feat: add time-series-preprocessor agent kit

kits/automation/time-series-preprocessor/.env.example

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+TIME_SERIES_PREPROCESSOR="Your Flow ID from Lamatic Studio"
+LAMATIC_API_URL="Your API Endpoint URL"
+LAMATIC_PROJECT_ID="Your Project ID"
+LAMATIC_API_KEY="Your API Key"

kits/automation/time-series-preprocessor/.gitignore

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+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# env files
+.env
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
+
+.env.local

kits/automation/time-series-preprocessor/README.md

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+# Time-Series Preprocessor — Lamatic AgentKit
+
+An automation kit that analyzes time-series dataset schemas and generates production-ready Python preprocessing pipelines using `pandas` and `scikit-learn`. Paste a JSON summary of your dataset and receive executable code in seconds.
+
+---
+
+## What It Does
+
+By providing a JSON summary of your dataset, the agent generates a complete Python script that handles:
+
+- **Missing value imputation** — forward-fill, mean, and median strategies selected based on column type
+- **Feature scaling** — MinMaxScaler or StandardScaler applied appropriately
+- **Datetime parsing and index management** — automatic timestamp detection and alignment
+- **Categorical encoding** — label or one-hot encoding based on cardinality
+- **Standardized implementation** — clean, readable `pandas` + `scikit-learn` code ready to run
+
+---
+
+## Project Background
+
+This kit was developed to solve the repetitive nature of data cleaning in time-series projects.
+
+The concept originated during the development of a **water demand forecasting model** for a college located in a rural area near Bhopal. Due to aging sensor hardware and inconsistent data streams, significant time was spent manually handling missing values and aligning disparate data sources — rainfall readings, local water levels, and consumption logs from different systems with mismatched timestamps.
+
+The goal was to automate the boilerplate preprocessing code, allowing engineers to focus on model performance rather than manual cleanup. This kit is the result of that experience.
+
+---
+
+## Who Is It For
+
+Data engineers and machine learning engineers who frequently work with time-series data and need a fast way to generate reliable, repeatable preprocessing pipelines without writing boilerplate code from scratch.
+
+---
+
+## Tech Stack
+
+| Tool | Role |
+|---|---|
+| [Lamatic.ai](https://lamatic.ai) | Flow orchestration and Edge deployment |
+| Gemini 2.5 Pro | AI-driven Python code generation |
+| Next.js 14 | Interactive frontend sandbox |
+| pandas + scikit-learn | Target libraries for generated pipelines |
+
+---
+
+## Setup
+
+### 1. Build and Deploy Flow in Lamatic Studio
+
+1. Sign in at [lamatic.ai](https://lamatic.ai)
+2. Create a new project (if you don't have one)
+3. Click **+ New Flow** and select **API Request** as the trigger
+4. Add a **Generate Text** node and select **Gemini 2.5 Pro**
+5. Set the input variable to `dataset_summary`
+6. Configure the system prompt to act as an expert data engineer
+7. Deploy the flow and copy your credentials from the Studio dashboard
+
+### 2. Environment Variables
+
+Create a `.env.local` file in the kit root directory:
+
+```env
+TIME_SERIES_PREPROCESSOR="Your Flow ID from Lamatic Studio"
+LAMATIC_API_URL="Your API Endpoint URL"
+LAMATIC_PROJECT_ID="Your Project ID"
+LAMATIC_API_KEY="Your API Key"
+```
+
+| Variable | Where to Find It |
+|---|---|
+| `TIME_SERIES_PREPROCESSOR` | Studio → Your Flow → Settings |
+| `LAMATIC_API_URL` | Studio → Your Flow → API Endpoint |
+| `LAMATIC_PROJECT_ID` | Studio → Project Settings |
+| `LAMATIC_API_KEY` | Studio → Project Settings → API Keys |
+
+### 3. Install and Run
+
+```bash
+npm install
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) to access the frontend interface.
+
+---
+
+## Example Input
+
+Provide a JSON object describing your dataset structure:
+
+```json
+{
+  "dataset_name": "sensor_readings",
+  "frequency": "1min",
+  "columns": [
+    {"name": "timestamp", "type": "datetime"},
+    {"name": "temperature", "type": "float", "missing_pct": 5},
+    {"name": "pressure", "type": "float", "missing_pct": 2},
+    {"name": "status", "type": "categorical", "missing_pct": 0}
+  ],
+  "rows": 50000,
+  "target_column": "temperature"
+}
+```
+
+## Example Output
+
+The agent returns a fully executable Python script:
+
+```python
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+# Load and index
+df = pd.read_csv("sensor_readings.csv", parse_dates=["timestamp"])
+df.set_index("timestamp", inplace=True)
+
+# Impute missing values
+df["temperature"].fillna(method="ffill", inplace=True)
+df["pressure"].fillna(df["pressure"].mean(), inplace=True)
+
+# Scale numerical features
+scaler = MinMaxScaler()
+df[["temperature", "pressure"]] = scaler.fit_transform(df[["temperature", "pressure"]])
+
+print("Preprocessing complete.")
+print(df.head())
+```
+
+---
+
+## Project Structure
+
+````text
+time-series-preprocessor/
+├── actions/
+│   └── orchestrate.ts       # Server action calling the Lamatic flow
+├── app/
+│   └── page.tsx             # Main UI — input form and output display
+├── components/
+│   └── ui/                  # shadcn/ui components
+├── flows/
+│   └── time-series-preprocessor/
+│       ├── config.json      # Exported Lamatic flow graph
+│       ├── inputs.json      # Input schema definition
+│       └── meta.json        # Flow metadata
+├── lib/
+│   └── lamatic-client.ts    # Lamatic SDK client
+├── .env.example             # Environment variable template
+├── config.json              # Kit metadata
+└── README.md
+````
+
+---
+
+## Contributing
+
+Contributions are welcome. Open an issue or pull request in the [AgentKit repository](https://github.com/Lamatic/AgentKit).
+
+## License
+
+MIT License — see [LICENSE](../../../LICENSE).

