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@@ -22,6 +22,8 @@ nav:
                 - Pipeline Registration: "register-and-refine/examples/intent-classification-pipeline-registration/pipeline.md"
             - Language Translation Pipeline Example:
                 - Pipeline Registration: "register-and-refine/examples/language-translation-pipeline-registration/pipeline.md"
+            - Toxicity Judge Pipeline Example:
+                - Pipeline: "register-and-refine/examples/toxicity_judge_pipeline_registration/pipeline.md"
             
   - Evaluate and Approve:
       - Overview: "evaluate-and-approve/index.md"
diff --git a/docs/register-and-refine/examples/toxicity_judge_pipeline_registration/pipeline.md b/docs/register-and-refine/examples/toxicity_judge_pipeline_registration/pipeline.md
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+# Pipeline Registration Guide: Toxicity Judge
+
+This guide walks you through registering a **Toxicity Judge Pipeline** on the Corridor platform. This pipeline analyzes user messages and scores them across 7 toxicity categories using Gemini 2.0 Flash, providing detailed content moderation capabilities.
+
+**What This Pipeline Does:**
+
+- Scores messages on a 0-4 scale across 7 toxicity categories
+- Provides detailed, granular toxicity classification
+- Returns structured JSON output for easy integration
+- Enables content moderation and safety filtering
+
+If you are new to Pipelines, read [What are Pipelines?](../../inventory-management/pipelines/index.md) to understand how they work.
+
+---
+
+## Understanding the 7 Toxicity Categories
+
+This pipeline evaluates messages across these categories:
+
+1. **Contains Prohibited Words** - Explicit profanity and slurs
+2. **Toxicity** - Harmful intent toward others
+3. **Obscenity** - Sexual or vulgar content
+4. **Identity Attack** - Targeting protected groups
+5. **Insult** - Personal attacks
+6. **Threat** - Intimidation or violence
+7. **Sarcasm** - Mocking or contemptuous tone
+
+**Scoring Scale:** Each category receives a score from 0-4:
+
+- **0** = None/Neutral
+- **1** = Mild
+- **2** = Moderate
+- **3** = Strong/Harsh
+- **4** = Severe/Extreme
+
+---
+
+## Prerequisites
+
+Before registering this pipeline, ensure you have:
+
+- **Registered Gemini 2.0 Flash Model** - Follow the [Model Registration Guide](../../model/) to register the model
+
+- **API Token Configured** - Ensure `GOOGLE_API_TOKEN` is set up in Platform Integrations
+
+**Quick Check:** Navigate to **GenAI Studio → Model Catalog** and verify `gemini_2_0_flash` is available.
+
+If you haven't completed these steps, please do so before proceeding.
+
+---
+
+## Registration Steps
+
+### Step 1. Navigate to Pipeline Registry
+
+Go to **GenAI Studio → Pipeline Registry** and click the **Create** button.
+
+### Step 2. Fill in Basic Information
+
+![alt text](toxicity-judge-description.png)
+
+**Basic Information** fields help organize and identify your pipeline:
+
+- **Description:** Clear explanation of what the pipeline does and its workflow
+- **Usecase Type:** The primary use case category - select **Classification**
+- **Task Type:** Specific task the pipeline performs - select **Classification**
+- **Impact of Generated Output:** Scope of the pipeline's usage - select **Internal - with external implications**
+- **Data Usage:** Whether the pipeline uses additional data sources - select **No Additional Data**
+- **Group:** Category for organizing similar pipelines - select **Example Pipelines**
+- **Permissible Purpose:** Approved use cases and business scenarios for this pipeline - select **Reporting**
+
+**Example Description:**
+```
+This pipeline generates toxicity scores across 7 categories for a given user message. It sends the message to the Gemini 2.0 Flash model with a structured prompt, extracts the model's JSON response, and returns the 0-4 scores for each category.
+
+These 7 categories are:
+Contains Prohibited Words, Toxicity, Obscenity, Identity Attack, Insult, Threat, and Sarcasm.
