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WP Agentic Writer: Model Recommender Implementation Brief

Executive Summary

The Model Recommender is an interactive guided experience in the plugin settings that asks users 4–5 questions, then automatically generates and applies a personalized OpenRouter model configuration matching their budget and use case.

Why it exists: Users arrive with widely varying budgets (totally free → premium agencies). The 3 presets (Budget/Balanced/Premium) cover 80% of use cases, but the Recommender handles the remaining 20%: ultra-low-budget users, niche workflows, or custom combinations.

Where it lives: Settings page → "AI Model Configuration" card → Button: "Open Model Recommender"

Output: A filled-in model configuration that users can save or customize.

User Flow
System Prompt (Agent Logic)
Question Schema
Response JSON Format
Backend Implementation
Frontend UI Component
Conversation Examples
Edge Cases & Fallbacks
Development Checklist

User Flow

User clicks: [Open Model Recommender] button on settings page
    ↓
Modal opens with welcoming intro text
    ↓
Question 1: "What's your main goal?"
    ├─ Dev blog, agency client content, hobby writing, etc.
    ↓
Question 2: "What's your budget per article?"
    ├─ <$0.01, $0.01–0.05, $0.05–0.20, >$0.20
    ↓
Question 3: "How many articles per month?"
    ├─ 1–3, 4–8, 9–15, 15+
    ↓
Question 4: "Do you need AI-generated images?"
    ├─ Yes, No, Optional (depends on image quality)
    ↓
(Optional) Question 5: "Any specific provider preference?"
    ├─ No preference, Google Gemini, OpenAI, Anthropic
    ↓
Agent processes answers → generates recommended config JSON
    ↓
Modal displays: "Recommended config: [Name]"
    ├─ Chat: [Model]
    ├─ Writing: [Model]
    ├─ etc.
    ├─ Estimated cost: $X/article
    ↓
User: [Apply Config] or [Customize Manually] or [Cancel]
    ↓
Config saved to plugin options / UI refreshed

System Prompt (Agent Logic)

This prompt runs server-side to convert user answers into a model configuration.

System Prompt for Model Recommender Agent
═══════════════════════════════════════════════════════════════

You are a Model Configuration Recommender for WP Agentic Writer.

Your job: Convert user answers (goal, budget, volume, preferences) 
into an optimal OpenRouter model preset for 6 tasks:
- chat, clarity, planning, writing, refinement, image

You MUST:
1. Respect budget constraints strictly
2. Optimize cost:quality tradeoff for use case
3. Prefer Gemini Flash for planning/chat (best value)
4. Prefer Claude Sonnet for writing (industry standard)
5. Disable images if budget <$0.01/article
6. Return ONLY valid JSON (no extra text)

Budget tiers (for reference):
- Ultra (<$0.01/article): Gemini Flash only, no images
- Budget ($0.01–0.05): DeepSeek + Flash, FLUX.2 klein images
- Balanced ($0.05–0.20): Gemini Flash + Claude Sonnet, Riverflow images
- Premium (>$0.20): GPT-5.2/Opus + Flash, FLUX.2 max images

User answers will come as:
{
  "goal": "string",
  "budget_per_article": "string ($0.01–0.05, etc.)",
  "articles_per_month": "number",
  "images_needed": "yes/no/optional",
  "provider_preference": "string or null"
}

Return a JSON object with this schema:
{
  "preset_name": "string (e.g., 'Budget: Gemini Flash + DeepSeek')",
  "rationale": "string (brief explanation of why this config)",
  "estimated_cost_per_article": number (USD, including 5.5% fee),
  "models": {
    "chat": "model_slug_string",
    "clarity": "model_slug_string",
    "planning": "model_slug_string",
    "writing": "model_slug_string",
    "refinement": "model_slug_string",
    "image": "model_slug_string or null"
  },
  "recommendations": {
    "when_to_use": "string",
    "workflow_expectation": "string"
  }
}

Examples of model slugs (valid on OpenRouter):
- deepseek-v3
- google/gemini-3-flash-preview
- anthropic/claude-3.5-sonnet
- anthropic/claude-sonnet-4
- mistral/mistral-small
- openai/gpt-5.2
- black-forest-labs/flux.2-klein
- black-forest-labs/flux.2-max
- sourceful/riverflow-v2-max