kits/automation/time-series-preprocessor/actions/orchestrate.ts

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+"use server";
+
+import { createLamaticClient } from "@/lib/lamatic-client";
+
+const client = createLamaticClient();
+
+export async function preprocessTimeSeries(datasetSummary: string) {
+  if (!process.env.TIME_SERIES_PREPROCESSOR) {
+    throw new Error("TIME_SERIES_PREPROCESSOR environment variable is not set");
+  }
+
+  try {
+    const response = await client.executeFlow({
+      flowId: process.env.TIME_SERIES_PREPROCESSOR,
+      inputs: {
+        dataset_summary: datasetSummary,
+      },
+    });
+
+    return {
+      success: true,
+      result: response?.data?.generatedText || "",
+    };
+  } catch (error) {
+    console.error("Error calling Lamatic flow:", error);
+    return {
+      success: false,
+      result: "Failed to generate preprocessing pipeline. Please try again.",
+    };
+  }
+}

kits/automation/time-series-preprocessor/app/globals.css

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+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+* {
+  box-sizing: border-box;
+  margin: 0;
+  padding: 0;
+}
+
+body {
+  font-family: 'Inter', sans-serif;
+  background-color: #f5f5f5;
+  color: #111;
+}

kits/automation/time-series-preprocessor/app/layout.tsx

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+import type { Metadata } from "next";
+import { ThemeProvider } from "@/components/ThemeProvider"
+import "./globals.css";
+
+export const metadata: Metadata = {
+  title: "Time-Series Preprocessor — Lamatic AgentKit",
+  description: "AI-powered time-series preprocessing pipeline generator",
+};
+
+export default function RootLayout({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  return (
+    <html lang="en">
+      <head>
+        <link rel="preconnect" href="https://fonts.googleapis.com" />
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossOrigin="anonymous" />
+        <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800;900&display=swap" rel="stylesheet" />
+      </head>
+      <body style={{ margin: 0, padding: 0 }}>
+        <ThemeProvider>
+          {children}
+        </ThemeProvider>
+      </body>
+    </html>
+  );
+}