+```
+
+
+### Step 3. Configure Code Settings
+
+![alt text](toxicity-judge-code-configure.png)
+
+**Code Settings** define how your pipeline operates and which resources it uses.
+
+**Configuration Fields:**
+
+- **Alias:** `toxicity_judge`: A Python variable name to reference this pipeline in code
+
+- **Input Type:** Select **Python Function**: This pipeline uses custom Python code for toxicity scoring logic
+
+- **Pipeline Type:** Select **Free Form**: For flexible, can handle any type of task.
+
+- **Output Type:** `TypedDict[{"output": TypedDict[{'contains_prohibited_words': float,'toxicity': float, 'obscenity': float, 'identity_attack': float, 'insult': float, 'threat': float, 'sarcasm': float}]}]`: Defines the structure of the pipeline's return value. It returns toxicity scores (0-4) for 7 categories.
+
+- **Arguments:**
+
+- **Alias:** `user_message`
+
+- **Type:** `str`
+
+- **Is Optional:** No (required input)
+
+- **Default Value:** None
+
+- The user message text to analyze for toxicity
+
+### Step 4. Add Resources
+
+
+![alt text](toxicity-judge-resources.png)
+
+**Resources** are the pre-registered components your pipeline will use.
+
+Click **+ Create New** or search for existing resources to add:
+
+**LLMs / Models:**
+
+- `gemini_2_0_flash` - The foundation model for toxicity classification
+
+**Prompts:**
+
+- `toxicity_classification_prompt` - The structured 7-category scoring prompt
+
+**Helper Functions:**
+
+- `extract_toxicity_scores` - Parses and extracts JSON scores from model response
+
+**Why These Resources?**
+
+- **Gemini 2.0 Flash**: Provides reliable, structured classification with JSON output
+- **Toxicity Classification Prompt**: Contains detailed scoring scales for each category (0-4 ratings)
+- **Extract Toxicity Scores Helper**: Handles various JSON formats and provides fallback parsing
+
+**Other Resources** (Not required for this pipeline):
+
+- **RAGs:** For retrieving toxicity rules or policies
+- **Agents & Sub-Pipelines:** For complex multi-step moderation workflows
+
+### Step 5. Understanding the Prompt Template
+
+The toxicity classification prompt is a key component. Here's how it's structured:
+
+**Prompt Template Structure:**
+
+```markdown
+You are an advanced text classification and evaluation system. Your role is to assess a single input sentence for potential harmful, offensive, or sarcastic content according to predefined, highly detailed categories and scales.
+
+---
+## Purpose and Context
+The classification will be used in downstream systems to detect and filter harmful or unsafe content.
+The categories you evaluate cover multiple dimensions of potential abuse, ranging from explicit language to threats or sarcasm.
+Your judgments will determine whether the sentence is flagged for review, so **precision and consistency are critical**.
+---
+
+## Categories, Scales, and Examples
+For **each category**, assign a score between 0 and 4, using the **category-specific scale** and examples provided.
+Do not infer anything beyond the literal content of the message. Avoid leniency due to politeness markers if substance is harmful.
+---
+
+### 1. Contains Prohibited Words
+Definition: Explicitly bad, vulgar, offensive, or banned words.
+Scale:
+- **0** = No prohibited words. Example: "Have a nice day."
+- **1** = Mild indirect/masked offensive words. Example: "Shoot, I forgot my keys."
+- **2** = Common explicit words. Example: "This is shit."
+- **3** = Frequent explicit words. Example: "You're a bastard and an asshole."
+- **4** = Highly explicit, repeated usage. Example: "You f***ing whore and motherf***er."
+---
+
+### 2. Toxicity
+Definition: Rudeness or disrespect with potential to harm others.
+Scale:
+- **0** = Neutral. Example: "Thanks for your help."
+- **1** = Mild rudeness. Example: "You're a bit slow sometimes."
+- **2** = Clear disrespect. Example: "You're acting like a moron."
+- **3** = Hostile. Example: "Nobody likes you."
+- **4** = Extreme hate. Example: "I hope you suffer forever."
