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dynamic-pricing-intelligence-agent（动态定价智能 Agent）

Agent Skill

dynamic-pricing-intelligence-agent 用于查找、检索和筛选相关信息，适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态，以及是否会触发联网、命令执行或文件读写。

总安装

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周安装

561

GitHub Stars

下载量

7,228

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安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

来源数

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手，先确认命令和权限，再让它执行。

请帮我安装这个 Agent Skill：dynamic-pricing-intelligence-agent（动态定价智能 Agent）
来源仓库：https://github.com/serendipityoneinc/apiclaw-skills
仓库路径：skills/dynamic-pricing-intelligence-agent
安装命令：
npx skills add https://github.com/serendipityoneinc/apiclaw-skills --skill 'Dynamic Pricing Intelligence Agent'
安装前请先检查当前环境是否支持对应 CLI，并向我确认将要执行的命令、安装目录、联网范围和文件读写权限；确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill；本站只展示命令，不托管安装包，也不自动执行。

skills.shnpx skills

npx skills add https://github.com/serendipityoneinc/apiclaw-skills --skill 'Dynamic Pricing Intelligence Agent'

简介

动态定价智能 Agent 提供基于 ASIN 的商品定价建议，支持批量分析并自动识别品类。

适用于电商运营场景，可输出 RAISE/HOLD/LOWER 策略及市场数据支撑。
通过输入 ASIN 获取定价建议，无需关键词，系统自动检测品类和竞品信息。
需配置 APICLAW_API_KEY，首次使用需引导用户获取免费密钥并验证权限范围。
dynamic-pricing-intelligence-agent 属于研究检索类 Skill，可作为该场景下的辅助能力补充。

SKILL.md

Dynamic Pricing Intelligence Agent — RAISE / HOLD / LOWER

Give me your ASIN(s). I'll tell you whether to raise, hold, or lower — with data.

Files

Script: {skill_base_dir}/scripts/apiclaw.py — run --help for params
Reference: {skill_base_dir}/references/reference.md (field names & response structure)

Credential

Required: APICLAW_API_KEY. Get free key at apiclaw.io/api-keys

Input

Required: one or more ASINs (your products). No keyword needed — category is auto-detected.
Optional: competitor_asins

On first interaction, tell user: "Give me your ASIN(s). I support single or batch analysis — I'll auto-detect each product's category and analyze the pricing landscape for you."

Auto Category Detection (CRITICAL — replaces manual keyword input)

For each ASIN: product --asin {asin} → extract bestsellersRank array
The last entry in bestsellersRank = leaf (most specific) category
Use leaf category name → categories --keyword "{leaf_category_name}" → get categoryPath
If categories returns empty, try the second-to-last BSR entry, or ask user
Batch mode: group ASINs by leaf category → share market data within same category (saves credits)

API Pitfalls

Revenue = sampleAvgMonthlyRevenue directly. NEVER calculate price×sales.
Sales = monthlySalesFloor (lower bound)
Price in realtime: buyboxWinner.price, NOT top-level price
All keyword-based endpoints MUST include --category once categoryPath is locked
FBA fees from products/search are estimates — verify with Amazon FBA calculator
Aggregation endpoints without categoryPath produce severely distorted data

Pricing Signal Logic

Signal	Condition
RAISE	Price below opportunity band AND rating ≥ category avg AND BSR stable/rising
HOLD	Price in optimal band AND BSR stable AND no competitor price war
LOWER	Price above hottest band AND BSR declining OR competitor undercut detected

New Seller Price Band Selection

Don't pick highest-sales band. Calculate per band: Sales/Competition Ratio = Avg Monthly Sales ÷ Avg Review Count Highest ratio = best entry point (strong demand + low review barriers).

Profit Simulation

3 scenarios: Conservative (current price), Moderate (±$1-2), Aggressive (±$3-5). Per scenario: Revenue = Price × Est. Sales − FBA Fee − Referral Fee (15%) − COGS = Net Profit & Margin.

