exclusions, and company stage before refreshing the weekly campaign list.
Audit ICP Alignment
Compare supplied prospects with ideal customer profile rules and prioritize changes to targeting or list sourcing.
試試這些提示
Score My List
Review prospects.csv using the documented quality rubric. Report axis scores, removal candidates, and top fixes. State missing fields and avoid assuming mailbox deliverability.
Find Duplicate Records
Review prospects.csv for duplicate emails, LinkedIn URLs, and person-company matches. Keep the oldest email record and newest LinkedIn signal. Flag near-duplicates for review.
Apply ICP Rules
Score prospects.csv against brand-config.json. Check roles, exclusions, signal dates, address heuristics, and inferred company stages. Explain assumptions and separate warnings from removal recommendations.
Prepare a Campaign List
Audit prospects.jsonl using brand-config.json and supplied warm-up status. Report scores, cleanup priorities, and send-volume arithmetic. Preserve the original. Write reviewed cleaned and removed CSV files.
最佳實務
- Provide current ICP configuration, dated signals, and complete prospect fields; identify missing information before accepting scores.
- Preserve the original list and review removal reasons and output destinations before writing cleaned files.
- Confirm mailbox deliverability separately and refresh stale signals before campaign import.
避免
- Treating a high quality score as proof of deliverability or permission to contact prospects.
- Executing the documented Python example when the referenced scoring script is absent.
- Automatically deleting near-duplicates or treating plus-address warnings as proof of invalid mailboxes.
常見問題
Which file formats are supported?
The instructions cover CSV and JSONL. Email is the minimum field; roles, companies, signals, dates, and LinkedIn URLs enable more checks.
What does the score measure?
Weights are deduplication 20, role fit 25, freshness 20, address heuristics 15, exclusions 10, and company stage 10.
Does this skill verify email deliverability?
No. It checks address shape, domain categories, role accounts, and plus-address patterns. It does not confirm mailbox existence.
Can it use my ICP rules?
Yes. Supplied brand configuration provides role targets, exclusions, segment information, and signal anchors. Missing fields limit targeting confidence.
Is the scoring script included?
No. The release note defers the deterministic script to a future release. Current scoring follows instructions interpreted by the assistant.
What outputs should I expect?
Expect scores, removal reasons, and prioritized fixes. The workflow specifies cleaned and removed CSV files for review; it does not send messages.