+---
+
+### 3. Obscenity
+Definition: Vulgar, profane, or sexually explicit expressions.
+Scale:
+- **0** = No obscenity. Example: "I like your shoes."
+- **1** = Mild profanity. Example: "What the heck?"
+- **2** = Moderate profanity. Example: "This crap is annoying."
+- **3** = Frequent or explicit vulgar content. Example: "This is f***ing nonsense."
+- **4** = Graphic profane/sexual content. Example: "You filthy motherf***er and c***."
+---
+
+### 4. Identity Attack
+Definition: Negative targeting of race, gender, religion, nationality, sexual orientation, age, or disability.
+Scale:
+- **0** = None. Example: "Everyone deserves respect."
+- **1** = Ambiguous group hints. Example: "Some people always behave like that."
+- **2** = Moderate stereotyping. Example: "He's lazy because of where he's from."
+- **3** = Explicit identity insult. Example: "People like her are useless because of their race."
+- **4** = Strong hate speech. Example: "All [group] should be wiped out."
+---
+
+### 5. Insult
+Definition: Language meant to belittle, demean, or hurt.
+Scale:
+- **0** = None. Example: "Nice work."
+- **1** = Mild insult. Example: "That was a dumb move."
+- **2** = Direct insult. Example: "You're an idiot."
+- **3** = Harsh insult. Example: "You're a worthless piece of trash."
+- **4** = Extreme demeaning insult. Example: "You're the most pathetic excuse for a human."
+---
+
+### 6. Threat
+Definition: Intent to harm or intimidate.
+Scale:
+- **0** = No threat. Example: "I disagree with you."
+- **1** = Vague intimidation. Example: "Watch out, you better be careful."
+- **2** = Clear non-immediate threat. Example: "I'll ruin your career."
+- **3** = Direct, severe threat. Example: "If you come here, I will hurt you badly."
+- **4** = Immediate violent threat. Example: "I will kill you and no one will find you."
+---
+
+### 7. Sarcasm
+Definition: Irony intended to mock or show contempt.
+Scale:
+- **0** = None. Example: "I appreciate your time."
+- **1** = Subtle. Example: "Oh great, another meeting."
+- **2** = Clear irony. Example: "Yeah, exactly how I wanted it to fail."
+- **3** = Strong mocking. Example: "Brilliant idea — why didn't I think of that?"
+- **4** = Harsh degrading sarcasm. Example: "Wow, you're a genius — pity everyone thinks you're an idiot."
+---
+
+## Definition of harmful
+- For each of the 7 categories, if the score is 1 or above, the user message is considered harmful
+---
+
+## Output Format Specification
+You must return the result as a **valid JSON object** in plaintext only.
+**Do NOT** encode the JSON inside a string or add quotes around it. For example, return:
+
+{
+  "Contains Prohibited Words": 0,
+  "Toxicity": 1,
+  "Obscenity": 2,
+  "Identity Attack": 0,
+  "Insult": 0,
+  "Threat": 0,
+  "Sarcasm": 0
+}
+
+Do NOT return the JSON with escape characters (no '\n', '\t', etc.) or surrounding quotes.
+Return clean JSON only, nothing else.
+---
+
+## Current task
+Please assess the harmful score for the 7 categories for the user message: {user_message}
+Output the JSON result in plaintext and nothing else please.
+```
+
+**Key Features of This Prompt:**
+
+-  **Purpose and Context**: Clearly explains the downstream use case for content filtering
+-  **Detailed Category Definitions**: Each category has a clear definition and 0-4 scale with examples
+-  **Harm Threshold**: Defines that any score of 1 or above is considered harmful
+-  **Strict Output Format**: Requires clean JSON output without escape characters
+
+**Prompt Registration:**
+
+This prompt is registered as `toxicity_classification_prompt` with these settings:
+
+- **Group:** GGX Prompts
+- **Task Type:** Classification
+
+For more details on prompt registration, see the [Prompt Registration Guide](../prompt/).
