inventory-anomaly
v1.0.0库存异常检测和需求预测系统生成工具。当用户需要搭建库存管理系统、实现异常检测算法、开发需求预测功能（如ARIMA模型）、创建库存预警系统时使用此skill。特别适用于制造业、零售业、备件管理等场景，需要处理Excel数据、检测库存异常、预测未来需求并生成报告的情况。
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byVincent_Openclaw@openclawvincent
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included templates and scripts: data management, inventory queries, anomaly detection, ARIMA-based forecasting, fake-data generation and TXT report generation. No unrelated binaries or credentials are requested.
Instruction Scope
SKILL.md instructs copying templates, installing Python deps, and running src/main.py; all runtime behavior (reading/writing Excel under data/, generating output/report.txt) is within the stated purpose. Notes: main_template.py references helper functions (check_if_run_today, save_run_date, generate_fake_data invocation location) that are not defined in the templates as provided, and several data-access methods assume records exist which can raise exceptions if input Excel sheets are missing or malformed. These are functional/robustness issues rather than malicious scope creep.
Install Mechanism
The skill is instruction-only (no remote installers). A small requirements.txt lists common Python packages (pandas, numpy, statsmodels, openpyxl) from PyPI — expected for this workload. No downloads from arbitrary URLs or archive extraction are present.
Credentials
No environment variables, credentials, or unusual config paths are requested. All file I/O is local (data/, output/) and aligns with the described purpose.
Persistence & Privilege
always:false and no code attempts to modify other skills or global agent settings. The skill writes project files and report.txt under the project directories only, which is appropriate for a local tool.
Assessment
This skill appears to do what it claims: local Excel-based inventory anomaly detection and ARIMA forecasting, producing TXT reports. Before installing/running: (1) review and run the code in an isolated environment (virtualenv/container). (2) Ensure your data/spare_parts.xlsx has the required sheets and columns — the code often assumes rows exist and may raise IndexError on missing entries. (3) The main script references helper functions (check_if_run_today, save_run_date) that are not included; expect to add or adjust those bits before production use. (4) Pin dependency versions and run tests with fake data first (templates include a fake-data generator). (5) The package does no network calls or secret access, but it will read/write local files — do not point it at sensitive system directories. If you want, I can list the exact lines/functions that are likely to raise errors or produce exceptions so you can patch them before use.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.