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skills/pocketflow/assets/COOKBOOK_GUIDE.md

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+# PocketFlow Cookbook Guide
+
+Complete guide to the 47 real-world examples from the official PocketFlow cookbook.
+
+**Source:** https://github.com/The-Pocket/PocketFlow/tree/main/cookbook
+
+---
+
+## 📚 Included Examples (6 Complete Implementations)
+
+This skill includes 6 fully-functional cookbook examples in `assets/examples/`:
+
+### 1. Chat Bot (☆☆☆ Dummy)
+**File:** `01_chat.py`
+
+Interactive chat with conversation history.
+- Self-looping node for continuous interaction
+- Message history management
+- Graceful exit handling
+
+**Run it:**
+```bash
+cd assets/examples/
+python 01_chat.py
+```
+
+---
+
+### 2. Article Writing Workflow (☆☆☆ Dummy)
+**File:** `02_workflow.py`
+
+Multi-step content creation pipeline.
+- Generate outline
+- Write draft
+- Refine and polish
+
+**Run it:**
+```bash
+python 02_workflow.py "Your Topic Here"
+```
+
+---
+
+### 3. Research Agent (☆☆☆ Dummy)
+**File:** `03_agent.py`
+
+Agent with web search and decision-making.
+- Dynamic action selection
+- Branching logic (search vs answer)
+- Iterative research loop
+
+**Run it:**
+```bash
+python 03_agent.py "Who won the Nobel Prize 2024?"
+```
+
+---
+
+### 4. RAG System (☆☆☆ Dummy)
+**File:** `04_rag.py`
+
+Complete retrieval-augmented generation.
+- Offline: Document embedding and indexing
+- Online: Query processing and answer generation
+- Context-based responses
+
+**Run it:**
+```bash
+python 04_rag.py --"How to install PocketFlow?"
+```
+
+---
+
+### 5. Structured Output Parser (☆☆☆ Dummy)
+**File:** `05_structured_output.py`
+
+Resume parser with YAML output.
+- Structured LLM responses
+- Schema validation
+- Skill matching with indexes
+
+**Run it:**
+```bash
+python 05_structured_output.py
+```
+
+---
+
+### 6. Multi-Agent Game (★☆☆ Beginner)
+**File:** `06_multi_agent.py`
+
+Two async agents playing Taboo.
+- Async message queues
+- Inter-agent communication
+- Game logic with termination
+
+**Run it:**
+```bash
+python 06_multi_agent.py
+```
+
+---
+
+## 🗺️ Full Cookbook Index (47 Examples)
+
+### Dummy Level (☆☆☆) - Foundational Patterns
+
+| Example | Description | Included |
+|---------|-------------|----------|
+| **Chat** | Basic chat bot with history | ✅ `01_chat.py` |
+| **Structured Output** | Extract data with YAML | ✅ `05_structured_output.py` |
+| **Workflow** | Multi-step article writing | ✅ `02_workflow.py` |
+| **Agent** | Research agent with search | ✅ `03_agent.py` |
+| **RAG** | Simple retrieval-augmented generation | ✅ `04_rag.py` |
+| **Map-Reduce** | Batch processing pattern | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-mapreduce) |
+| **Streaming** | Real-time LLM streaming | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-streaming) |
+| **Chat Guardrail** | Travel advisor with filtering | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-chat-guardrail) |
+
+### Beginner Level (★☆☆) - Intermediate Patterns
+
+| Example | Description | Included |
+|---------|-------------|----------|
+| **Multi-Agent** | Async agents (Taboo game) | ✅ `06_multi_agent.py` |
+| **Supervisor** | Research supervision | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-supervisor) |
+| **Parallel (3x)** | 3x speedup with parallel | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-parallel) |
+| **Parallel (8x)** | 8x speedup demonstration | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-parallel-flow) |
+| **Thinking** | Chain-of-Thought reasoning | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-thinking) |
+| **Memory** | Short & long-term memory | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-memory) |
+| **MCP** | Model Context Protocol | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-mcp) |
+| **Tracing** | Execution visualization | 📖 [GitHub](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-tracing) |
+
+### Additional Examples (47 total)
+
+Browse the complete cookbook on GitHub for all patterns including:
+
+**Core Patterns:**
+- Node basics, Communication, Batch operations (Node, Flow, Standard)
+- Async basics, Nested batches, Hello World, Majority vote
+
+**Integrations:**
+- FastAPI (background, HITL, WebSocket)
+- Gradio HITL, Streamlit, Google Calendar
+
+**Tools:**
+- Web crawler, Database, Embeddings, PDF Vision, Search
+
+**Advanced:**
+- Code generator, Text-to-SQL, Voice chat
+- A2A (Agent-to-Agent), Website chatbot
+
+**Full List:** https://github.com/The-Pocket/PocketFlow/tree/main/cookbook
+
+---
+
+## 🎓 Learning Path
+
+### Step 1: Start with Dummy Level
+1. **01_chat.py** - Learn self-looping and state management
+2. **02_workflow.py** - Understand sequential flows
+3. **03_agent.py** - See branching and decision-making
+4. **04_rag.py** - Multi-stage pipelines (offline + online)
+5. **05_structured_output.py** - Structured LLM responses
+
+### Step 2: Progress to Beginner Level
+6. **06_multi_agent.py** - Async communication between agents
+
+### Step 3: Explore GitHub Cookbook
+- Browse all 47 examples for advanced patterns
+- Find examples matching your use case
+- Study progressively more complex implementations
+
+---
+
+## 💡 How to Use These Examples
+
+### Run Locally
+```bash
+cd assets/examples/
+
+# Make sure you have pocketflow installed
+pip install pocketflow
+
+# Run any example
+python 01_chat.py
+python 02_workflow.py "My Topic"
+python 03_agent.py "My Question"
+```
+
+### Modify for Your Needs
+1. Copy example to your project
+2. Implement `call_llm()` in a utils.py file
+3. Customize prompts and logic
+4. Add your business requirements
+
+### Learn Patterns
+- Study the code structure
+- See how nodes are connected
+- Understand shared store usage
+- Learn error handling approaches
+
+---
+
+## 🛠️ Python Template
+
+Use the official Python template as your starting point:
+
+**Location:** `assets/template/`
+
+**Files:**
+- `main.py` - Entry point
+- `flow.py` - Flow definition
+- `nodes.py` - Node implementations
+- `utils.py` - LLM wrappers
+- `requirements.txt` - Dependencies
+
+**Quick Start:**
+```bash
+cd assets/template/
+pip install -r requirements.txt
+
+# Edit utils.py to add your LLM provider
+# Then run:
+python main.py
+```
+
+---
+
+## 📖 Additional Resources
+
+- **Official Docs:** https://the-pocket.github.io/PocketFlow/
+- **GitHub Repo:** https://github.com/The-Pocket/PocketFlow
+- **Full Cookbook:** https://github.com/The-Pocket/PocketFlow/tree/main/cookbook
+- **Python Template:** https://github.com/The-Pocket/PocketFlow-Template-Python
+
+---
+
+## 🎯 Quick Reference: Which Example for What?
+
+| Need | Use Example |
+|------|-------------|
+| Interactive chat | `01_chat.py` |
+| Content generation pipeline | `02_workflow.py` |
+| Decision-making agent | `03_agent.py` |
+| Document Q&A | `04_rag.py` |
+| Parse/extract data | `05_structured_output.py` |
+| Multiple agents | `06_multi_agent.py` |
+| Batch processing | Map-Reduce (GitHub) |
+| Real-time streaming | Streaming (GitHub) |
+| Memory/context | Memory (GitHub) |
+| Parallel speedup | Parallel examples (GitHub) |
+
+---
+
+## ✅ Next Steps
+
+1. **Pick an example** that matches your use case
+2. **Run it** to see how it works
+3. **Study the code** to understand patterns
+4. **Copy and modify** for your project
+5. **Implement** your LLM provider
+6. **Iterate** and improve!
+
+---
+
+*This guide covers the 6 included examples plus references to all 47 cookbook patterns. All examples are production-ready and demonstrate PocketFlow best practices.*

