国家统计局数据采集

国家统计局数据采集技能。当用户需要采集国家统计局（NBS）的宏观经济数据（如GDP、CPI、PPI、产出缺口等）时触发。适用场景包括：（1）采集GDP、CPI、PPI等指标；（2）从国家统计局官网/统计年鉴获取数据；（3）计算产出缺口（HP滤波）；（4）整理数据到Excel。本skill包含标准工作流程、数据源U...

This skill is generally coherent with its stated purpose, but review and take precautions before running: - Inspect any Excel files you provide: verify_links.py will extract hyperlinks and run curl against each URL. Do not give it spreadsheets containing internal/private URLs (this could cause unwanted internal network probes). - Run the scripts in an isolated environment (sandbox, VM, or container) if you are unsure about network/host exposure. - Ensure required runtime dependencies are present: Python 3.10+, openpyxl, numpy (and optionally scipy if you add it), and the curl binary. The skill does not install these automatically. - Note some crawler functions are placeholders (TODO) and return null values; verify that nbs_crawler actually retrieves data for your target timeframe before relying on outputs. - If you need stricter behavior, consider modifying verify_links.py to whitelist domains (e.g., stats.gov.cn, data.stats.gov.cn) rather than checking all links, or add an option to skip network checks. If you want, I can: (1) list exact commands to create an isolated environment and install dependencies, (2) show the minimal code change to make verify_links.py domain-whitelist-only, or (3) walk through the crawler functions and point out where to implement real API calls.

Type: OpenClaw Skill Name: nbs-data-collection Version: 1.0.0 The nbs-data-collection skill bundle is a legitimate and well-structured tool designed for macro-economic research. It provides a comprehensive workflow for collecting GDP, CPI, and PPI data from the National Bureau of Statistics of China (stats.gov.cn). The included Python scripts perform specialized tasks such as Hodrick-Prescott (HP) filtering for output gap calculation (scripts/hp_filter.py), Excel report generation with automated charting (scripts/add_charts.py), and link validation (scripts/verify_links.py). While verify_links.py utilizes subprocess to execute curl for HTTP status checks, it follows best practices by using argument lists to prevent shell injection and serves a clear, functional purpose. The documentation (SKILL.md, README.md) is professional and aligns perfectly with the code's behavior, with no signs of data exfiltration, persistence, or malicious prompt injection.