If budget <$0.01/article:
- Recommend: Gemini Flash for ALL tasks
- Image: null (disabled)
- Estimated cost: ~$0.008–0.012/article

If budget $0.01–0.05/article:
- Chat/Planning: Gemini Flash
- Writing/Refinement: Mistral Small or DeepSeek
- Images: FLUX.2 klein or null
- Estimated cost: $0.03–0.05/article

If budget $0.05–0.20/article:
- Chat/Clarity/Planning: Gemini Flash
- Writing/Refinement: Claude Sonnet
- Images: Riverflow V2 Max or FLUX.2 Pro
- Estimated cost: $0.10–0.18/article

If budget >$0.20/article:
- Chat/Planning: Gemini Flash (cost doesn't improve)
- Clarity: Claude Sonnet 4 (nuanced feedback)
- Writing/Refinement: GPT-5.2 or Claude Opus
- Images: FLUX.2 max
- Estimated cost: $0.25–0.50+/article

Return valid JSON only. No markdown, no explanations outside the JSON.

Question Schema

Frontend: Questions Array (React/JS)

const recommenderQuestions = [
  {
    id: "goal",
    question: "What's your main goal for this plugin?",
    type: "radio",
    options: [
      { value: "dev_blog", label: "Dev blog / technical tutorials" },
      { value: "agency_content", label: "Agency client content" },
      { value: "hobby_writing", label: "Hobby writing / personal blog" },
      { value: "marketing", label: "Marketing / sales content" },
      { value: "research", label: "Research papers / long-form" }
    ],
    required: true
  },
  {
    id: "budget_per_article",
    question: "What's your budget limit per article?",
    type: "radio",
    options: [
      { value: "<0.01", label: "Ultra-budget (< $0.01 / article)" },
      { value: "0.01-0.05", label: "Budget ($0.01–$0.05 / article)" },
      { value: "0.05-0.20", label: "Balanced ($0.05–$0.20 / article)" },
      { value: ">0.20", label: "Premium (> $0.20 / article)" },
      { value: "no_limit", label: "No budget limit" }
    ],
    required: true
  },
  {
    id: "articles_per_month",
    question: "How many articles per month do you plan to generate?",
    type: "radio",
    options: [
      { value: "1-3", label: "1–3 articles/month" },
      { value: "4-8", label: "4–8 articles/month" },
      { value: "9-15", label: "9–15 articles/month" },
      { value: "15+", label: "15+ articles/month" }
    ],
    required: true
  },
  {
    id: "images_needed",
    question: "Do you need AI-generated images?",
    type: "radio",
    options: [
      { value: "yes", label: "Yes, high-quality hero images" },
      { value: "optional", label: "Optional / nice-to-have" },
      { value: "no", label: "No, I'll upload my own" }
    ],
    required: true
  },
  {
    id: "provider_preference",
    question: "(Optional) Any provider preference?",
    type: "radio",
    options: [
      { value: null, label: "No preference (use what's best)" },
      { value: "google", label: "Google (Gemini)" },
      { value: "openai", label: "OpenAI (GPT)" },
      { value: "anthropic", label: "Anthropic (Claude)" }
    ],
    required: false
  }
];

Response JSON Format

Backend: Agent Response

{
  "preset_name": "Balanced: Gemini Flash + Claude Sonnet + Riverflow",
  "rationale": "Your budget ($0.10–0.20/article) and moderate volume (8 articles/month) fit the Balanced preset perfectly. Gemini Flash handles chat/planning efficiently; Claude Sonnet is the industry standard for long-form writing. Riverflow images provide high quality at flat $0.03/image.",
  "estimated_cost_per_article": 0.1359,
  "models": {
    "chat": "google/gemini-3-flash-preview",
    "clarity": "google/gemini-3-flash-preview",
    "planning": "google/gemini-3-flash-preview",
    "writing": "anthropic/claude-3.5-sonnet",
    "refinement": "anthropic/claude-3.5-sonnet",
    "image": "sourceful/riverflow-v2-max"
  },
  "recommendations": {
    "when_to_use": "Regular blogging (4–10 articles/month), mixed content types, publishing to professional blog or portfolio.",
    "workflow_expectation": "User does one refinement cycle; publishes with high confidence."
  }
}

