スキル cold-email-list-quality
📦
cold-email-list-quality
コンテンツリビジョン r1 安全 ⚙️ 外部コマンド📁 ファイルシステムへのアクセス
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.
対応: Claude Codex Code(CC)
自分のエージェントでインストール
このリクエストをエージェントにコピーしてください。正規の Skill ページとマニフェストが含まれています。
エージェントリクエスト
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.エージェントは引き続き計画を提示し、セキュリティポリシーで必要な確認を求める必要があります。
エージェントが読めるリソース
AI エージェント、クローラー、スクリプトがページ全体ではなく整理されたコンテキストを必要とする場合は、これらのリンクを使ってください。
テストする
「cold-email-list-quality」を使用しています。 Illustrative review: a list includes duplicate emails, signals older than 30 days, and prospects outside configured role targets.
期待される結果:
- 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.
「cold-email-list-quality」を使用しています。 Illustrative review: ICP rules target revenue leaders at Series A or Series B companies; rows include role accounts and plus-addresses.
期待される結果:
- 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.
セキュリティ監査
安全v1 • 2026/10/3 バージョン別レポートを開く
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
スキャンされたファイル
161
解析済み行数
0
レビュー項目
0
誤検知を無視
リスク要因
⚙️ 外部コマンド (26)
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📁 ファイルシステムへのアクセス (3)
最新の完了済み静的・セマンティック監査では、確認済みのセキュリティ検出事項は見つかりませんでした。これは、スキルに副作用がないことを証明するものではありません。
監査者: codex
このレポートを共有・引用
バージョン付き評価レポート、中立的なバッジ、埋め込みカード、引用を共有できます。Skillstore は証拠を報告しますが、この Skill が安全かどうかは判断しません。
レポートリンクをコピー
https://skillstore.io/skills/cmj-hub-cold-email-list-quality/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdownバッジ
[](https://skillstore.io/skills/cmj-hub-cold-email-list-quality?utm_source=security_passport_badge)HTMLバッジ
<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>埋め込みカード
<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>学術引用 (APA · BibTeX · CFF)
APA形式の引用
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形式の引用
@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 スコア
このスコアの理由 証拠の信頼度: 中 55
アーキテクチャ
85
保守性
87
コンテンツ
65
コミュニティ
83
仕様準拠
作成できるもの
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.
これらのプロンプトを試す
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.
ベストプラクティス
- 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.
回避
- 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.
よくある質問
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.