Profit Margin Interpretation

Net Margin	Signal	Interpretation
>30%	🟢 Healthy	Strong margin, room for ad spend and promotions 📊
15-30%	🟡 Acceptable	Viable but monitor costs closely 🔍
5-15%	🟠 Thin	One price war or cost increase away from loss 🔍
<5%	🔴 Unsustainable	Must raise price, cut costs, or exit 💡

Price Position Analysis

Price < opportunity band min: Underpriced — likely leaving money on the table if rating ≥ category avg 🔍
Price in opportunity band: Optimal zone — hold unless competitors shift 🔍
Price in hottest band: Maximum volume zone — high competition, margin pressure likely 🔍
Price > hottest band max: Premium positioning — only viable with strong brand/reviews 🔍
DB price ≠ Realtime price (>5% diff): Likely running a promotion or coupon — flag as temporary 📊

Output

Respond in user's language.

Per ASIN: Price Signal (RAISE/HOLD/LOWER) → Current Position in Category → Price Band Heatmap (with Sales/Competition Ratio) → Competitor Price Map (top 10 in leaf category) → 30-Day Trend → Profit Simulation (3 scenarios) → BuyBox Analysis → Recommended Price.

End with: Data Provenance → API Usage. Flag DB vs Realtime discrepancies as likely promotions.

Language (required)

Output language MUST match the user's input language. If the user asks in Chinese, the entire report is in Chinese. If in English, output in English. Exception: API field names (e.g. monthlySalesFloor, categoryPath), endpoint names, technical terms (e.g. ASIN, BSR, CR10, FBA, credits) remain in English.

Disclaimer (required, at the top of every report)

Data is based on APIClaw API sampling as of [date]. Monthly sales (monthlySalesFloor) are lower-bound estimates. This analysis is for reference only and should not be the sole basis for business decisions. Validate with additional sources before acting.

Confidence Labels (required, tag EVERY conclusion)

📊 Data-backed — direct API data (e.g. "current price $12.99 📊")
🔍 Inferred — logical reasoning from data (e.g. "price is below opportunity band 🔍")
💡 Directional — suggestions, predictions, strategy (e.g. "consider raising to $14.99 💡")

Rules: Strategy recommendations and price signals (RAISE/HOLD/LOWER) are NEVER 📊. User criteria override AI judgment.

Data Provenance (required)

Include a table at the end of every report:

Data	Endpoint	Key Params	Notes
(e.g. Market Overview)	`markets/search`	categoryPath, topN=10	📊 Top N sampling, sales are lower-bound
...	...	...	...

Extract endpoint and params from _query in JSON output. Add notes: sampling method, T+1 delay, realtime vs DB, minimum review threshold, etc.

API Usage (required)

Endpoint	Calls	Credits
(each endpoint used)	N	N
Total	N	N

Extract from meta.creditsConsumed per response. End with Credits remaining: N.

API Budget

Single ASIN: ~20-25 credits
Batch N ASINs (same category): ~20-25 + 1 per additional ASIN
Batch N ASINs (different categories): ~20-25 per unique category

适合场景

用户想查找某类 Agent Skill 时

需要根据任务场景推荐可安装能力包时

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明，方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用；具体调用方式请以来源页面和 README 为准。

平台分布

Codex

39.01%

按下载量换算2,820

Claude

27.84%

按下载量换算2,012

Cursor

18.33%

按下载量换算1,325

Gemini CLI

9.36%

按下载量换算677

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可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API；具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息，不托管安装包，不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果，不能写成本站确认安全。当前只有一个来源，正式发布前建议补源仓库或其他目录站核验。

来源信息

GitHub 仓库

github.com

/serendipityoneinc/apiclaw-skills

SKILL.md 来源

github.com

/serendipityoneinc/apiclaw-skills/tree/main/skills/dynamic-pricing-intelligence-agent

skills-sh

skills.sh

/serendipityoneinc/apiclaw-skills/dynamic-pricing-intelligence-agent

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