Polymarket Analysis

Polymarket 预测市场数据分析助手。自动抓取市场数据、热门榜单、赔率变化、情绪指标，生成结构化分析报告。 激活场景： - "Polymarket 分析"、"分析 Polamrket" - "热门市场有哪些"、"最近什么市场最火" - "查看 Polymarket 排行榜" - "Polymarket 市场...

Security Scan

The skill's Polymarket data fetching is coherent, but its billing integration is inconsistent and potentially surprising: a hard-coded SkillPay API key and an import that performs a billing check at import-time create a risk of unexpected network calls/charges and credential misuse.

Purpose & Capability

The network calls to Polymarket (gamma/clob/web) and the provided parsing/analysis code match the stated purpose of fetching and analyzing Polymarket markets. However, an embedded SkillPay billing integration (used to gate execution) is unrelated to pure data analysis and increases the scope of what the skill does.

Instruction Scope

SKILL.md describes SkillPay as optional and gives instructions to edit scripts/skillpay.py to add your own keys, and otherwise focuses on Polymarket scraping. In contrast, scripts/analyze.py imports scripts/skillpay.py and immediately runs billing_check at import time; if billing_check returns False analyze.py exits. That means billing is effectively enforced at runtime despite being described as optional. SKILL.md also references helper tools like extract_content_from_websites, but the Python code performs direct HTTP requests rather than calling those agent tools — a mismatch between the prose instructions and actual behavior.

Install Mechanism

No install spec (instruction-only at registry level) and the skill ships only two Python scripts and a SKILL.md. There is no external archive download or package install specified. This is lower-risk from an install-mechanism perspective.

Credentials

Registry metadata declares no required env vars, but the code reads SKILLPAY_USER_ID from the environment and will call out to skillpay.me using a hard-coded BILLING_API_KEY and SKILL_ID embedded in scripts/skillpay.py. Embedding an API key in the shipped code is a red flag: it can be used to query/charge via SkillPay on behalf of the publisher, and the skill does not declare or justify this credential in the registry metadata. The skill may perform external network calls (polymarket endpoints and skillpay.me) and could cause unexpected charges or leak the SKILLPAY_USER_ID value.

Persistence & Privilege

The skill does not request always:true and does not modify other skills or system settings. Autonomous invocation is allowed by default (platform normal), which combined with the hard-coded billing key increases the blast radius: the skill could be invoked and trigger billing checks/charges without the user explicitly configuring billing credentials.

What to consider before installing

Before installing or enabling this skill, consider the following: (1) The code contains a hard-coded SkillPay API key and SKILL_ID in scripts/skillpay.py — this may allow the publisher's billing account to be used automatically. (2) analyze.py imports and runs the billing check at import time; this can trigger network calls and may prevent the skill from running unless the publisher's billing succeeds. (3) The registry declares no required env vars, but the code reads SKILLPAY_USER_ID from the environment — an undeclared implicit input. Recommended actions: (a) do not install in a production agent until you confirm the publisher and the intended billing behavior; (b) inspect or remove scripts/skillpay.py (or replace the embedded keys with your own) before running; (c) run the skill in an isolated/sandboxed environment to observe network calls (block skillpay.me if you don't want billing); (d) ask the publisher for a homepage, source provenance, and a justification for embedding the API key and requiring billing at import time; (e) if you want only the Polymarket analysis, consider editing analyze.py to remove the billing import/call so it does not contact the billing endpoint.

Polymarket Analysis Skill

功能说明

通过 Polymarket API + 网页抓取，实时获取市场数据并生成分析报告。

数据来源

• 主 API：https://gamma.polymarket.com
• CLOB API：https://clob.polymarket.com
• 官方排行榜：https://polymarket.com/leaderboard
• 分类市场：https://polymarket.com/predictions/{category}

工具使用

首选工具：extract_content_from_websites

• 直接抓取市场页面结构化数据
• 支持所有 Polymarket 官方页面

备用工具：batch_web_search

• 搜索最新市场动态和新闻
• 查询特定市场赔率

工作流程

通用分析流程

1. 确定分析目标 — 用户是问热门市场、特定类别、还是单个市场深度分析？
2. 抓取数据 — 根据目标调用对应 URL
3. 解析关键指标 — 提取赔率(yes/no)、成交量、流动性、参与者数量
4. 生成分析 — 解读数据，给出见解

市场概览

使用 extract_content_from_websites 抓取：

• https://polymarket.com/predictions → 全部热门市场
• https://polymarket.com/predictions/weekly → 本周市场
• https://polymarket.com/leaderboard/overall/weekly/volume → 周榜

分类市场

• /predictions/crypto — 加密货币
• /predictions/trump — Trump 相关
• /predictions/btc — BTC 价格预测
• /predictions/politics — 政治
• /predictions/sports — 体育

单市场深度分析

抓取市场详情页：https://polymarket.com/event/{slug}

提取指标：

字段	含义
yes price	「是」的概率（0~1）
no price	「否」的概率（0~1）
volume	总成交量（USD）
liquidity	流动池大小
end date	市场结束时间
shares	参与者数量

分析框架

热门市场判断标准

1. 成交量 — 越大说明市场越热
2. 流动性 — 决定价格深度和操纵难度
3. 参与者数量 — 参与的人越多，预测越接近群体智慧
4. 距离结算时间 — 越近不确定性越大

情绪判断

• 高赞成率（>80%）+ 低成交量 → 可能是庄家控盘，谨慎
• 赔率剧烈波动 → 市场不确定性强，机会与风险并存
• 长期低成交量 + 高流动性池 → 可能是预热市场

风险提示

• Polymarket 在中国大陆需要 VPN 访问
• 预测市场不构成投资建议
• 智能合约有清算风险，小额试探为主

SkillPay 变现配置（可选）

本 Skill 支持接入 SkillPay 实现每次调用自动扣费。

配置步骤

1. 注册 SkillPay：访问 skillpay.me，使用钱包登录
2. 创建 Skill：在 Dashboard 创建新技能，获取：
   • SKILL_ID（UUID 格式）
   • BILLING_API_KEY（sk_ 开头）
3. 编辑脚本配置：打开 scripts/skillpay.py，替换以下两个值：

BILLING_API_KEY = "sk_your_api_key_here"   # ← 替换这里
SKILL_ID = "your_skill_id_here"             # ← 替换这里

4. 重新打包发布：

python3 /usr/local/lib/node_modules/openclaw/skills/skill-creator/scripts/package_skill.py \
  /workspace/skill-workspace/polymarket-analysis
# 发布
npx clawhub publish /workspace/skill-workspace/polymarket-analysis \
  --slug shenmeng-polymarket-analysis --version 1.1.0

扣费参数

参数	默认值	说明
单次扣费金额	0.001 USDT	可在 skillpay.py 中修改
分成比例	开发者 95%	SkillPay 抽 5%
到账方式	即时到钱包	无需提现

扣费流程

用户发起分析请求
    ↓
skillpay.py 检查余额
    ↓
余额充足 → 自动扣费 → 执行分析 → 返回结果
    ↓
余额不足 → 返回充值链接 → 用户充值后重试

输出格式

分析报告包含：

1. 市场概览 — 成交量、流动性、最热门市场
2. 分类亮点 — 各板块最值得关注的市场
3. 深度分析 — 用户指定市场的详细数据
4. 风险提示 — 注意事项
5. 操作建议 — 针对不同类型用户的参考意见