Backend Implementation

1. PHP Endpoint (WordPress REST API)

<?php
// File: includes/class-model-recommender.php

class Agentic_Writer_Model_Recommender {
    
    /**
     * Register REST endpoint
     */
    public static function register_endpoints() {
        register_rest_route(
            'agentic-writer/v1',
            '/recommend-models',
            [
                'methods' => 'POST',
                'callback' => [ self::class, 'get_recommendation' ],
                'permission_callback' => [ self::class, 'check_permissions' ],
                'args' => [
                    'goal' => ['required' => true],
                    'budget_per_article' => ['required' => true],
                    'articles_per_month' => ['required' => true],
                    'images_needed' => ['required' => true],
                    'provider_preference' => ['required' => false],
                ]
            ]
        );
    }
    
    /**
     * Get model recommendation from Claude/LLM
     */
    public static function get_recommendation( $request ) {
        $params = $request->get_json_params();
        
        // Build prompt with user answers
        $user_input = self::format_user_input( $params );
        
        // Call OpenRouter API (using Claude 3.5 Sonnet for reasoning)
        $recommendation = self::call_recommender_agent( $user_input );
        
        if ( is_wp_error( $recommendation ) ) {
            return new WP_REST_Response(
                ['error' => $recommendation->get_error_message()],
                500
            );
        }
        
        return new WP_REST_Response( $recommendation, 200 );
    }
    
    /**
     * Format user answers into structured prompt
     */
    private static function format_user_input( $params ) {
        return json_encode([
            'goal' => $params['goal'] ?? null,
            'budget_per_article' => $params['budget_per_article'] ?? null,
            'articles_per_month' => (int) $params['articles_per_month'] ?? null,
            'images_needed' => $params['images_needed'] ?? 'no',
            'provider_preference' => $params['provider_preference'] ?? null,
        ]);
    }
    
    /**
     * Call OpenRouter API with system prompt + user input
     */
    private static function call_recommender_agent( $user_input ) {
        $api_key = get_option( 'agentic_writer_openrouter_api_key' );
        
        if ( !$api_key ) {
            return new WP_Error(
                'no_api_key',
                'OpenRouter API key not configured.'
            );
        }
        
        $system_prompt = self::get_system_prompt();
        
        $response = wp_remote_post(
            'https://openrouter.ai/api/v1/chat/completions',
            [
                'headers' => [
                    'Authorization' => 'Bearer ' . $api_key,
                    'Content-Type' => 'application/json',
                ],
                'body' => json_encode([
                    'model' => 'anthropic/claude-3.5-sonnet',
                    'messages' => [
                        [
                            'role' => 'system',
                            'content' => $system_prompt,
                        ],
                        [
                            'role' => 'user',
                            'content' => "User answers: {$user_input}\n\nBased on these answers, generate a recommended model configuration.",
                        ]
                    ],
                    'temperature' => 0.2, // Deterministic
                    'max_tokens' => 500,
                ]),
            ]
        );
        
        if ( is_wp_error( $response ) ) {
            return $response;
        }
        
        $body = json_decode( wp_remote_retrieve_body( $response ), true );
        
        if ( !isset( $body['choices'][0]['message']['content'] ) ) {
            return new WP_Error(
                'invalid_response',
                'Invalid response from OpenRouter API.'
            );
        }
        
        $content = $body['choices'][0]['message']['content'];
        
        // Parse JSON from response
        $recommendation = json_decode( $content, true );
        
        if ( !$recommendation ) {
            return new WP_Error(
                'invalid_json',
                'Could not parse agent recommendation.'
            );
        }
        
        return $recommendation;
    }
    
    /**
     * Get system prompt (embedded in class)
     */
    private static function get_system_prompt() {
        return <<<'PROMPT'
You are a Model Configuration Recommender for WP Agentic Writer.

