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在 OpenClaw 中安装
/install crypto-self-learning
功能描述
Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy.
安全使用建议
Install only if you want an agent to keep local crypto trade records and persist learned rules. Review what it writes to MEMORY.md, keep trade logs free of secrets or exchange credentials, and prefer running memory updates only after explicit user approval.
功能分析
Type: OpenClaw Skill
Name: crypto-self-learning
Version: 1.0.0
The OpenClaw skill 'crypto-self-learning' is designed for local crypto trade analysis and rule generation. All scripts (`analyze.py`, `generate_rules.py`, `log_trade.py`, `update_memory.py`) operate on local JSON files within the skill's `data` directory. The `SKILL.md` instructions and the `update_memory.py` script's modification of `MEMORY.md` are directly aligned with the stated purpose of updating the agent's learned rules, without any evidence of prompt injection, data exfiltration, remote execution, or other malicious behaviors. Required binaries (`jq`, `python3`) are standard and appropriate for the task.
能力评估
Purpose & Capability
Local trade logging, analysis, rule generation, and learned-rule updates fit the stated crypto self-learning purpose. The supplied telemetry does not show wallet access, live trading authority, credential use, remote execution, or exfiltration.
Instruction Scope
The skill instructs the agent to read and write local files, including trade records and MEMORY.md. That is purpose-aligned, but users should understand that running the memory update changes future guidance.
Install Mechanism
No separate install-time behavior, package installation, startup hook, or background service was identified in the supplied artifact evidence. SkillSpector's metadata-permission concern is a transparency note rather than evidence of hidden behavior.
Credentials
Use of python3 and jq, local JSON files, and a skill data directory is proportionate for local analysis and rule generation.
Persistence & Privilege
Appending learned rules to MEMORY.md is persistent agent-behavior influence. It is disclosed and aligned with the self-learning purpose, but should remain user-directed and scoped.
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 crypto-self-learning - 安装完成后，直接呼叫该 Skill 的名称或使用
/crypto-self-learning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
Self-learning system for crypto trading. Logs trades, analyzes patterns, generates rules, and auto-updates agent memory for continuous improvement.
元数据
常见问题
Crypto Self-Learning 是什么？
Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 6559 次。
如何安装 Crypto Self-Learning？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install crypto-self-learning」即可一键安装，无需额外配置。
Crypto Self-Learning 是免费的吗？
是的，Crypto Self-Learning 完全免费（开源免费），可自由下载、安装和使用。
Crypto Self-Learning 支持哪些平台？
Crypto Self-Learning 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 Crypto Self-Learning？
由 totaleasy（@totaleasy）开发并维护，当前版本 v1.0.0。
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