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Generate & Rank Animal Advocacy Content with Claude AI, Open Paws & Hugging Face
https://n8nworkflows.xyz/workflows/generate---rank-animal-advocacy-content-with-claude-ai--open-paws---hugging-face-6485
# Generate & Rank Animal Advocacy Content with Claude AI, Open Paws & Hugging Face
### 1. Workflow Overview
This workflow, titled **"Multi-Version Content Generator with AI Scoring & Advocacy Preference Ranking"**, is designed to generate multiple versions of animal advocacy-focused content, perform AI-driven scoring and ranking based on advocacy preferences, and aggregate the results for optimized outreach use. It targets organizations or individuals creating persuasive, research-based content for animal welfare campaigns, enabling data-driven selection of the most effective messaging variants.
The workflow is logically grouped into the following blocks:
- **1.1 Input Reception:** Receives user inputs defining the content generation parameters.
- **1.2 Content Generation & Research:** Uses a multi-tool research subworkflow and Claude AI (via OpenRouter) to create content variants.
- **1.3 Content Extraction & Splitting:** Parses AI output JSON and splits content into individual variants.
- **1.4 AI Scoring & Evaluation:** Invokes an evaluation subworkflow for scoring each content variant using Hugging Face and Open Paws models.
- **1.5 Aggregation & Final Output:** Aggregates scored content variants and prepares them for downstream use.
---
### 2. Block-by-Block Analysis
#### 1.1 Input Reception
**Overview:**
This block captures all necessary user inputs to customize the content generation process, including content type, tone, style, topic, and poster profile description.
**Nodes Involved:**
- When Executed by Another Workflow
- Sticky Note (User Input Configuration)
**Node Details:**
- **When Executed by Another Workflow**
- Type: Execute Workflow Trigger
- Role: Entry point that accepts input parameters from an external workflow or trigger.
- Configuration: Accepts inputs for `contentType`, `Poster’s profile description`, `Tone (e.g., formal, casual, friendly)`, `Style (e.g., concise, detailed, persuasive)`, and `Topic or Instructions`.
- Connections: Outputs data to "Create Content" node.
- Edge Cases: Missing or malformed inputs may cause downstream failures or incorrect content generation.
- **Sticky Note**
- Type: Sticky Note (Documentation)
- Role: Provides user instructions on filling input fields to ensure effective content generation.
- Content Notes: Emphasizes accuracy and clarity of inputs, lists required fields and their purpose.
---
#### 1.2 Content Generation & Research
**Overview:**
This block performs focused research and generates multiple full-length content versions based on the user inputs, leveraging AI language models and a research subworkflow.
**Nodes Involved:**
- Create Content
- OpenRouter Chat Model2
- Extract Content
- Sticky Note1 (Research Agent Usage)
**Node Details:**
- **Create Content**
- Type: Execute Workflow
- Role: Calls the "Multi-tool Research Agent" subworkflow that researches the topic via Serper (Google Search) and the Open Paws database, then generates content.
- Configuration: Passes user input parameters as `chatInput` along with a session ID for context.
- Connections: Output feeds into "Extract Content".
- Edge Cases: Subworkflow failures or API timeouts; data inconsistency if research sources are unavailable.
- Sub-workflow: Referenced workflow ID `k053fXGjIF7dUIQZ` (Multi-tool Research Agent).
- **OpenRouter Chat Model2**
- Type: LangChain OpenRouter Chat Model
- Role: Uses the Claude Sonnet 4 model to generate natural language content based on instructions.
- Configuration: Model set to `anthropic/claude-sonnet-4`, credentials configured for OpenRouter API.
- Connections: Outputs generated text to "Extract Content" node (via ai_languageModel connection).
- Edge Cases: API quota limits, authentication failures, or model unavailability.
- **Extract Content**
- Type: LangChain Information Extractor
- Role: Parses the JSON-formatted AI-generated content, ensuring exactly 10 variations are extracted as per instructions.
- Configuration: Uses system prompt to enforce strict JSON output without extraneous formatting. Parses input from the `output` field.
- Connections: Outputs parsed JSON to "Split Out Content".
- Edge Cases: Parsing errors if AI output is malformed or incomplete.
- **Sticky Note1**
- Type: Sticky Note
- Role: Documents the usage of the research agent subworkflow, highlighting research sources and generation process.