Your job: Convert user answers (goal, budget, volume, preferences) 
into an optimal OpenRouter model preset for 6 tasks:
- chat, clarity, planning, writing, refinement, image

You MUST:
1. Respect budget constraints strictly
2. Optimize cost:quality tradeoff for use case
3. Prefer Gemini Flash for planning/chat (best value)
4. Prefer Claude Sonnet for writing (industry standard)
5. Disable images if budget <$0.01/article
6. Return ONLY valid JSON (no extra text)

Budget tiers (for reference):
- Ultra (<$0.01/article): Gemini Flash only, no images
- Budget ($0.01–0.05): DeepSeek + Flash, FLUX.2 klein images
- Balanced ($0.05–0.20): Gemini Flash + Claude Sonnet, Riverflow images
- Premium (>$0.20): GPT-5.2/Opus + Flash, FLUX.2 max images

Valid model slugs (OpenRouter):
- deepseek-v3
- google/gemini-3-flash-preview
- anthropic/claude-3.5-sonnet
- anthropic/claude-sonnet-4
- mistral/mistral-small
- openai/gpt-5.2
- black-forest-labs/flux.2-klein
- black-forest-labs/flux.2-max
- sourceful/riverflow-v2-max

Return ONLY this JSON structure (no markdown, no extra text):
{
  "preset_name": "string",
  "rationale": "string",
  "estimated_cost_per_article": number,
  "models": {
    "chat": "string",
    "clarity": "string",
    "planning": "string",
    "writing": "string",
    "refinement": "string",
    "image": "string or null"
  },
  "recommendations": {
    "when_to_use": "string",
    "workflow_expectation": "string"
  }
}
PROMPT;
    }
    
    /**
     * Check user permissions
     */
    public static function check_permissions() {
        return current_user_can( 'manage_options' );
    }
}

// Hook to register on init
add_action( 'rest_api_init', [ 'Agentic_Writer_Model_Recommender', 'register_endpoints' ] );

Frontend UI Component

React Component (Gutenberg/Admin UI)

// File: components/ModelRecommender.jsx

import React, { useState } from 'react';
import { Button, Modal, RadioControl, Spinner } from '@wordpress/components';
import apiFetch from '@wordpress/api-fetch';

export const ModelRecommender = ({ onApplyConfig }) => {
  const [isOpen, setIsOpen] = useState(false);
  const [currentStep, setCurrentStep] = useState(0);
  const [answers, setAnswers] = useState({
    goal: null,
    budget_per_article: null,
    articles_per_month: null,
    images_needed: 'no',
    provider_preference: null,
  });
  const [loading, setLoading] = useState(false);
  const [recommendation, setRecommendation] = useState(null);

  const questions = [
    {
      id: 'goal',
      question: 'What\'s your main goal for this plugin?',
      options: [
        { value: 'dev_blog', label: 'Dev blog / technical tutorials' },
        { value: 'agency_content', label: 'Agency client content' },
        { value: 'hobby_writing', label: 'Hobby writing / personal blog' },
        { value: 'marketing', label: 'Marketing / sales content' },
        { value: 'research', label: 'Research papers / long-form' },
      ],
    },
    {
      id: 'budget_per_article',
      question: 'What\'s your budget limit per article?',
      options: [
        { value: '<0.01', label: 'Ultra-budget (< $0.01)' },
        { value: '0.01-0.05', label: 'Budget ($0.01–$0.05)' },
        { value: '0.05-0.20', label: 'Balanced ($0.05–$0.20)' },
        { value: '>0.20', label: 'Premium (> $0.20)' },
        { value: 'no_limit', label: 'No budget limit' },
      ],
    },
    {
      id: 'articles_per_month',
      question: 'How many articles per month?',
      options: [
        { value: '1-3', label: '1–3 articles/month' },
        { value: '4-8', label: '4–8 articles/month' },
        { value: '9-15', label: '9–15 articles/month' },
        { value: '15+', label: '15+ articles/month' },
      ],
    },
    {
      id: 'images_needed',
      question: 'Do you need AI-generated images?',
      options: [
        { value: 'yes', label: 'Yes, high-quality hero images' },
        { value: 'optional', label: 'Optional / nice-to-have' },
        { value: 'no', label: 'No, I\'ll upload my own' },
      ],
    },
    {
      id: 'provider_preference',
      question: '(Optional) Any provider preference?',
      options: [
        { value: null, label: 'No preference' },
        { value: 'google', label: 'Google Gemini' },
        { value: 'openai', label: 'OpenAI GPT' },
        { value: 'anthropic', label: 'Anthropic Claude' },
      ],
    },
  ];

