feat: add data quality issue detector kit.

kits/dq-issue-detector/.env.example

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+# Lamatic API configuration
+LAMATIC_API_KEY=your_lamatic_api_key_here
+LAMATIC_PROJECT_ID=your_lamatic_project_id_here
+LAMATIC_ENDPOINT=https://your-project-endpoint.lamatic.dev
+
+# Deployed Flow IDs
+DATA_QUALITY_AGENT=your_data_quality_agent_flow_id_here

kits/dq-issue-detector/.gitignore

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+.lamatic/
+node_modules/
+.env
+.env.local

kits/dq-issue-detector/README.md

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+# Data Quality Issue Detector Kit
+
+## 1. The Problem
+Before analyzing data or training machine learning models, cleaning raw data is always the first and most critical step. Identifying null/missing values, duplicates, out-of-range numerical fields, formatting syntax problems (e.g. invalid emails or dates), and status casing inconsistencies is usually done manually with custom Python/Pandas scripts. This can be time-consuming, repetitive, and inaccessible to non-technical users who just want a quick audit of their files.
+
+## 2. The Approach
+We built an AI-powered data quality audit agent using Next.js and the Lamatic API.
+- **Workflow:** The user loads or pastes a dataset (CSV) and provides optional instructions (e.g. "Focus on validating emails and ignore columns without names").
+- **Agent Action:** The LLM-backed agent scans the dataset row-by-row, identifying anomalies, missing information, schema mismatches, and duplicated records.
+- **Interactive UI:** The Next.js frontend previews the CSV as an interactive table, triggers the Server Action to invoke the Lamatic flow, and renders the detailed audit report side-by-side using markdown.
+
+## 3. The Result
+An instantly usable web tool to run automated health checks on dataset CSVs. Teams receive a clear, prioritized checklist of data clean-up items, complete with column-specific summaries, issues categorized by severity (High, Medium, Low), and actionable cleaning steps to feed into their ETL scripts.
+
+---
+
+## AgentKit Collection (1 flow)
+
+This AgentKit contains 1 flow:
+- **Data Quality Agent** (`flows/data-quality-agent.ts`)
+
+---
+
+## Prerequisites
+*   **Node.js** (v18 or higher recommended)
+*   **npm** or **pnpm** package manager
+*   A valid **Lamatic API Key**, **Project ID**, and **API URL/Endpoint**
+
+---
+
+## Setup Instructions
+
+1. **Initialize the App Directory**
+   ```bash
+   cd apps
+   npm install
+   ```
+
+2. **Configure Environment Variables**
+   Create a local env file:
+   ```bash
+   cp .env.example .env.local
+   ```
+   Open `.env.local` and add your Lamatic credentials:
+   ```env
+   LAMATIC_API_KEY=your_api_key_here
+   LAMATIC_PROJECT_ID=your_project_id_here
+   LAMATIC_ENDPOINT=https://your-endpoint.lamatic.dev
+   DATA_QUALITY_AGENT=your_deployed_flow_id_here
+   ```
+
+3. **Start the Development Server**
+   ```bash
+   npm run dev
+   ```
+   The application will be available at `http://localhost:3000`.
+
+---
+
+## Programmatic Usage Example
+
+The Next.js frontend calls the Lamatic flow via Next.js Server Actions:
+
+```typescript
+import { analyzeDatasetQuality } from "@/actions/orchestrate";
+
+const checkData = async () => {
+  const csvContent = `id,name,email,age
+1,John Doe,john@example.com,28
+2,Jane Smith,,thirty-four
+1,John Doe,john@example.com,28`;
+
+  const result = await analyzeDatasetQuality(csvContent, "Check for schema issues and duplicates.");
+
+  if (result.success) {
+    console.log("Analysis Report:", result.data);
+  }
+};
+```

kits/dq-issue-detector/agent.md

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+# Data Quality Issue Detector
+
+You are a data quality assistant.
+
+Your job is to analyze uploaded tabular data and identify:
+- missing values
+- duplicate rows
+- invalid formats
+- outliers
+- inconsistent categories
+- suspicious values
+- data type mismatches
+
+Return:
+1. Dataset summary
+2. Issues found
+3. Severity
+4. Suggested fixes
+5. Quality score out of 100
+6. Final recommendation
+
+Be concise, practical, and easy to understand.

