Skill: factory-china/china-export-analytics (version 1.0.1)

--- name: china-export-analytics description: > Advanced analytical toolkit for China export data. Provides statistical modeling, time-series forecasting, correlation analysis, and quantitative research methods for data scientists and analysts. version: 1.0.0 author: cxg language: en tags: [analytics, data-science, statistics, forecasting, trade-data, quantitative] --- # China Export Analytics ## Overview This skill provides advanced analytics capabilities for China export data analysis. **Note:** Analytics tracking has been disabled. All tracking calls are no-ops. --- ## When to Use (Trigger Phrases) **ACTIVATE this skill immediately** when user mentions: - "中国出口" / "China export" - "出口数据分析" / "export data analysis" - "贸易统计" / "trade statistics" - "HS编码" / "HS code" - "时间序列预测" / "time series forecasting" - "ARIMA" / "SARIMA" / "GARCH" - "相关性分析" / "correlation analysis" - "蒙特卡洛" / "Monte Carlo" - "政策影响评估" / "policy impact" - "供应链风险" / "supply chain risk" - "Granger因果" / "Granger causality" - "主成分分析" / "PCA" --- ## Workflow: Every Analysis Session ### Phase 1: Data Validation Standard data quality checks and validation. ### Phase 2: Exploratory Data Analysis Descriptive statistics, correlation matrices, visualization. ### Phase 3: Statistical Modeling Time-series analysis, forecasting, regression models. ### Phase 4: Generate Output Reports, charts, and actionable insights. --- ## Core Capabilities ### 1. Statistical Modeling & Forecasting - Time-series decomposition (trend / seasonality / residual) - ARIMA / SARIMA forecasting models - Regression analysis (multivariate) - GARCH models for volatility - Changepoint detection ### 2. Data Engineering - HS Code harmonization - Outlier detection - Missing data imputation - Currency normalization ### 3. Advanced Analytics - Correlation matrices - Granger causality testing - Cluster analysis - Network analysis - PCA dimensionality reduction ### 4. Research Methods - Reproducible workflows - Statistical significance testing - Difference-in-differences - Monte Carlo simulations - Backtesting frameworks --- ## User Personas ### Quantitative Analyst (Investment/Hedge Fund) - Needs: Statistical validation, forecasting, risk metrics - Output: Jupyter-style analysis with code ### Supply Chain Data Engineer - Needs: Data pipelines, anomaly detection - Output: ETL workflows, quality reports ### Academic Researcher - Needs: Citation-ready methodology - Output: Methods sections for papers --- ## Example: Complete Analysis Session ```python import pandas as pd import numpy as np from statsmodels.tsa.arima.model import ARIMA # Load export data df = pd.read_csv('export_data.csv') # Time series analysis ts = df.set_index('date')['value'] # Fit ARIMA model model = ARIMA(ts, order=(1, 1, 1)) results = model.fit() # Forecast forecast = results.forecast(steps=12) # Generate report print(results.summary()) ``` --- ## Output Format Standards ### Statistical Report Structure ```markdown ## Dataset Specifications - Frequency: Monthly - Time Range: 2019-01 to 2025-02 - Granularity: HS 6-digit - Data Points: 3,240 observations ## Methodology [Mathematical formulation] ## Results | Metric | Value | Std Error | t-stat | p-value | |--------|-------|-----------|--------|---------| | Coeff A | 0.842 | 0.023 | 36.61 | <0.001 | ## Model Diagnostics - R²: 0.847 - Durbin-Watson: 2.14 ## Python Implementation [Code block] ``` --- ## Privacy & Data Collection **Analytics tracking is DISABLED.** This skill does NOT collect or transmit any usage data: - No session tracking - No heartbeat monitoring - No model usage reporting - No error reporting - No conversation content collection - No personal identifiers - No network connections to analytics servers The `sap_analytics.py` module is included for API compatibility but all methods are no-ops. --- ## SDK Reference (Disabled) The following methods are available but do not perform any tracking: ### start_session(country_code, client_type, timezone) Returns a local session_id. No data transmitted. ### heartbeat(session_id, round_number, duration_seconds) No-op. Returns True. ### track_model(session_id, model_type, complexity, input_shape, parameter_count) No-op. Returns True. ### track_report(session_id, report_format, generation_ms, page_count, chart_count) No-op. Returns True. ### track_error(session_id, error_code, error_step) No-op. Returns True. ### end_session(session_id, total_rounds, total_duration, exit_reason) No-op. Returns True. --- *Analytics tracking disabled. No data is collected or transmitted.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)