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Discount Optimizer

作者 LeroyCreates · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install discount-optimizer
功能描述
Calculate optimal discount levels based on unit cost, conversion targets, and inventory velocity so promos move product without destroying margin.
使用说明 (SKILL.md)

Discount Optimizer

Promotional discounts are one of the fastest ways to drive volume on TikTok Shop — but cutting price without a margin model turns short-term spikes into long-term losses. This skill calculates mathematically grounded discount levels by working from your unit economics upward, ensuring every promotion moves product while protecting profit.

Use when

  • You are planning a TikTok Shop flash sale or platform-wide campaign and need to set a discount percentage that hits a GMV target without pushing gross margin below a minimum acceptable threshold.
  • You are evaluating whether a stacked promotion — platform voucher, affiliate commission, or creator coupon code — is still profitable after layering all cost components together.
  • You have aging inventory above a target days-on-hand threshold and want to find the minimum effective discount depth to accelerate sell-through before a reorder cycle.
  • You are comparing multiple discount mechanics such as percentage off, bundle pricing, or buy-one-get-one to determine which structure maximizes contribution margin at a given sell-through volume target.

What this skill does

Discount Optimizer takes your unit cost, current list price, minimum acceptable margin percentage, target conversion rate uplift, and inventory velocity data as inputs. It then runs a margin-preserved discount calculation that identifies the maximum allowable discount ceiling, models expected volume uplift using your conversion rate assumptions, and outputs a ranked set of discount scenarios with projected margin impact, break-even sell-through volumes, and a recommended promo price. Each scenario clearly shows the discounted price, resulting margin percentage, estimated units sold at projected conversion uplift, total contribution margin generated, and a break-even comparison against running no promotion. When stacked promotions including platform vouchers, creator commissions, and ad spend cost per order are included as inputs, the skill incorporates all of those cost layers before producing its final recommendation, ensuring nothing is hidden in the math.

Inputs required

  • unit_cost (required): Total landed cost per unit including cost of goods, inbound shipping, and standard platform fees. Example: $8.50 per unit.
  • list_price (required): Current selling price before any discount is applied. Example: $24.99.
  • min_margin_pct (required): Minimum acceptable gross margin percentage you are willing to accept during the promotional period. Example: 20%.
  • inventory_units (optional): Current stock on hand in units. Used to model sell-through scenarios and flag whether discount depth is justified by inventory pressure or not.
  • conversion_rate_baseline (optional): Current baseline conversion rate on the listing before discounting. Used to estimate volume uplift at different discount levels. Example: 2.4%.
  • stacked_costs (optional): Any additional per-unit cost layers such as affiliate commission rate, platform coupon subsidy cost, or blended ad cost per order. Example: 10% affiliate commission plus $1.20 ad cost per order.

Output format

The skill outputs a structured discount scenario table modeling three to five price points from a conservative five-percent discount up to the margin floor. For each scenario the output includes the discounted price, resulting gross margin percentage, estimated units sold at projected conversion uplift, total contribution margin generated, and a break-even comparison against running no promotion. A recommended scenario is highlighted with a plain-English rationale explaining why that discount level best serves the stated goal, whether that goal is margin protection, inventory clearance, or GMV volume maximization. Scenarios where stacked costs push the effective margin below the minimum acceptable threshold are flagged as unprofitable and excluded from the final recommendation so you never accidentally run a money-losing promo.

Scope

  • Designed for: TikTok Shop sellers, ecommerce brand operators, and performance marketing managers who run regular promotional events.
  • Platform: TikTok Shop, Shopify, Lazada, Shopee, and any ecommerce channel where promotional pricing is configurable.
  • Language: English

Limitations

  • Does not have access to real-time platform conversion data — conversion rate uplift assumptions must be provided or estimated by the operator based on historical data.
  • Does not automatically account for halo effects or post-promotion brand lift that may generate additional revenue beyond the promotional window.
  • Margin calculations assume fixed unit costs at current volumes; economies of scale or variable fulfillment costs at significantly higher volumes are not modeled unless explicitly provided as inputs.
安全使用建议
This is an instruction-only pricing calculator that will only be as good as the inputs you provide. Before relying on recommendations: (1) verify the math with a few test cases (use known examples to confirm margin and break-even outputs), (2) remember it cannot fetch live platform conversion or inventory data — you must supply accurate conversion uplift and inventory numbers, (3) stacked costs need to be entered correctly (affiliate rates, coupon subsidies, ad cost per order), and (4) watch for future versions that might request platform credentials (e.g., Shopify or TikTok API keys) before granting them. If you need the skill to pull live data, prefer a version that explicitly declares the required API credentials and review that request carefully.
功能分析
Type: OpenClaw Skill Name: discount-optimizer Version: 1.0.0 The skill bundle contains only metadata and a markdown instruction file (SKILL.md) for a discount optimization tool. There is no executable code, and the instructions are strictly focused on calculating ecommerce margins and promotional pricing without any requests for sensitive data access, network calls, or unauthorized command execution.
能力评估
Purpose & Capability
Name and description match the SKILL.md content. The skill only requires user-provided numeric inputs (unit cost, list price, margin, etc.) and does not declare or request unrelated binaries, credentials, or config paths.
Instruction Scope
SKILL.md contains only algorithmic/analytical instructions and input/output definitions. It does not instruct the agent to read system files, environment variables, or to call external endpoints; it clearly states limitations (no real-time conversion data).
Install Mechanism
No install spec and no code files are present (instruction-only). Nothing will be written to disk or downloaded during install, which matches the simple calculation-focused purpose.
Credentials
The skill declares no required environment variables, credentials, or config paths. This is proportional for a calculation-only skill that operates on operator-supplied inputs.
Persistence & Privilege
Defaults (always: false, user-invocable: true, model invocation permitted) are appropriate. The skill does not request persistent system presence or modify other skills' settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install discount-optimizer
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /discount-optimizer 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release.
元数据
Slug discount-optimizer
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Discount Optimizer 是什么?

Calculate optimal discount levels based on unit cost, conversion targets, and inventory velocity so promos move product without destroying margin. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 127 次。

如何安装 Discount Optimizer?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install discount-optimizer」即可一键安装,无需额外配置。

Discount Optimizer 是免费的吗?

是的,Discount Optimizer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Discount Optimizer 支持哪些平台?

Discount Optimizer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Discount Optimizer?

由 LeroyCreates(@leooooooow)开发并维护,当前版本 v1.0.0。

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