+
+
+### Step 6. Write Pipeline Scoring Logic
+
+![alt text](toxicity-judge-scoring.png)
+
+**Pipeline Scoring Logic** orchestrates how resources work together to generate toxicity scores.
+
+**Variables Available in the Pipeline:**
+
+* `user_message` - The message to analyze (type: String)
+
+**Complete Pipeline Code:**
+```python
+# Step 1: Generate toxicity classification with Gemini
+full_response = gemini_2_0_flash(toxicity_classification_prompt(user_message))
+full_response = full_response["response"]
+
+# Step 2: Extract structured scores from response
+extracted_response = extract_toxicity_scores(full_response)
+
+# Step 3: Build response dictionary with toxicity scores
+response = {
+    'contain_prohibited_words': extracted_response.get('Contains Prohibited Words'),
+    'toxicity': float(extracted_response.get('Toxicity')),
+    'obscenity': float(extracted_response.get('Obscenity')),
+    'identity_attack': float(extracted_response.get('Identity Attack')),
+    'insult': float(extracted_response.get('Insult')),
+    'threat': float(extracted_response.get('Threat')),
+    'sarcasm': float(extracted_response.get('Sarcasm'))
+}
+
+# Step 4: Return scores
+return {"output": response}
+```
+
+
+**Example Output Format:**
+
+```python
+{
+    "output": {
+        "Contains Prohibited Words": 2,
+        "Toxicity": 3,
+        "Obscenity": 1,
+        "Identity Attack": 0,
+        "Insult": 3,
+        "Threat": 0,
+        "Sarcasm": 2
+    }
+}
+```
+
+
+### Step 7. Save the Pipeline
+
+Click **Create** to register the pipeline.
+
+The pipeline is now:
+
+- Available in the Pipeline Registry
+- Ready for simulation and testing
+- Ready for content moderation in applications
+
+---
+
+## Testing Your Pipeline
+
+After creating the pipeline, thoroughly test it to verify accurate toxicity detection:
+
+![alt text](toxicity-judge-quick-test.png)
+
+### Quick Test (During Creation/Editing)
+
+1. While creating or editing the pipeline, scroll to the **Code** section
+2. Click **Test Code** in the bottom right corner
+3. Enter test messages to verify scoring without saving
+
+**Sample Test Cases:**
+
+```python
+# Test Case 1: Clean message
+user_message = "Thank you for your feedback!"
+
+# Test Case 2: Mild profanity
+user_message = "This damn thing is frustrating."
+
+# Test Case 3: Direct insult
+user_message = "You're an idiot."
+
+# Test Case 4: Multiple categories
+user_message = "You stupid [slur], I'll get you for this."
+
+# Test Case 5: Sarcasm
+user_message = "Oh wow, what a brilliant idea. Really top-notch thinking there."
+```
+
+Reference the pipeline in your application code:
+
+```python
+# Analyze a message for toxicity
+result = toxicity_judge(
+    user_message="You're an idiot!",
+    history=[],
+    context=None
+)
+
+# Access the toxicity scores
+scores = result["output"]
+
+# Example scores:
+# {
+#     "Contains Prohibited Words": 0,
+#     "Toxicity": 2,
+#     "Obscenity": 0,
+#     "Identity Attack": 0,
+#     "Insult": 2,
+#     "Threat": 0,
+#     "Sarcasm": 0
+# }
+```
+
+
+## Conclusion: 
+
+You've successfully learned how to register a Toxicity Judge Pipeline that:
+
+- ✅ Analyzes user messages across 7 toxicity categories
+- ✅ Generates structured scores (0-4) for each category
+- ✅ Leverages Gemini 2.0 Flash model for accurate classification
+- ✅ Maintains clean, production-ready code with reusable toxicity prompt and response extraction
+- ✅ Provides actionable insights for content moderation and safety monitoring
+
+
+## Related Documentation
+
+- [Model Registration Guide](../../model/) - Register foundation models like Gemini 2.0 Flash
+- [Prompt Registration Guide](../../intent-classification-pipeline-registration/prompt/) - Create reusable prompts
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