  const handleAnswerChange = (answerId, value) => {
    setAnswers(prev => ({ ...prev, [answerId]: value }));
  };

  const handleNext = async () => {
    if (currentStep < questions.length - 1) {
      setCurrentStep(currentStep + 1);
    } else {
      // Submit to backend
      await submitRecommendation();
    }
  };

  const submitRecommendation = async () => {
    setLoading(true);
    try {
      const rec = await apiFetch({
        path: '/agentic-writer/v1/recommend-models',
        method: 'POST',
        data: answers,
      });
      setRecommendation(rec);
    } catch (error) {
      alert('Error generating recommendation: ' + error.message);
    } finally {
      setLoading(false);
    }
  };

  const handleApply = () => {
    onApplyConfig(recommendation);
    setIsOpen(false);
    setCurrentStep(0);
    setRecommendation(null);
  };

  const currentQuestion = questions[currentStep];
  const isAnswered = answers[currentQuestion.id] !== null;

  return (
    <>
      <Button 
        variant="primary" 
        onClick={() => setIsOpen(true)}
        style={{ marginTop: '10px' }}
      >
        Open Model Recommender
      </Button>

      {isOpen && (
        <Modal
          title="AI Model Recommender"
          onRequestClose={() => setIsOpen(false)}
          style={{ width: '500px' }}
        >
          {recommendation ? (
            // Results view
            <div style={{ padding: '20px' }}>
              <h3>Recommended Configuration</h3>
              <p><strong>{recommendation.preset_name}</strong></p>
              <p><em>{recommendation.rationale}</em></p>
              
              <table style={{ width: '100%', marginTop: '20px', borderCollapse: 'collapse' }}>
                <tbody>
                  <tr>
                    <td><strong>Chat</strong></td>
                    <td>{recommendation.models.chat}</td>
                  </tr>
                  <tr>
                    <td><strong>Clarity</strong></td>
                    <td>{recommendation.models.clarity}</td>
                  </tr>
                  <tr>
                    <td><strong>Planning</strong></td>
                    <td>{recommendation.models.planning}</td>
                  </tr>
                  <tr>
                    <td><strong>Writing</strong></td>
                    <td>{recommendation.models.writing}</td>
                  </tr>
                  <tr>
                    <td><strong>Refinement</strong></td>
                    <td>{recommendation.models.refinement}</td>
                  </tr>
                  <tr>
                    <td><strong>Image</strong></td>
                    <td>{recommendation.models.image || 'Disabled'}</td>
                  </tr>
                </tbody>
              </table>

              <p style={{ marginTop: '20px' }}>
                <strong>Estimated cost:</strong> ${recommendation.estimated_cost_per_article.toFixed(4)}/article
              </p>

              <div style={{ marginTop: '20px', display: 'flex', gap: '10px' }}>
                <Button 
                  variant="primary" 
                  onClick={handleApply}
                >
                  Apply This Configuration
                </Button>
                <Button 
                  variant="secondary" 
                  onClick={() => {
                    setRecommendation(null);
                    setCurrentStep(0);
                  }}
                >
                  Go Back
                </Button>
              </div>
            </div>
          ) : (
            // Questions view
            <div style={{ padding: '20px' }}>
              <p><strong>Question {currentStep + 1} of {questions.length}</strong></p>
              <h3>{currentQuestion.question}</h3>

              <RadioControl
                selected={answers[currentQuestion.id]}
                options={currentQuestion.options}
                onChange={(value) => handleAnswerChange(currentQuestion.id, value)}
              />

