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:

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