OpenRouter Trending Models Skill

Overview

This skill provides access to current trending programming models from OpenRouter's public rankings. It executes a Bun script that fetches, parses, and structures data about the top 9 most-used AI models for programming tasks.

What you get:

• Model IDs and names (e.g., x-ai/grok-code-fast-1)
• Token usage statistics (last week's trends)
• Context window sizes (input capacity)
• Pricing information (per token and per 1M tokens)
• Summary statistics (top provider, price ranges, averages)

Data Source:

• OpenRouter Rankings (https://openrouter.ai/rankings?category=programming)
• OpenRouter Models API (https://openrouter.ai/api/v1/models)

Update Frequency: Weekly (OpenRouter updates rankings every week)

When to Use This Skill

Use this skill when you need to:

1. Select models for multi-model review

   • Plan reviewer needs current trending models
   • User asks "which models should I use for review?"
   • Updating model recommendations in agent workflows

2. Research AI coding trends

   • Developer wants to know most popular coding models
   • Comparing model capabilities (context, pricing, usage)
   • Identifying "best value" models for specific tasks

3. Update plugin documentation

   • Refreshing model lists in README files
   • Keeping agent prompts current with trending models
   • Documentation maintenance workflows

4. Cost optimization

   • Finding cheapest models with sufficient context
   • Comparing pricing across trending models
   • Budget planning for AI-assisted development

5. Model recommendations

   • User asks "what's the best model for X?"
   • Providing data-driven suggestions vs hardcoded lists
   • Offering alternatives based on requirements

Quick Start

Running the Script

Basic Usage:

bun run scripts/get-trending-models.ts

Output to File:

bun run scripts/get-trending-models.ts > trending-models.json

Pretty Print:

bun run scripts/get-trending-models.ts | jq '.'

Help:

bun run scripts/get-trending-models.ts --help

Expected Output

The script outputs structured JSON to stdout:

{
  "metadata": {
    "fetchedAt": "2025-11-14T10:30:00.000Z",
    "weekEnding": "2025-11-10",
    "category": "programming",
    "view": "trending"
  },
  "models": [
    {
      "rank": 1,
      "id": "x-ai/grok-code-fast-1",
      "name": "Grok Code Fast",
      "tokenUsage": 908664328688,
      "contextLength": 131072,
      "maxCompletionTokens": 32768,
      "pricing": {
        "prompt": 0.0000005,
        "completion": 0.000001,
        "promptPer1M": 0.5,
        "completionPer1M": 1.0
      }
    }
    // ... 8 more models
  ],
  "summary": {
    "totalTokens": 4500000000000,
    "topProvider": "x-ai",
    "averageContextLength": 98304,
    "priceRange": {
      "min": 0.5,
      "max": 15.0,
      "unit": "USD per 1M tokens"
    }
  }
}

Execution Time

Typical execution: 2-5 seconds

• Fetch rankings: ~1 second
• Fetch model details: ~1-2 seconds (parallel requests)
• Parse and format: <1 second

Output Format

Metadata Object

{
  fetchedAt: string;        // ISO 8601 timestamp of when data was fetched
  weekEnding: string;       // YYYY-MM-DD format, end of ranking week
  category: "programming";  // Fixed category
  view: "trending";         // Fixed view type
}

Models Array (9 items)

Each model contains:

{
  rank: number;             // 1-9, position in trending list
  id: string;               // OpenRouter model ID (e.g., "x-ai/grok-code-fast-1")
  name: string;             // Human-readable name (e.g., "Grok Code Fast")
  tokenUsage: number;       // Total tokens used last week
  contextLength: number;    // Maximum input tokens
  maxCompletionTokens: number; // Maximum output tokens
  pricing: {
    prompt: number;         // Per-token input cost (USD)
    completion: number;     // Per-token output cost (USD)
    promptPer1M: number;    // Input cost per 1M tokens (USD)
    completionPer1M: number; // Output cost per 1M tokens (USD)
  }
}

Summary Object

{
  totalTokens: number;      // Sum of token usage across top 9 models
  topProvider: string;      // Most represented provider (e.g., "x-ai")
  averageContextLength: number; // Average context window size
  priceRange: {
    min: number;            // Lowest prompt price per 1M tokens
    max: number;            // Highest prompt price per 1M tokens
    unit: "USD per 1M tokens";
  }
}

Integration Examples

Example 1: Dynamic Model Selection in Agent

Scenario: Plan reviewer needs current trending models for multi-model review

# In plan-reviewer agent workflow

STEP 1: Fetch trending models
- Execute: Bash("bun run scripts/get-trending-models.ts > /tmp/trending-models.json")
- Read: /tmp/trending-models.json

STEP 2: Parse and present to user
- Extract top 3-5 models from models array
- Display with context and pricing info
- Let user select preferred model(s)

STEP 3: Use selected model for review
- Pass model ID to Claudish proxy

Implementation:

// Agent reads output
const data = JSON.parse(bashOutput);

// Extract top 5 models
const topModels = data.models.slice(0, 5);

// Present to user
const modelList = topModels.map((m, i) =>
  `${i + 1}. **${m.name}** (\`${m.id}\`)
   - Context: ${m.contextLength.toLocaleString()} tokens
   - Pricing: $${m.pricing.promptPer1M}/1M input
   - Usage: ${(m.tokenUsage / 1e9).toFixed(1)}B tokens last week`
).join('\n\n');

// Ask user to select
const userChoice = await AskUserQuestion(`Select model for review:\n\n${modelList}`);

Example 2: Find Best Value Models

Scenario: User wants high-context models at lowest cost

# Fetch models and filter with jq
bun run scripts/get-trending-models.ts | jq '
  .models
  | map(select(.contextLength > 100000))
  | sort_by(.pricing.promptPer1M)
  | .[:3]
  | .[] | {
      name,
      id,
      contextLength,
      price: .pricing.promptPer1M
    }
'

Output:

how to use openrouter-trending-models
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use openrouter-trending-models on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add openrouter-trending-models
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/madappgang/claude-code --skill openrouter-trending-modelscopy
The skills CLI fetches openrouter-trending-models from GitHub repository madappgang/claude-code and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/openrouter-trending-models
Reload or restart Cursor to activate openrouter-trending-models. Access the skill through slash commands (e.g., /openrouter-trending-models) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?