time-sereis-analysis
v1.0.0Comprehensive time series data science skill covering feature engineering, model training, and competition-winning strategies for forecasting and prediction problems.
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & 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.
Assessment
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.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.