---
#### 1.3 Content Extraction & Splitting
**Overview:**
Separates the JSON array of content variants into individual items for subsequent scoring.
**Nodes Involved:**
- Split Out Content
**Node Details:**
- **Split Out Content**
- Type: Split Out
- Role: Splits the `output` array into singular content objects for processing each variant independently.
- Configuration: Splitting on the `output` field.
- Connections: Outputs individual content items to "Score Text".
- Edge Cases: Empty or malformed array input may cause node to fail or produce no output.
---
#### 1.4 AI Scoring & Evaluation
**Overview:**
Each content variant is scored individually by invoking an evaluation subworkflow that applies AI models to assess effectiveness and advocacy preference.
**Nodes Involved:**
- Score Text
- Sticky Note2 (Text Scoring and Evaluation)
**Node Details:**
- **Score Text**
- Type: Execute Workflow
- Role: Invokes the "Evaluate Animal Advocacy Text" subworkflow once per content item to score text quality and advocacy impact.
- Configuration: Runs in "each" mode, passing the `text` property of each content variant.
- Connections: Outputs scored results to "Aggregate".
- Edge Cases: Subworkflow or API failures, slow execution for large batch sizes.
- Sub-workflow: Referenced workflow ID `RyxYPLmF5sWDhC2Z` (Evaluate Animal Advocacy Text).
- **Sticky Note2**
- Type: Sticky Note
- Role: Describes the scoring process including AI models, metrics such as impact, persuasiveness, emotional resonance, and clarity used for evaluation.
---
#### 1.5 Aggregation & Final Output
**Overview:**
Aggregates all scored content variants into a single dataset that combines generated content with performance and prediction scores for downstream utilization.
**Nodes Involved:**
- Aggregate
- Sticky Note3 (Final Aggregation & Output)
**Node Details:**
- **Aggregate**
- Type: Aggregate
- Role: Combines all scored content items into a single output array for final delivery.
- Configuration: Uses `aggregateAllItemData` option to collect all incoming data.
- Connections: Output serves as the workflow’s final output or input for further processing workflows.
- Edge Cases: Large data size may affect performance or memory; partial inputs could cause incomplete aggregation.
- **Sticky Note3**
- Type: Sticky Note
- Role: Explains the purpose of aggregation and how the combined data can be used for automated posting, AI decision-making, and campaign management.
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|---------------------------|-------------------------------------|-------------------------------------------------|-----------------------------|---------------------------|----------------------------------------------------------------------------------------------|
| When Executed by Another Workflow | Execute Workflow Trigger | Entry point for receiving user content parameters | — | Create Content | |
| Sticky Note | Sticky Note | User input configuration instructions | — | — | # 📝 User Input Configuration - Explains fields to customize content generation |
| Create Content | Execute Workflow | Calls research subworkflow and generates content | When Executed by Another Workflow | Extract Content | # 🔍 Research Agent Usage - Describes research tools and AI content generation |
| OpenRouter Chat Model2 | LangChain OpenRouter Chat Model | AI content generation using Claude Sonnet 4 | — (used inside subworkflow) | Extract Content (ai_languageModel) | |
| Extract Content | LangChain Information Extractor | Parses JSON content output from AI model | Create Content, OpenRouter Chat Model2 | Split Out Content | |
| Split Out Content | Split Out | Splits JSON array into individual content items | Extract Content | Score Text | |
| Score Text | Execute Workflow | Invokes scoring subworkflow per content variant | Split Out Content | Aggregate | # 📊 Text Scoring and Evaluation - Details on scoring methods and metrics used |
| Aggregate | Aggregate | Combines scored content variants into final array | Score Text | — | # 📦 Final Aggregation & Output - Explains aggregation and downstream integration |
| Sticky Note1 | Sticky Note | Research agent usage documentation | — | — | # 🔍 Research Agent Usage - Details on research tools and subworkflow |
| Sticky Note2 | Sticky Note | Text scoring and evaluation documentation | — | — | # 📊 Text Scoring and Evaluation - Explains AI scoring and advocacy ranking |
| Sticky Note3 | Sticky Note | Final aggregation and output documentation | — | — | # 📦 Final Aggregation & Output - Describes final data usage and integration |
---
### 4. Reproducing the Workflow from Scratch
1. **Create Trigger Node:**
- Add an **Execute Workflow Trigger** node named `"When Executed by Another Workflow"`.
