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Oraclaw Bandit

Name: Oraclaw Bandit
Author: whatsonyourmind

作者 Whatsonyourmind · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

106

总下载

当前安装

版本数

在 OpenClaw 中安装

/install oraclaw-bandit

功能描述

A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data war...

安全使用建议

What to consider before installing or using this skill: - Treat the ORACLAW_API_KEY as a real credential: only provide it after you verify the service and understand how it is used. - Ask the vendor for the oraclaw-mcp source (or an official npm package) and inspect it before running. The SKILL.md tells you to run `npx tsx path/to/oraclaw-mcp/index.ts` — do NOT run that command with an unreviewed path or package. - Verify the endpoints and data flows: what exactly is sent (context vectors, history, user identifiers), where (domain/host), and whether traffic is encrypted. Confirm a privacy policy and data retention rules. - Start with non-sensitive, synthetic test data to observe behavior and costs. The skill charges $0.01/call (USDC on Base) and advertises a free tier; confirm billing details and the account/address used for payments. - Limit the scope of data sent: avoid including PII or secrets in context/history. If the optimization requires user-related signals, ask for a minimal, anonymized feature vector. - If you test and then revoke access, rotate the ORACLAW_API_KEY if you suspect it was misused. - If the provider cannot supply or permit you to audit the oraclaw-mcp code or clearly document API endpoints and data handling, treat the integration as higher-risk and prefer alternatives with transparent implementations.

功能分析

Type: OpenClaw Skill Name: oraclaw-bandit Version: 1.0.0 The oraclaw-bandit skill bundle provides instructions and metadata for an AI agent to perform A/B testing and feature optimization using Multi-Armed Bandit algorithms. The SKILL.md file outlines how to use the optimize_bandit and optimize_contextual tools and provides a configuration template for an MCP server. The instructions are consistent with the stated purpose, and there is no evidence of malicious intent, data exfiltration, or harmful prompt injection.

能力评估

ℹ Purpose & Capability

Name and description align with requiring an API key for an external optimization service. Asking for a single ORACLAW_API_KEY is consistent with a hosted optimization API. Minor oddity: SKILL.md refers to a local/remote 'oraclaw-mcp/index.ts' to be executed via npx, which is not packaged with the skill and is not explained in the metadata.

⚠ Instruction Scope

SKILL.md instructs agents to add an MCP server executed via `npx tsx path/to/oraclaw-mcp/index.ts`. That instruction is vague about where the file comes from and what that MCP server will do. The skill asks agents to send context/history and rewards to the optimization tool (which may include user data or PII) but does not document endpoints, telemetry, or exactly how ORACLAW_API_KEY is used. This grants broad discretion and could result in transmitting sensitive conversational context to an external service.

ℹ Install Mechanism

There is no formal install spec or bundled code (instruction-only), which limits on-disk footprint. However, the instructions encourage running `npx tsx ...` at runtime — npx may fetch packages from npm (or execute remote paths), so following the instruction could cause arbitrary code downloads/exec without a vetted install step.

ℹ Credentials

Only ORACLAW_API_KEY is required and is appropriate for a hosted optimization API. The SKILL.md does not show how the key is used or scoped. Because the skill transmits 'context' and 'history' for contextual bandits, there's a real risk of sending sensitive data along with the API key unless usage is documented and limited.

✓ Persistence & Privilege

Skill does not request always:true, does not ask to modify other skills or system-wide settings, and has no install that would force permanent presence. Default autonomous invocation is allowed but is not by itself a red flag here.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install oraclaw-bandit
安装完成后，直接呼叫该 Skill 的名称或使用 /oraclaw-bandit 触发
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

OraClaw Bandit 1.0.0 — Initial Release - Introduces A/B and feature optimization using Multi-Armed Bandits and Contextual Bandits (LinUCB). - No data warehouse required; works from the first request. - Supports both simple A/B testing and context-aware (personalized) optimization. - Integrates via MCP server with easy JSON-based API. - Includes flexible algorithm options (UCB1, Thompson sampling). - Pay-as-you-go pricing with a free monthly tier.

元数据

Slug oraclaw-bandit

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

Oraclaw Bandit 是什么？

A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data war... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 106 次。

如何安装 Oraclaw Bandit？

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

Oraclaw Bandit 是免费的吗？

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

Oraclaw Bandit 支持哪些平台？

Oraclaw Bandit 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 Oraclaw Bandit？

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