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在 OpenClaw 中安装
/install time-series-analysis
功能描述
Comprehensive time series data science skill covering feature engineering, model training, and competition-winning strategies for forecasting and prediction problems.
安全使用建议
This skill is an instruction-only guide for time-series modeling and appears coherent and proportionate. Before using it, ensure your runtime has the expected libraries (numpy, pandas, lightgbm, sklearn) installed in a controlled environment. Review any code snippets and test them on non-sensitive or synthetic data first — the skill will produce runnable code patterns but does not itself validate security or data privacy. If you plan to run it inside an agent that can execute code, run it in an isolated environment (container or VM) and avoid feeding the skill sensitive credentials or private data unless you trust where execution will run.
能力评估
Purpose & Capability
The name and description match the content: the SKILL.md is a comprehensive guide for feature engineering, validation, and model training for time-series forecasting. The examples reference libraries and patterns (pandas, numpy, LightGBM, sklearn) that are appropriate for the stated purpose. (Minor cosmetic inconsistency: the provided skill name has a small typo 'time-sereis-analysis' while the slug is 'time-series-analysis', but this does not affect security.)
Instruction Scope
The SKILL.md contains code snippets and prescriptive modeling advice but does not instruct the agent to read arbitrary system files, access environment variables, or contact external endpoints. It stays within the data-science scope (data transforms, model training, validation).
Install Mechanism
No install spec is provided (instruction-only), so nothing will be written to disk by the skill itself — this is low risk. Note: the guide assumes common Python libraries (numpy, pandas, lightgbm, sklearn) but does not declare dependencies; an agent or user may need to install these separately.
Credentials
The skill requests no environment variables, credentials, or config paths. The only resources implied are typical ML libraries and compute; no secret access is requested nor appears necessary for the described tasks.
Persistence & Privilege
The skill is not always-enabled and does not request persistent system presence or modify other skills. Autonomous invocation is allowed by default (platform normal), but the skill itself does not request elevated privileges.
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 time-series-analysis - 安装完成后，直接呼叫该 Skill 的名称或使用
/time-series-analysis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
Initial release of the Time Series Data Science skill, a comprehensive end-to-end guide and toolkit for competitive time series forecasting and prediction.
- Covers feature engineering (lags, rolling stats, EWM, target encoding, interactions), model training, and robust validation strategies tailored for time series.
- Provides proven best practices and code templates for LightGBM training, ensembling, and horizon-specific modeling.
- Includes a full EDA checklist, practical competition workflow diagram, and ready-to-use pipeline commands.
- Lists common pitfalls in time series forecasting and actionable solutions.
- Designed for integration with related data science and analytics workflows.
元数据
常见问题
time-sereis-analysis 是什么？
Comprehensive time series data science skill covering feature engineering, model training, and competition-winning strategies for forecasting and prediction problems. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1589 次。
如何安装 time-sereis-analysis？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install time-series-analysis」即可一键安装，无需额外配置。
time-sereis-analysis 是免费的吗？
是的，time-sereis-analysis 完全免费（开源免费），可自由下载、安装和使用。
time-sereis-analysis 支持哪些平台？
time-sereis-analysis 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 time-sereis-analysis？
由 dubnium0（@dubnium0）开发并维护，当前版本 v1.0.0。
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