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在 OpenClaw 中安装
/install predictclaw-publish-docs-20260320
功能描述
Predict.fun skill with a PolyClaw-style CLI for markets, wallet funding, trading, positions, and hedging.
安全使用建议
This skill appears to implement the CLI it claims. Key things to check before installing or enabling signer-backed modes: 1) Prefer trying read-only mode first (PREDICT_WALLET_MODE=read-only) to validate market/browse functionality without providing secrets. 2) If you enable eoa/predict-account/mandated-vault modes you must supply private keys or vault credentials — only do this if you trust the skill and the host. 3) ERC_MANDATED_MCP_COMMAND is a user-configurable local command the skill will execute for vault operations; verify that command is a trusted binary/script (it can execute arbitrary local actions). 4) Optional hedge analysis may call OpenRouter if you set OPENROUTER_API_KEY — only enable that if you trust the external LLM provider. 5) If you want extra assurance, review the included source (many Python modules are present) or run the skill in an isolated environment/container before using real keys or mainnet endpoints.
功能分析
Type: OpenClaw Skill
Name: predictclaw-publish-docs-20260320
Version: 0.1.11
PredictClaw is a legitimate skill for interacting with the predict.fun platform, providing features for market analysis, trading, and wallet management. The codebase follows good security practices, such as using Pydantic's SecretStr for sensitive credentials and implementing a redaction utility (lib/config.py, lib/api.py) to prevent private keys or tokens from leaking into logs or error messages. While the skill uses subprocesses for CLI routing (scripts/predictclaw.py) and to communicate with a Mandated Vault MCP server (lib/mandated_mcp_bridge.py), these are functional requirements for its stated purpose. No evidence of data exfiltration, intentional prompt injection, or malicious backdoors was found.
能力评估
Purpose & Capability
The name/description (PredictClaw CLI for markets, wallets, trades, hedging) aligns with the files, dependencies, and runtime behavior. Requiring the 'uv' binary is coherent (the SKILL.md uses 'uv sync' / 'uv run'). The presence of code to call predict.fun REST endpoints, manage orders, and handle private-key-based signing is expected for this purpose.
Instruction Scope
The runtime instructions and code legitimately read environment variables and a local .env file and make network calls to predict.fun (and optionally OpenRouter for hedge analysis). However, PredictClaw delegates advanced vault control-plane work to a user-configurable command (ERC_MANDATED_MCP_COMMAND, default 'erc-mandated-mcp') — the skill will invoke whatever launcher you configure. That launcher can execute arbitrary local actions: ensure the command you point at is trustworthy. Otherwise, instructions stay within the stated domain and do not, by default, send data to unknown endpoints.
Install Mechanism
Install uses a single brew formula 'uv' (low-risk, traceable). There are no downloads from arbitrary URLs or extract steps in the manifest. The code is packaged as a Python project; install instructions rely on uv rather than pulling unknown binaries.
Credentials
The SKILL metadata intentionally lists only two universal env vars (PREDICT_ENV, PREDICT_WALLET_MODE) while many other sensitive env vars (PREDICT_PRIVATE_KEY, PREDICT_PRIVY_PRIVATE_KEY, ERC_MANDATED_* keys, OPENROUTER_API_KEY, etc.) are mode-specific. This is explained in SKILL.md, but it means the manifest's minimal env list can underrepresent the sensitive configuration the code accepts. Providing private keys or vault credentials is required for signer-backed modes and is therefore proportionate to the feature set — but you should only supply those secrets if you trust the skill and host.
Persistence & Privilege
always:false (not force-included) and disable-model-invocation:false (normal) — the skill doesn't request elevated platform presence. It does not modify other skills' configs or request system-wide privileges. Note: because the skill can be invoked autonomously (default platform behavior), combining autonomous invocation with provided private keys would increase risk — treat signer-backed modes cautiously.
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 predictclaw-publish-docs-20260320 - 安装完成后,直接呼叫该 Skill 的名称或使用
/predictclaw-publish-docs-20260320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
v0.1.11
Clarify flat OpenClaw metadata vs mode-specific PredictClaw configuration requirements.
元数据
常见问题
Predictclaw Publish Docs 20260320 是什么?
Predict.fun skill with a PolyClaw-style CLI for markets, wallet funding, trading, positions, and hedging. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 133 次。
如何安装 Predictclaw Publish Docs 20260320?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install predictclaw-publish-docs-20260320」即可一键安装,无需额外配置。
Predictclaw Publish Docs 20260320 是免费的吗?
是的,Predictclaw Publish Docs 20260320 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Predictclaw Publish Docs 20260320 支持哪些平台?
Predictclaw Publish Docs 20260320 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 Predictclaw Publish Docs 20260320?
由 walioo(@walioo)开发并维护,当前版本 v0.1.11。
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