- Configure inputs to accept:
- `contentType` (string)
- `Poster’s profile description` (string)
- `Tone (e.g., formal, casual, friendly)` (string)
- `Style (e.g., concise, detailed, persuasive)` (string)
- `Topic or Instructions` (string)
2. **Add Sticky Note:**
- Add a **Sticky Note** near the trigger with instructions on user input fields and their purpose.
3. **Add Content Generation Node:**
- Add an **Execute Workflow** node named `"Create Content"`.
- Set it to call the research subworkflow with ID `k053fXGjIF7dUIQZ` (Multi-tool Research Agent).
- Map inputs by defining the `chatInput` string with embedded user inputs, following the prompt instructions (include content type, tone, style, topic, and poster profile description).
- Generate a unique `sessionId` using an expression like `={{ (Math.random().toString(36).substring(2) + Date.now().toString(36)) }}`.
- Connect `"When Executed by Another Workflow"` output to this node.
4. **Add AI Chat Model Node (Inside Subworkflow):**
- In the referenced subworkflow, configure the **LangChain OpenRouter Chat Model** node to use model `anthropic/claude-sonnet-4` with OpenRouter API credentials.
- This node generates the content text.
5. **Add Content Extraction Node:**
- Add a **LangChain Information Extractor** node named `"Extract Content"`.
- Set input to the AI-generated `output` field.
- Use a system prompt template to enforce strict JSON output with exactly 10 content variations.
- Connect `"Create Content"` output to this node.
6. **Add Content Split Node:**
- Add **Split Out** node named `"Split Out Content"`.
- Configure to split on the `output` field, separating content variants.
- Connect `"Extract Content"` output to this node.
7. **Add Scoring Workflow Node:**
- Add an **Execute Workflow** node named `"Score Text"`.
- Set to run in mode `"each"` to process each content variant separately.
- Call subworkflow ID `RyxYPLmF5sWDhC2Z` (Evaluate Animal Advocacy Text).
- Pass the `text` field of each content variant as input.
- Connect `"Split Out Content"` output to this node.
8. **Add Aggregation Node:**
- Add an **Aggregate** node named `"Aggregate"`.
- Configure it with option `aggregateAllItemData` to collect all scored results into one output array.
- Connect `"Score Text"` output to this node.
9. **Add Sticky Notes:**
- Add three **Sticky Notes**:
- Near `"Create Content"` node explaining research agent usage and sources.
- Near `"Score Text"` node explaining scoring metrics and AI models used.
- Near `"Aggregate"` node explaining final aggregation and downstream usage.
10. **Configure Credentials:**
- Set up **OpenRouter API** credentials for Claude AI integration.
- Ensure OAuth2 or API key credentials are configured for the research subworkflow APIs (Serper, Open Paws).
- Ensure Hugging Face or Open Paws credentials are set for the evaluation subworkflow.
11. **Finalize Connections:**
- Connect nodes as per flow:
When Executed by Another Workflow → Create Content → Extract Content → Split Out Content → Score Text → Aggregate
12. **Testing & Validation:**
- Test with sample inputs for content type, tone, style, and topic.
- Verify generated content variants are properly extracted and scored.
- Confirm aggregation outputs all scored content variants with expected fields.
---
### 5. General Notes & Resources
| Note Content | Context or Link |
|----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Multi-tool Research Agent subworkflow uses Serper (Google search) and Open Paws database for up-to-date animal advocacy research. | https://n8n.io/workflows/5588-multi-tool-research-agent-for-animal-advocacy-with-openrouter-serper-and-open-paws-db/ |
| Evaluate Animal Advocacy Text subworkflow applies Hugging Face models fine-tuned on advocacy datasets to score content effectiveness. | https://n8n.io/workflows/5587-evaluate-animal-advocacy-text-with-hugging-face-open-paws-ai-models/ |
| Use BB-code formatting in generated content (not Markdown) to meet platform compatibility requirements. | Specified in user instructions and AI prompts |
| Session ID generation uses a combination of random string and timestamp for uniqueness and session tracking. | Expression: `={{ (Math.random().toString(36).substring(2) + Date.now().toString(36)) }}` |
---
**Disclaimer:**
The text provided is exclusively derived from an automated workflow created with n8n, a tool for integration and automation. This process strictly complies with current content policies and does not include any illegal, offensive, or protected elements. All data processed is legal and public.