data-science-for-intelligence

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: data-science-for-intelligence
description: Statistical analysis, ML, NLP, time series forecasting, network analysis for political intelligence data
license: Apache-2.0
---

# Data Science for Intelligence Skill


## 🔴 AI FIRST Quality Principle

> **This skill MUST be applied with the AI FIRST principle: never accept first-pass quality. ALL analysis and content MUST go through minimum 2 complete iterations. After first pass, read ALL output back completely and systematically improve every section — strengthen evidence, deepen analysis, add specific citations, broaden perspectives. Spend ALL allocated time on real work. Single-pass output is NEVER acceptable. NO SHORTCUTS.**

## Purpose

This skill provides comprehensive data science methodologies tailored for political intelligence analysis in the Riksdagsmonitor platform. It covers statistical analysis, machine learning, natural language processing, time series forecasting, and network analysis techniques applied to the 6 intelligence frameworks and 82 database views for democratic accountability assessment.

## When to Use This Skill

Apply this skill when:
- ✅ Building predictive models for election outcomes or coalition stability
- ✅ Analyzing temporal patterns in voting behavior or policy positions
- ✅ Detecting anomalies in political behavior (sudden voting shifts, absences)
- ✅ Performing text analysis on parliamentary documents and speeches
- ✅ Constructing influence networks from voting alignment patterns
- ✅ Forecasting trends in party support or politician effectiveness
- ✅ Clustering politicians or parties by voting similarity
- ✅ Modelling Swedish economic transmission chains using IMF macro context plus SCB KPI/AKU/HEK/fuel-price series and Riksbank policy-rate/minutes signals

Do NOT use for:
- ❌ Simple aggregation queries (use SQL views instead)
- ❌ Real-time operational dashboards (use materialized views)
- ❌ Causal inference without proper experimental design or quasi-experimental methods
- ❌ Replacing IMF cross-country macro/fiscal indicators with SCB or Riksbank series; use SCB/Riksbank only as Swedish ground-truth complements

## Data Science Framework for CIA Platform

### 6 Intelligence Analysis Frameworks

```mermaid
graph TB
    subgraph "Data Sources"
        A[Riksdagen API<br/>3.5M votes]
        B[Election Authority<br/>40 parties]
        C[World Bank<br/>598K indicators]
        D[Financial Authority<br/>Agency data]
    end
    
    subgraph "Data Science Techniques"
        A & B & C & D --> E[Feature Engineering]
        E --> F1[Time Series<br/>Analysis]
        E --> F2[Classification<br/>Models]
        E --> F3[Clustering<br/>Analysis]
        E --> F4[NLP<br/>Processing]
        E --> F5[Network<br/>Analysis]
    end
    
    subgraph "Intelligence Frameworks"
        F1 --> G1[1. Temporal<br/>Analysis]
        F2 --> G2[3. Pattern<br/>Recognition]
        F3 --> G3[2. Comparative<br/>Analysis]
        F4 --> G3
        F5 --> G5[5. Network<br/>Analysis]
        F1 & F2 --> G4[4. Predictive<br/>Intelligence]
        G1 & G2 & G3 & G4 & G5 --> G6[6. Decision<br/>Intelligence]
    end
    
    subgraph "Intelligence Products"
        G1 & G2 & G3 & G4 & G5 & G6 --> H[Risk Assessments]
        H --> I[Political Scorecards]
        H --> J[Coalition Forecasts]
        H --> K[Anomaly Alerts]
    end
    
    style E fill:#ffeb99
    style F1 fill:#e1f5ff
    style F2 fill:#e1f5ff
    style F3 fill:#e1f5ff
    style F4 fill:#e1f5ff
    style F5 fill:#e1f5ff
    style H fill:#ccffcc
```

## 1. Time Series Analysis (Temporal Framework)

**Purpose:** Analyze trends, seasonality, and forecast political metrics over time.

**CIA Platform Applications:**
- Politician voting participation trends
- Party support trajectories
- Government approval ratings
- Legislative productivity patterns

### Decomposition Analysis

**Example: Decompose Party Support into Trend, Seasonal, Residual Components**

```python
import pandas as pd
import numpy a