This skill is for adding new processing capabilities to the open-data module. It handles the ingestion, parsing, and visualization of large datasets (usually CSVs from government portals).

Execution Steps

1. Data Contract Definition

Locate lib/open-data.ts.

Action: Define a TypeScript interface for the new raw data structure.
Example:

export interface RawOcamEntry {
  "RUC PROVEEDOR": string;
  "MONTO TOTAL": string; // Note: Raw data is often string
  "FECHA FORMALIZACION": string;
  // ...
}

2. Processing Logic

Create/Edit lib/open-data-processing.ts. Implement a normalization function that converts Raw Strings -> Typed Objects (Numbers, Dates).

export function normalizeOcamData(raw: RawOcamEntry[]): NormalizedEntry[] {
  return raw.map(item => ({
    amount: parseFloat(item["MONTO TOTAL"]),
    vendor: item["RUC PROVEEDOR"],
    date: parse(item["FECHA FORMALIZACION"], "dd/MM/yyyy", new Date())
  }))
}

3. Analysis Functions

Implement specific business logic questions as pure functions.

Top Vendors: Sort by total amount.
Price Trends: Group by month/week.
Competitor Analysis: Filter by specific RUCs defined in competitor_matrix table.

4. API Endpoint (server-side)

Since datasets are large, processing should happen on the server.

Create app/api/open-data/[analysis-type]/route.ts.
This route should accept filters (Date Range, Category) and return the JSON ready for the chart.

5. Visualization Component

Create components/open-data/[analysis-name]-chart.tsx.

Use recharts.
Input: The processed JSON from the API.
Output: BarChart, LineChart, or DataTable.

Agent Check

Did you handle messy data? (NaNs, Invalid Dates).
Is the processing efficient? (Avoid nested loops on large arrays if possible).

npx skills add smithery/hcvm --skill create-open-data-analyzer