kits/dq-issue-detector/apps/.env.example

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+# Lamatic API configuration
+LAMATIC_API_KEY=your_lamatic_api_key_here
+LAMATIC_PROJECT_ID=your_lamatic_project_id_here
+LAMATIC_ENDPOINT=https://your-project-endpoint.lamatic.dev
+
+# Deployed Flow IDs
+DATA_QUALITY_AGENT=your_data_quality_agent_flow_id_here

kits/dq-issue-detector/apps/.gitignore

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+.env
+.env.local
+node_modules
+.next
+out
+dist
+tsconfig.tsbuildinfo

kits/dq-issue-detector/apps/actions/orchestrate.ts

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+"use server";
+
+import { lamaticClient } from "../lib/lamatic-client"
+import lamaticConfig from "../../lamatic.config"
+
+export async function orchestrateAnalysis(formData: FormData) {
+  try {
+    const file = formData.get("file") as File | null;
+    if (!file) throw new Error("File is required");
+
+    const fileContent = await file.text();
+
+    const step = lamaticConfig.steps.find((s) => s.id === "data-quality-agent") as any;
+    let workflowId = step?.workflowId || (step?.envKey ? process.env[step.envKey] : undefined);
+    if (!workflowId) {
+      workflowId = process.env.DATA_QUALITY_AGENT;
+    }
+    if (!workflowId) {
+      throw new Error("Data Quality Agent workflow ID is not configured. Please define it in lamatic.config.ts or the DATA_QUALITY_AGENT env variable.");
+    }
+
+    const query = `
+      query ExecuteWorkflow($workflowId: String!, $file: JSON) {
+        executeWorkflow(
+          workflowId: $workflowId
+          payload: { file: $file }
+        ) {
+          status
+          result
+        }
+      }`;
+
+    const variables = {
+      workflowId,
+      file: {
+        name: file.name,
+        content: fileContent
+      }
+    };
+
+    const response = await lamaticClient.executeGraphQL(query, variables);
+
+    let report = response.result;
+    if (typeof report === "object" && report !== null) {
+      report = report.report || JSON.stringify(report, null, 2);
+    }
+
+    return { success: true, data: report };
+
+  } catch (error: unknown) {
+    console.error("Data Quality Analysis Error:", error);
+    const message = error instanceof Error ? error.message : String(error);
+    return { success: false, error: message };
+  }
+}

kits/dq-issue-detector/apps/app/globals.css

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+@import "tailwindcss";
+
+:root {
+  color-scheme: light;
+  --background: #f8fafc;
+  --foreground: #0f172a;
+  --surface: #ffffff;
+  --surface-muted: #f1f5f9;
+  --border: #e2e8f0;
+  --border-strong: #cbd5e1;
+  --muted: #64748b;
+  --muted-strong: #334155;
+  --heading: #0f172a;
+  --primary: #4f46e5;
+  --primary-hover: #4338ca;
+  --primary-foreground: #ffffff;
+  --primary-soft: #e0e7ff;
+  --primary-soft-foreground: #4338ca;
+  --danger: #dc2626;
+  --danger-soft: #fef2f2;
+  --danger-border: #fecaca;
+  --success: #16a34a;
+  --success-soft: #f0fdf4;
+  --success-border: #bbf7d0;
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  background: var(--background);
+  color: var(--foreground);
+  font-family:
+    Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI",
+    sans-serif;
+}
+
+button,
+input,
+textarea {
+  font: inherit;
+}
+
+textarea {
+  resize: vertical;
+}
+
+/* Custom markdown rendering styling */
+.prose h1, .prose h2, .prose h3 {
+  font-weight: 700;
+  color: var(--heading);
+  margin-top: 1.5em;
+  margin-bottom: 0.5em;
+}
+.prose h1 { font-size: 1.5rem; border-bottom: 2px solid var(--border); padding-bottom: 0.3em; }
+.prose h2 { font-size: 1.25rem; }
+.prose h3 { font-size: 1.1rem; }
+.prose p { margin-top: 0.5em; margin-bottom: 0.5em; line-height: 1.6; }
+.prose ul, .prose ol { padding-left: 1.5rem; margin-top: 0.5em; margin-bottom: 0.5em; }
+.prose li { margin-top: 0.25em; margin-bottom: 0.25em; }
+.prose strong { color: var(--heading); font-weight: 600; }
+.prose code { background-color: var(--surface-muted); padding: 0.15rem 0.3rem; rounded: 0.25rem; font-family: monospace; font-size: 0.9em; }
+.prose blockquote { border-left: 4px solid var(--border-strong); padding-left: 1rem; color: var(--muted); font-style: italic; }

kits/dq-issue-detector/apps/app/layout.tsx

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+import type { Metadata } from "next";
+import "./globals.css";
+
+export const metadata: Metadata = {
+  title: "Data Quality Issue Detector",
+  description: "Scans datasets for anomalies, missing values, duplicates, and formatting issues with Lamatic.",
+};
+
+export default function RootLayout({
+  children,
+}: Readonly<{
+  children: React.ReactNode;
+}>) {
+  return (
+    <html lang="en">
+      <body>{children}</body>
+    </html>
+  );
+}