Update README.md

README.md

@@ -1,10 +1,45 @@
-# OPENCHEF!: AGENTIC v2.0 - Multi-Agent Routing Dataset
-
-## Dataset Description
-
-This dataset contains simulated multi-agent sessions generated by OPENCHEF!: AGENTIC v2.0, designed for training AI agents in tool use and routing decisions.
+---
+license: mit
+task_categories:
+- question-answering
+- text-classification
+- text-generation
+tags:
+- conversational
+- question-answering
+- agentic
+- multi-agent
+- tool-calling
+- function-calling
+- routing
+- agent-routing
+- agentic workflows
+- multi-agent coordination
+---
+
+[![Website](https://img.shields.io/badge/webXOS.netlify.app-Explore_Apps-00d4aa?style=for-the-badge&logo=netlify&logoColor=white)](https://webxos.netlify.app)
+[![GitHub](https://img.shields.io/badge/GitHub-webxos/webxos-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/webxos/webxos)
+[![Hugging Face](https://img.shields.io/badge/Hugging_Face-🤗_webxos-FFD21E?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/webxos)
+[![Follow on X](https://img.shields.io/badge/Follow_@webxos-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/webxos)
+
+# OPENCHEF!: AGENTIC DATASET
+
+**Multi-agent routing & tool-calling traces** generated by OPENCHEF!:AGENTIC v2.0  
+Small simulated agent sessions with intentional routing errors (15% error rate).
+
+## Key Stats
+- **Total sessions**: 770
+- **Splits**: train (539 ≈70%), validation (127 ≈16.5%), test (104 ≈13.5%)
+- **Success rate**: 92.9%
+- **Average steps**: varies (typically 8–15 per session)
+- **License**: MIT
+
+This dataset contains simulated multi-agent sessions generated by OPENCHEF!: AGENTIC, 
+designed for training AI agents in tool use and routing decisions. You can download the app in the
+/chef/ folder of this repo and make your own similar datasets.
 
 ### Dataset Summary
+
 - **Total Sessions**: 770
 - **Train**: 539 (70.0%)
 - **Validation**: 127 (16.5%)
@@ -13,14 +48,21 @@ This dataset contains simulated multi-agent sessions generated by OPENCHEF!: AGE
 - **Generated**: 2026-01-30T10:54:18.423Z
 
 ### Supported Tasks
+
 - Tool calling with function-calling format
 - Multi-agent routing decisions
 - Error recovery from routing mistakes
-- Agent coordination
+- Agent coordinationExample Tasks:
+- Calendar: create/check events, conflict detection
+- Email: search, read, forward/send
+- Code: write/debug Node.js, TypeScript, Python scripts
+- Web: search, summarize pages
+- File/system: basic operations
 
 ### Data Fields
 
 Each example contains:
+
 - `id`: Unique session identifier
 - `split`: Dataset split (train/validation/test)
 - `task_description`: User instruction for the session
@@ -36,21 +78,8 @@ Each example contains:
 
 The dataset is split into train (70%), validation (15%), and test (15%) sets.
 
-## Usage
-
-### For Hugging Face Datasets
-```python
-from datasets import load_dataset
-
-# Load directly from JSONL files
-dataset = load_dataset('json', data_files={
-    'train': 'data/train.jsonl',
-    'validation': 'data/validation.jsonl',
-    'test': 'data/test.jsonl'
-})
-```
-
 ### For Training Agentic Models
+
 This dataset is optimized for training models on:
 - Tool calling behavior
 - Routing decisions between specialized agents
@@ -69,14 +98,14 @@ This dataset is optimized for training models on:
 
 If you use this dataset, please cite:
 ```
-@software{openchef2024,
-  title = {OPENCHEF!: AGENTIC v2.0 - Multi-Agent Routing Dataset Generator},
-  author = {OPENCLAW.ai Team},
-  year = {2024},
-  url = {https://github.com/openclaw/openchef}
+@software{openclaw_agentic_dataset,
+  title = {openclaw_agentic_dataset},
+  author = {webXOS},
+  year = {2026},
+  url = {webxos.netlify.app}
 }
 ```
 
 ## License
 
-MIT License
+MIT License