              <div style={{ marginTop: '20px', display: 'flex', gap: '10px' }}>
                <Button 
                  variant="primary" 
                  onClick={handleNext}
                  disabled={!isAnswered || loading}
                >
                  {loading ? <Spinner /> : (currentStep === questions.length - 1 ? 'Get Recommendation' : 'Next')}
                </Button>
                {currentStep > 0 && (
                  <Button 
                    variant="secondary" 
                    onClick={() => setCurrentStep(currentStep - 1)}
                  >
                    Back
                  </Button>
                )}
              </div>
            </div>
          )}
        </Modal>
      )}
    </>
  );
};

Conversation Examples

Example 1: Ultra-Budget Dev Blogger

User answers:

Goal: Dev blog
Budget: <$0.01/article
Volume: 2 articles/month
Images: No
Provider: No preference

Agent response:

{
  "preset_name": "Ultra-Budget: Gemini Flash Only",
  "rationale": "Your ultra-low budget and dev blog focus make Gemini 3 Flash the perfect fit. It's optimized for coding + reasoning at nearly free cost. No images keeps cost minimal.",
  "estimated_cost_per_article": 0.0082,
  "models": {
    "chat": "google/gemini-3-flash-preview",
    "clarity": "google/gemini-3-flash-preview",
    "planning": "google/gemini-3-flash-preview",
    "writing": "google/gemini-3-flash-preview",
    "refinement": "google/gemini-3-flash-preview",
    "image": null
  },
  "recommendations": {
    "when_to_use": "Solo dev bloggers with minimal budget; testing the plugin before investing.",
    "workflow_expectation": "Output may need 1–2 editing rounds before publishing."
  }
}

Example 2: Agency with Quality Focus

User answers:

Goal: Agency client content
Budget: >$0.20/article
Volume: 20 articles/month
Images: Yes, high-quality
Provider: No preference

Agent response:

{
  "preset_name": "Premium: GPT-5.2 + Gemini Flash + FLUX.2 max",
  "rationale": "Your agency volume and quality-first mindset justify premium models. GPT-5.2 delivers publication-ready prose; FLUX.2 max generates hero images. Your clients will notice the difference.",
  "estimated_cost_per_article": 0.3142,
  "models": {
    "chat": "google/gemini-3-flash-preview",
    "clarity": "anthropic/claude-sonnet-4",
    "planning": "google/gemini-3-flash-preview",
    "writing": "openai/gpt-5.2",
    "refinement": "openai/gpt-5.2",
    "image": "black-forest-labs/flux.2-max"
  },
  "recommendations": {
    "when_to_use": "Agencies publishing 15+ articles/month; thought leadership, flagship posts, client deliverables.",
    "workflow_expectation": "Minimal editing required; publish with high confidence."
  }
}

Example 3: Balanced Hobbyist

User answers:

Goal: Hobby writing / personal blog
Budget: $0.05–$0.20/article
Volume: 4 articles/month
Images: Optional
Provider: Google Gemini

Agent response:

{
  "preset_name": "Balanced: Gemini Flash + Claude Sonnet + Riverflow",
  "rationale": "Your modest volume and provider preference align perfectly with the Balanced preset. All-Gemini planning + Claude writing = great quality at fair cost. Riverflow images optional but affordable.",
  "estimated_cost_per_article": 0.1359,
  "models": {
    "chat": "google/gemini-3-flash-preview",
    "clarity": "google/gemini-3-flash-preview",
    "planning": "google/gemini-3-flash-preview",
    "writing": "anthropic/claude-3.5-sonnet",
    "refinement": "anthropic/claude-3.5-sonnet",
    "image": "sourceful/riverflow-v2-max"
  },
  "recommendations": {
    "when_to_use": "Regular hobby blogging or personal portfolio; professional output without premium cost.",
    "workflow_expectation": "One refinement cycle typical; polished final product."
  }
}

Edge Cases & Fallbacks

1. API failure (OpenRouter down)

Fallback behavior:

// If recommender API fails, load from static presets
if ( is_wp_error( $recommendation ) ) {
    $recommendation = self::get_fallback_preset_by_budget( 
        $params['budget_per_article']
    );
}

2. Budget = "no_limit"

Logic:

Treat as ">$0.20" (Premium tier).
Recommend frontier models + best images.

