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Content Moderation Patterns

Content Moderation Patterns

Implement content moderation in Google Antigravity with AI filtering, flagging, and admin review queues.

moderationsafetyadminai
by antigravity-team
⭐0Stars
.antigravity
# Content Moderation for Google Antigravity

Build robust content moderation systems with AI filtering, user reporting, and admin review.

## Database Schema

```sql
CREATE TYPE moderation_status AS ENUM ('pending', 'approved', 'rejected', 'flagged');

CREATE TABLE public.moderation_queue (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    content_type TEXT NOT NULL,
    content_id UUID NOT NULL,
    content_preview TEXT,
    status moderation_status DEFAULT 'pending',
    reason TEXT,
    reporter_id UUID REFERENCES auth.users(id),
    reviewer_id UUID REFERENCES auth.users(id),
    reviewed_at TIMESTAMPTZ,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE public.content_reports (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    content_type TEXT NOT NULL,
    content_id UUID NOT NULL,
    reporter_id UUID REFERENCES auth.users(id) ON DELETE SET NULL,
    reason TEXT NOT NULL,
    details TEXT,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_moderation_status ON public.moderation_queue(status, created_at);
CREATE INDEX idx_reports_content ON public.content_reports(content_type, content_id);
```

## Moderation Service

```typescript
// lib/moderation.ts
import { createClient } from "@/lib/supabase/server";

export async function submitForModeration(contentType: string, contentId: string, contentPreview: string) {
    const supabase = createClient();
    await supabase.from("moderation_queue").insert({ content_type: contentType, content_id: contentId, content_preview: contentPreview, status: "pending" });
}

export async function reportContent(contentType: string, contentId: string, reporterId: string, reason: string, details?: string) {
    const supabase = createClient();
    
    // Create report
    await supabase.from("content_reports").insert({ content_type: contentType, content_id: contentId, reporter_id: reporterId, reason, details });
    
    // Check if threshold reached for auto-flagging
    const { count } = await supabase.from("content_reports").select("*", { count: "exact", head: true }).eq("content_type", contentType).eq("content_id", contentId);
    
    if (count && count >= 3) {
        await supabase.from("moderation_queue").upsert({ content_type: contentType, content_id: contentId, status: "flagged", reason: "Multiple reports" }, { onConflict: "content_type,content_id" });
    }
}

export async function moderateContent(itemId: string, reviewerId: string, decision: "approved" | "rejected", reason?: string) {
    const supabase = createClient();
    await supabase.from("moderation_queue").update({ status: decision, reviewer_id: reviewerId, reviewed_at: new Date().toISOString(), reason }).eq("id", itemId);
}
```

## AI Content Filter

```typescript
// lib/ai-moderation.ts
export interface ModerationResult {
    safe: boolean;
    categories: { name: string; score: number; flagged: boolean }[];
    action: "allow" | "review" | "block";
}

export async function checkContent(text: string): Promise<ModerationResult> {
    const response = await fetch("https://api.openai.com/v1/moderations", {
        method: "POST",
        headers: { "Authorization": `Bearer ${process.env.OPENAI_API_KEY}`, "Content-Type": "application/json" },
        body: JSON.stringify({ input: text }),
    });

    const data = await response.json();
    const result = data.results[0];

    const categories = Object.entries(result.category_scores).map(([name, score]) => ({
        name,
        score: score as number,
        flagged: result.categories[name],
    }));

    const flaggedCount = categories.filter((c) => c.flagged).length;
    const action = flaggedCount > 2 ? "block" : flaggedCount > 0 ? "review" : "allow";

    return { safe: !result.flagged, categories, action };
}
```

