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Polymarket Autopilot Experimental

作者 mauonga · GitHub ↗ · v0.1.1
cross-platform ⚠ suspicious
374
总下载
0
收藏
2
当前安装
2
版本数
在 OpenClaw 中安装
/install polymarket-autopilot-experimental
功能描述
Skill sperimentale per l’analisi automatica di mercati pubblici Polymarket con simulazione paper trading, controllo dei costi LLM e report in italiano con mi...
安全使用建议
This skill describes behavior that needs LLM API access (OpenAI and Anthropic) and precise token/cost accounting, but the published metadata lists no credentials — that's the main red flag. Before installing: 1) Ask the publisher which environment variables or provider integrations the skill expects and why they were omitted from the metadata. 2) If you supply API keys, create low-quota or billing-limited keys (or use separate sandbox accounts) so accidental cost is limited. 3) Confirm the platform/agent can enforce the stated frequency (1 run / 3 days) and the hard budget stop (2 € / week); if enforcement depends solely on the agent following instructions, it may fail. 4) Request clarification on the concrete method for token accounting and what happens when budget is exceeded. 5) Monitor activity and logs on first runs, and do not provide wallet/private keys. Absence of code and scanner findings reduces install risk, but the credential mismatch and vague stopping rules justify caution.
功能分析
Type: OpenClaw Skill Name: polymarket-autopilot-experimental Version: 0.1.1 The skill explicitly states multiple strong constraints against malicious behavior, including 'no real transactions', 'no wallets', 'read-only access only', and 'works exclusively on public data'. It also details mechanisms for LLM cost control and self-limitation. There are no instructions for data exfiltration, unauthorized execution, persistence, or prompt injection attempts to bypass these constraints. All stated objectives and operational logic in SKILL.md align with a benign, experimental paper-trading simulation.
能力评估
Purpose & Capability
The SKILL.md explicitly requires using OpenAI and Anthropic for analysis and for tracking token usage/costs. The registry metadata, however, declares no required environment variables, no primary credential, and no config paths. A skill that calls external LLMs would normally require API keys or declared credentials; the absence of these declarations is an incoherence.
Instruction Scope
Most runtime instructions stay within the stated purpose (read-only public Polymarket data, paper trading simulation, limited outputs). However the instructions require: (a) calling two LLM providers (OpenAI and Anthropic), (b) tracing token consumption and estimating euro costs, and (c) enforcing per-execution frequency and a weekly budget limit. Those items grant the agent broad discretion (e.g., "ridurre la complessità"), and the SKILL.md gives no precise enforcement mechanism — e.g., how token accounting is measured, which API keys are used, or what happens if the platform cannot reliably stop execution when budget is exceeded.
Install Mechanism
This is an instruction-only skill with no install spec and no code files — lowest installation risk. Nothing is downloaded or written to disk by an install step.
Credentials
The skill requires access to external LLM services and must track usage for cost control, but it declares no environment variables or credentials. That is disproportionate/unreported: at minimum you should expect API keys/tokens for OpenAI and Anthropic (or equivalent provider integrations). Also, implementing reliable budget enforcement may require access to billing/usage APIs or platform-level quotas — none of which are declared.
Persistence & Privilege
No 'always: true', no install, no write-to-disk steps described, and the skill does not request persistent privileges or modifications to other skills. Autonomous invocation is allowed by default (normal) but not elevated here.
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 polymarket-autopilot-experimental
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /polymarket-autopilot-experimental 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
v0.1.1
Bugfix publish / retry Windows
v0.1.0
- Prima versione sperimentale della skill per l’analisi automatica di mercati pubblici Polymarket con simulazione di paper trading. - Accesso solo in modalità read-only; nessun uso di wallet, transazioni reali o denaro reale. - Analisi e simulazione con controllo rigoroso dei costi LLM (budget 2 € a settimana, massimo una esecuzione ogni 3 giorni). - Output dettagliato in italiano: risultati simulati, costi, commento e mini-riassunto. - Progettata per test autonomi a basso budget, anche da utenti non esperti di mercati. - Arresto automatico se il budget viene superato o se il valore simulato non giustifica i costi.
元数据
Slug polymarket-autopilot-experimental
版本 0.1.1
许可证
累计安装 2
当前安装数 2
历史版本数 2
常见问题

Polymarket Autopilot Experimental 是什么?

Skill sperimentale per l’analisi automatica di mercati pubblici Polymarket con simulazione paper trading, controllo dei costi LLM e report in italiano con mi... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 374 次。

如何安装 Polymarket Autopilot Experimental?

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

Polymarket Autopilot Experimental 是免费的吗?

是的,Polymarket Autopilot Experimental 完全免费(开源免费),可自由下载、安装和使用。

Polymarket Autopilot Experimental 支持哪些平台?

Polymarket Autopilot Experimental 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 Polymarket Autopilot Experimental?

由 mauonga(@mauonga)开发并维护,当前版本 v0.1.1。

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