スキル data-cost-attribution-expert

📦

data-cost-attribution-expert

Name: data-cost-attribution-expert
Author: 图灵数盟

コンテンツリビジョン r1 安全 📁 ファイルシステムへのアクセス⚙️ 外部コマンド🌐 ネットワークアクセス

Build Auditable Data Resource Cost Schedules

Organizations often lack a consistent method to identify and support data-resource costs. This skill structures classification, stage assessment, allocation, and evidence planning.

対応: Claude Codex Code(CC)

🥉 77 ブロンズ

Maintain this Skill? Claim it 🥉 77 ブロンズ

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 "data-cost-attribution-expert" from https://skillstore.io/skills/dhturing-data-cost-attribution-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-cost-attribution-expert/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。

Markdown詳細 GET /skills/dhturing-data-cost-attribution-expert.md 署名済み manifest GET /api/skills/dhturing-data-cost-attribution-expert/manifest 署名済み lockfile GET /api/skills/dhturing-data-cost-attribution-expert/lockfile

テストする

「data-cost-attribution-expert」を使用しています。 Classify payroll, cloud hosting, feasibility research, and staff training for a customer data platform.

期待される結果:

Development payroll: direct labor, subject to time records and the capitalization start date.
Development cloud hosting: direct expense when usage is traceable to the platform.
Feasibility research: research-stage expense, not capitalized.
General staff training: excluded from the data-resource cost base.

「data-cost-attribution-expert」を使用しています。 Choose an allocation basis for one engineering team supporting three data products.

期待される結果:

Use approved labor hours when work logs reliably identify effort by product. Document exceptions, review rates monthly, and reconcile allocated hours to payroll.

「data-cost-attribution-expert」を使用しています。 List the evidence needed to support a capitalization start date.

期待される結果:

Approved project initiation or technical feasibility decision.
Evidence of intended completion and planned use or sale.
Resource and budget approvals.
A reliable method for measuring development expenditure.

セキュリティ監査

安全

v1 • 2026/9/27 バージョン別レポートを開く

All 12 static findings are false positives caused by Markdown paths, inline code formatting, public website links, and normal Chinese-language text. The reviewed files contain no executable commands, active network access, obfuscated payloads, or prompt injection.

スキャンされたファイル

402

解析済み行数

レビュー項目

誤検知を無視

リスク要因

📁 ファイルシステムへのアクセス (2)

profile.md:38 profile.md:38

⚙️ 外部コマンド (3)

SKILL.md:20 SKILL.md:26 SKILL.md:33

🌐 ネットワークアクセス (2)

SKILL.md:6 SKILL.md:12

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

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バージョン別レポートを開く

レポートリンクをコピー

https://skillstore.io/skills/dhturing-data-cost-attribution-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdownバッジ

[![Skillstore security assessment](https://skillstore.io/badges/skills/dhturing-data-cost-attribution-expert/security.svg)](https://skillstore.io/skills/dhturing-data-cost-attribution-expert?utm_source=security_passport_badge)

HTMLバッジ

<a href="https://skillstore.io/skills/dhturing-data-cost-attribution-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-cost-attribution-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

埋め込みカード

<iframe src="https://skillstore.io/embed/skills/dhturing-data-cost-attribution-expert.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

图灵数盟. (2026). data-cost-attribution-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-cost-attribution-expert/audits/1

BibTeX形式の引用

@techreport{dhturing-data-cost-attribution-expert-2026,
  author = {图灵数盟},
  title = {data-cost-attribution-expert security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/dhturing-data-cost-attribution-expert/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: "data-cost-attribution-expert security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "图灵数盟"
date-released: "2026-09-27"
url: "https://skillstore.io/skills/dhturing-data-cost-attribution-expert/audits/1"
identifiers:
  - type: other
    value: "skillstore:dhturing-data-cost-attribution-expert: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

アーキテクチャ

保守性

コンテンツ

コミュニティ

仕様準拠

作成できるもの

Prepare a cost inventory

Map ledger items to cost categories, exclusions, periods, and supporting records before closing the accounts.

Define project allocation rules

Choose a consistent basis for splitting shared team, cloud, and service costs across data projects.

Review capitalization evidence

Test stage boundaries, capitalization conditions, calculations, and document references before formal assurance work.

これらのプロンプトを試す

Classify basic costs

Classify these data-project expenses into the four cost categories or exclusions: [expense list]. Explain each decision and identify missing evidence.

Assess project stages

Review this project timeline: [timeline]. Separate research and development periods, test the five capitalization conditions, and list evidence needed for each conclusion.

Design an allocation method

Compare labor hours, data volume, usage frequency, and benefit ratios for [shared costs]. Recommend a basis, formula, controls, and consistency rules.

Plan an audit-ready attribution

Create an end-to-end cost attribution plan for [data resource]. Include scope, stage gates, collection tables, formulas, evidence indexes, checks, and unresolved judgments.

ベストプラクティス

Define the cost object, capitalization dates, currency, and reporting period before collecting records.
Prefer direct attribution and use documented, consistent allocation methods only for shared costs.
Link every included amount to source evidence and record unresolved judgments separately.

回避

Do not capitalize research, routine maintenance, training, advertising, or unsupported management costs.
Do not use valuation estimates as substitutes for actual cost in initial measurement.
Do not change allocation methods to increase a preferred capitalization amount.

よくある質問

Does this skill provide an official accounting opinion?

No. It supports analysis and documentation, but qualified professionals must approve accounting treatment and assurance conclusions.

Can all data-team payroll be capitalized?

No. Only supported development work may qualify, subject to stage, participation, measurement, and applicable accounting requirements.

Which allocation method is best?

Direct attribution is preferred. For shared costs, choose the method that best represents consumption and can be applied consistently.

Are invoices required for every cost?

External purchases generally need contracts and invoices. Payroll can use payroll, social insurance, assignment, and time records.

Can earlier costs be reconstructed?

They may be analyzed, but recognition depends on effective dates, reliable records, and applicable rules against retrospective adjustment.

What should I provide first?

Provide the resource scope, project timeline, approvals, ledger details, payroll, time records, contracts, invoices, cloud bills, and current accounting policy.