## Report Dialog Component

```typescript
// components/ReportDialog.tsx
"use client";

import { useState } from "react";

const REPORT_REASONS = ["Spam", "Harassment", "Hate speech", "Misinformation", "Copyright violation", "Other"];

export function ReportDialog({ contentType, contentId, onClose }: { contentType: string; contentId: string; onClose: () => void }) {
    const [reason, setReason] = useState("");
    const [details, setDetails] = useState("");
    const [submitting, setSubmitting] = useState(false);

    const handleSubmit = async (e: React.FormEvent) => {
        e.preventDefault();
        setSubmitting(true);
        await fetch("/api/report", {
            method: "POST",
            headers: { "Content-Type": "application/json" },
            body: JSON.stringify({ contentType, contentId, reason, details }),
        });
        onClose();
    };

    return (
        <div className="report-dialog">
            <h3>Report Content</h3>
            <form onSubmit={handleSubmit}>
                <label>Reason</label>
                <select value={reason} onChange={(e) => setReason(e.target.value)} required>
                    <option value="">Select a reason</option>
                    {REPORT_REASONS.map((r) => <option key={r} value={r}>{r}</option>)}
                </select>
                <label>Additional details (optional)</label>
                <textarea value={details} onChange={(e) => setDetails(e.target.value)} placeholder="Provide more context..." />
                <div className="actions">
                    <button type="button" onClick={onClose}>Cancel</button>
                    <button type="submit" disabled={submitting}>{submitting ? "Submitting..." : "Submit Report"}</button>
                </div>
            </form>
        </div>
    );
}
```

## Admin Moderation Queue

```typescript
// app/admin/moderation/page.tsx
import { createClient } from "@/lib/supabase/server";
import { ModerationItem } from "@/components/admin/ModerationItem";

export default async function ModerationPage() {
    const supabase = createClient();
    const { data: items } = await supabase.from("moderation_queue").select("*").in("status", ["pending", "flagged"]).order("created_at", { ascending: true });

    return (
        <div className="moderation-queue">
            <h1>Moderation Queue</h1>
            <p>{items?.length || 0} items pending review</p>
            <div className="queue-list">
                {items?.map((item) => <ModerationItem key={item.id} item={item} />)}
            </div>
        </div>
    );
}
```

## Best Practices

1. **AI Pre-screening**: Use AI to pre-filter obvious violations
2. **Threshold Triggers**: Auto-flag content with multiple reports
3. **Audit Trail**: Keep complete audit trail of decisions
4. **Appeals Process**: Allow users to appeal moderation decisions
5. **Rate Limiting**: Prevent abuse of reporting system

When to Use This Prompt

This moderation prompt is ideal for developers working on:

  • moderation applications requiring modern best practices and optimal performance
  • Projects that need production-ready moderation code with proper error handling
  • Teams looking to standardize their moderation development workflow
  • Developers wanting to learn industry-standard moderation patterns and techniques

By using this prompt, you can save hours of manual coding and ensure best practices are followed from the start. It's particularly valuable for teams looking to maintain consistency across their moderation implementations.

How to Use

  1. Copy the prompt - Click the copy button above to copy the entire prompt to your clipboard
  2. Paste into your AI assistant - Use with Claude, ChatGPT, Cursor, or any AI coding tool
  3. Customize as needed - Adjust the prompt based on your specific requirements
  4. Review the output - Always review generated code for security and correctness
💡 Pro Tip: For best results, provide context about your project structure and any specific constraints or preferences you have.

Best Practices

  • ✓ Always review generated code for security vulnerabilities before deploying
  • ✓ Test the moderation code in a development environment first
  • ✓ Customize the prompt output to match your project's coding standards
  • ✓ Keep your AI assistant's context window in mind for complex requirements
  • ✓ Version control your prompts alongside your code for reproducibility

Frequently Asked Questions

Can I use this moderation prompt commercially?

Yes! All prompts on Antigravity AI Directory are free to use for both personal and commercial projects. No attribution required, though it's always appreciated.

Which AI assistants work best with this prompt?

This prompt works excellently with Claude, ChatGPT, Cursor, GitHub Copilot, and other modern AI coding assistants. For best results, use models with large context windows.

How do I customize this prompt for my specific needs?

You can modify the prompt by adding specific requirements, constraints, or preferences. For moderation projects, consider mentioning your framework version, coding style, and any specific libraries you're using.

Related Prompts

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