Skills data-product-pricing-expert

📦

data-product-pricing-expert

Name: data-product-pricing-expert
Author: 图灵数盟

Content revision r1 Safe 📁 Filesystem access⚙️ External commands🌐 Network access

Price Data Products with Defensible Models

Data products often lack clear costs, comparable offers, and stable buyer value. This skill builds evidence-based price ranges, commercial structures, and negotiation plans.

Supports: Claude Codex Code(CC)

🥉 77 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "data-product-pricing-expert" from https://skillstore.io/skills/dhturing-data-product-pricing-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-product-pricing-expert/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.

Markdown detail GET /skills/dhturing-data-product-pricing-expert.md Signed manifest GET /api/skills/dhturing-data-product-pricing-expert/manifest Signed lockfile GET /api/skills/dhturing-data-product-pricing-expert/lockfile

Test it

Using "data-product-pricing-expert". Annual costs are CNY 240,000. Target markup is 25%. Buyer savings are CNY 4 million. Comparable offers range from CNY 280,000 to 360,000.

Expected outcome:

Illustrative cost floor: CNY 300,000, assuming a 25% markup on annual costs.
Value reference: CNY 400,000 at a 10% share of buyer savings.
Recommended range: CNY 320,000 to 360,000, with a CNY 400,000 anchor and CNY 300,000 floor.

Using "data-product-pricing-expert". The API receives seasonal traffic, updates weekly, and serves a buyer seeking low initial commitment.

Expected outcome:

Recommended structure: a base subscription plus tiered usage charges.
The base fee supports recurring update costs, while usage tiers align price with realized demand.
Set volume thresholds from observed usage and review them after the first contract period.

Using "data-product-pricing-expert". An enterprise buyer says the quote is too expensive but will not share a budget.

Expected outcome:

Confirm whether the concern is affordability, uncertain value, scope, or competitive comparison.
Offer a limited pilot or narrower data scope before reducing the unit price.
Trade any concession for volume, term length, prepayment, reduced support, or narrower usage rights.

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

All 12 static findings are false positives caused by documentation paths, Markdown backticks, ordinary website links, and dense Chinese UTF-8 text. The reviewed files contain no executable code, network requests, hidden file operations, encoded payloads, or prompt injection attempts.

Files scanned

455

Lines analyzed

Review items

False positives ignored

Risk Factors

📁 Filesystem access (2)

profile.md:38 profile.md:38

⚙️ External commands (3)

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

🌐 Network access (2)

SKILL.md:6 SKILL.md:12

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex

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Open versioned report

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

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Academic citations (APA · BibTeX · CFF)

APA citation

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

BibTeX citation

@techreport{dhturing-data-product-pricing-expert-2026,
  author = {图灵数盟},
  title = {data-product-pricing-expert security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/dhturing-data-product-pricing-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-product-pricing-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-product-pricing-expert/audits/1"
identifiers:
  - type: other
    value: "skillstore:dhturing-data-product-pricing-expert:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Launch a Data API

Compare cost, buyer value, and market evidence before choosing a launch range and billing model.

Prepare an Enterprise Quote

Create anchor, target, and floor positions with concession rules for a complex buyer discussion.

Review an Existing Price

Assess cost, usage, customer value, and competitive changes before recommending a price adjustment.

Try These Prompts

Estimate a Basic Price Range

I sell [data product] to [buyer]. Costs are [costs], and expected buyer value is [value]. Estimate a price range and state assumptions.

Choose a Pricing Structure

Compare subscription, usage, tiered, and hybrid pricing for [product]. Recommend one model using [usage pattern], [update frequency], and [buyer constraints].

Build a Negotiation Plan

Build anchor, target, and floor prices for [product] using [cost], [buyer value], and [market comparables]. Add decreasing concessions and non-price tradeoffs.

Create an Advanced Pricing Memo

Create a pricing decision memo for [product] across [segments]. Apply cost, value, and market methods. Show sensitivities, compliance risks, evidence gaps, and review triggers.

Best Practices

Label every assumption, source, unit, time period, and confidence range used in the calculation.
Validate buyer value with customer evidence before selecting a value-share percentage.
Review prices every six to twelve months and after material changes in cost, quality, demand, or regulation.

Avoid

Do not use marginal delivery cost alone to price a reusable data product.
Do not present a precise price when buyer value or comparable evidence is weak.
Do not offer unplanned discounts without receiving a defined commercial concession.