3. Invalid JSON from agent

Fallback:

Log error to error_log.
Show message: "Recommender had trouble generating a config. Try adjusting your answers."
Offer manual preset picker instead.

4. User cancels mid-flow

Behavior:

Modal closes cleanly.
No settings changed.
State resets for next usage.

Development Checklist

Backend setup
- Create includes/class-model-recommender.php
- Register REST endpoint /agentic-writer/v1/recommend-models
- Implement call_recommender_agent() with OpenRouter API
- Add error handling + fallbacks
- Test with real OpenRouter key
System prompt
- Write and refine system prompt (see above)
- Test with 3–5 example user inputs
- Verify JSON output is parseable
- Ensure cost estimates match model-presets.md
Frontend component
- Create React component: ModelRecommender.jsx
- Implement question flow (5 questions)
- Add results display with config summary
- Wire up "Apply Configuration" button
- Test modal UX (open/close/back/next)
Integration
- Add button to settings page → "AI Model Configuration" card
- Wire up onApplyConfig callback to save settings
- Refresh model selectors after apply
- Show success toast message
Testing
- Test all 5 questions with valid OpenRouter API key
- Test budget boundary cases ($0.01, $0.05, $0.20)
- Test edge cases (ultra-budget, no-limit, specific providers)
- Test API failure + fallback behavior
- Test invalid JSON response handling
- Test user cancellation mid-flow
Documentation
- Add to settings help: "Click 'Open Model Recommender' for personalized setup"
- Document system prompt in code comments
- Add troubleshooting guide: "Recommender not working? Check API key."

Security Considerations

API Key Protection:
- Only server-side calls to OpenRouter (key never exposed to client)
- REST endpoint requires manage_options capability
Rate Limiting:
- Add WordPress nonce to prevent CSRF
- Limit calls to 1 per minute per user
Input Validation:
- Validate all user answers against whitelist
- Reject invalid budget ranges

// Example: Add nonce validation
register_rest_route(
    'agentic-writer/v1',
    '/recommend-models',
    [
        'callback' => [ self::class, 'get_recommendation' ],
        'permission_callback' => function () {
            return current_user_can( 'manage_options' ) 
                && isset( $_REQUEST['_wpnonce'] )
                && wp_verify_nonce( $_REQUEST['_wpnonce'], 'agentic_recommender' );
        }
    ]
);

Cost Estimation for Development

Task	Time	Notes
Backend endpoint + system prompt	3–4 hours	Includes testing with OpenRouter
React component + UI	3–4 hours	Includes modal, form flow, validation
Integration with settings page	1–2 hours	Wire up button, callbacks, save logic
Testing + refinement	2–3 hours	Edge cases, error handling
Total	9–13 hours	Can be split across team

Next Steps (Priority Order)

Refine system prompt – Test with Claude to ensure it generates correct JSON
Build backend endpoint – Implement /recommend-models route with error handling
Build React component – Create modal UI with question flow
Integration – Wire up to settings page
Testing – Full QA with real OpenRouter API

Document version: 1.0
Date: January 22, 2026
Author: WP Agentic Writer Product Team
Status: Ready for Development

29 KiB Raw Blame History Unescape Escape

WP Agentic Writer: Model Recommender Implementation Brief

Executive Summary

Table of Contents

User Flow

System Prompt (Agent Logic)

Question Schema

Frontend: Questions Array (React/JS)

Response JSON Format

Backend: Agent Response

Backend Implementation

1. PHP Endpoint (WordPress REST API)

Frontend UI Component

React Component (Gutenberg/Admin UI)

Conversation Examples

Example 1: Ultra-Budget Dev Blogger

Example 2: Agency with Quality Focus

Example 3: Balanced Hobbyist

Edge Cases & Fallbacks

1. API failure (OpenRouter down)

2. Budget = "no_limit"

3. Invalid JSON from agent

4. User cancels mid-flow

Development Checklist

Security Considerations

Cost Estimation for Development

Next Steps (Priority Order)

29 KiB

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