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Prediction Market Arbiter

作者 kingmadellc · GitHub ↗ · v1.1.5 · MIT-0
cross-platform ⚠ suspicious
340
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0
收藏
1
当前安装
4
版本数
在 OpenClaw 中安装
/install prediction-market-arbiter
功能描述
Cross-platform divergence scanner comparing Kalshi and Polymarket prices on identical events. Fuzzy title matching across 1000+ markets per run, configurable...
安全使用建议
This skill appears to implement a legitimate Kalshi↔Polymarket divergence scanner, but there are metadata/instruction mismatches you should address before use: - The SKILL.md and script read a per-user config (~/.openclaw/config.yaml) and a Kalshi private key PEM file, but the registry metadata does not declare any required config paths or credentials. Treat the private_key_file as highly sensitive: do not point it at unrelated keys (SSH, cloud credentials, etc.). - Verify where the script will write cache/JSON files (current working dir or a logged path) and run it in an isolated environment (VM/container) if you are unsure. - Inspect the kalshi-python package (version pinned in requirements.txt) yourself, and install dependencies in a virtualenv rather than globally. - Consider adding the Kalshi credentials via a dedicated Kalshi key (not reuse of other private keys) and confirm the key format before supplying the path. - Because the skill metadata omits the config file requirement, ask the publisher (or update metadata locally) to declare the required config path and the sensitive file access so you can assess it fully. If you cannot verify these items, treat the skill as untrusted and avoid supplying high-value secrets or system-wide keys.
功能分析
Type: OpenClaw Skill Name: prediction-market-arbiter Version: 1.1.5 The skill bundle is a legitimate tool for scanning price divergences between Kalshi and Polymarket. The code in `scripts/arbiter.py` correctly implements the described fuzzy matching logic and interacts only with official API endpoints (kalshi.com and polymarket.com). While it handles sensitive Kalshi API credentials, it does so locally as required for authentication without any signs of exfiltration or unauthorized execution.
能力评估
Purpose & Capability
The code and SKILL.md match the described purpose: fetching Kalshi and Polymarket markets, fuzzy-matching titles, and reporting divergences. Requesting a Kalshi API key + private PEM file is proportionate to accessing the Kalshi API. However, the registry metadata lists no required config paths or required env vars while the SKILL.md and code expect a user config file (~/.openclaw/config.yaml) and a private key file — this metadata omission is an inconsistency.
Instruction Scope
The SKILL.md and scripts instruct the agent to read a user config file (default: ~/.openclaw/config.yaml) and to read a private key PEM file from a path provided in that config. Those file reads are not declared in the registry metadata. The instructions also cache results to JSON (writing to disk) and make network calls to Kalshi and Polymarket. There is no instruction to validate that the provided private key file is indeed a Kalshi key or to avoid pointing it at unrelated sensitive files (e.g., SSH keys).
Install Mechanism
There is no automated install spec (no brew/npm/download), which reduces install-time risk. The repo includes requirements.txt and requests the user to pip-install packages (kalshi-python, requests, pyyaml) in SKILL.md. That is a common, moderate-risk pattern; no unusual or remote download URLs are present.
Credentials
Access to Kalshi credentials (api_key_id + private_key_file) is required by the SKILL.md and code, which is reasonable for this purpose — but the skill metadata declares no required env vars or config paths. The private_key_file is a sensitive credential stored as a file path: the skill will read any file the user points it to, so a malicious or accidental pointer could expose other secrets. The number and type of secrets requested are not excessive for the stated purpose, but they are not declared in the metadata and there is no guidance to prevent misuse of arbitrary key files.
Persistence & Privilege
always is false (good). The skill reads a per-user config (~/.openclaw/config.yaml) and caches results to JSON; it does not request to persist as always-enabled or to modify other skills. The ability to run autonomously (disable-model-invocation=false) is platform-default; combined with the undeclared config access this increases the need for caution but is not itself a definitive red flag.
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 prediction-market-arbiter
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /prediction-market-arbiter 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
v1.1.5
migrate to Kalshi SDK v3 — fix constructor crash
v1.1.0
v1.1.0: unified stack release
v1.0.1
fix: cache schema standardized, prices as 0-1 floats
v1.0.0
Prediction Market Arbiter 1.0.0 — Initial release - Cross-platform divergence scanner comparing Kalshi and Polymarket prices on matching event markets - Fuzzy Jaccard-based title matching across 1000+ markets for robust event pairing - Configurable thresholds for price divergence, minimum volume, and match quality - Automatic detection and alerting of arbitrage opportunities and market mispricings - Zero API cost; results cached for programmatic access - Integrates with the OpenClaw Prediction Market Trading Stack
元数据
Slug prediction-market-arbiter
版本 1.1.5
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 4
常见问题

Prediction Market Arbiter 是什么?

Cross-platform divergence scanner comparing Kalshi and Polymarket prices on identical events. Fuzzy title matching across 1000+ markets per run, configurable... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 340 次。

如何安装 Prediction Market Arbiter?

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

Prediction Market Arbiter 是免费的吗?

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

Prediction Market Arbiter 支持哪些平台?

Prediction Market Arbiter 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 Prediction Market Arbiter?

由 kingmadellc(@kingmadellc)开发并维护,当前版本 v1.1.5。

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