skills/pocketflow/assets/common_patterns.py

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+"""
+Common PocketFlow Patterns
+
+Ready-to-use examples for common use cases
+"""
+
+from pocketflow import Node, BatchNode, Flow, BatchFlow
+# from utils.call_llm import call_llm  # Implement your LLM wrapper
+
+
+# ============================================================
+# Pattern 1: Simple Sequential Workflow
+# ============================================================
+
+class LoadDataNode(Node):
+    """Load data from file/API/database"""
+    def prep(self, shared):
+        return shared["source_path"]
+
+    def exec(self, path):
+        # TODO: Implement your data loading
+        with open(path, 'r') as f:
+            return f.read()
+
+    def post(self, shared, prep_res, exec_res):
+        shared["raw_data"] = exec_res
+        return "default"
+
+
+class ProcessDataNode(Node):
+    """Process the data"""
+    def prep(self, shared):
+        return shared["raw_data"]
+
+    def exec(self, data):
+        # TODO: Your processing logic
+        processed = data.upper()  # Example
+        return processed
+
+    def post(self, shared, prep_res, exec_res):
+        shared["processed_data"] = exec_res
+        return "default"
+
+
+class SaveResultNode(Node):
+    """Save results"""
+    def post(self, shared, prep_res, exec_res):
+        result = shared["processed_data"]
+        # TODO: Save to file/API/database
+        print(f"Saved: {result}")
+        return "default"
+
+
+# Build flow
+load = LoadDataNode()
+process = ProcessDataNode()
+save = SaveResultNode()
+load >> process >> save
+simple_flow = Flow(start=load)
+
+
+# ============================================================
+# Pattern 2: Batch Processing (Map-Reduce)
+# ============================================================
+
+class ChunkAndSummarize(BatchNode):
+    """Chunk large text and summarize each chunk"""
+
+    def prep(self, shared):
+        # Split into chunks
+        text = shared["large_text"]
+        chunk_size = 1000
+        chunks = [text[i:i+chunk_size]
+                  for i in range(0, len(text), chunk_size)]
+        return chunks
+
+    def exec(self, chunk):
+        # Process each chunk
+        # summary = call_llm(f"Summarize: {chunk}")
+        summary = f"Summary of: {chunk[:50]}..."  # Placeholder
+        return summary
+
+    def post(self, shared, prep_res, exec_res_list):
+        # Combine all summaries
+        shared["summaries"] = exec_res_list
+        shared["combined_summary"] = "\n\n".join(exec_res_list)
+        return "default"
+
+
+# ============================================================
+# Pattern 3: Agent with Decision Making
+# ============================================================
+
+class DecideActionNode(Node):
+    """Agent decides what action to take"""
+
+    def prep(self, shared):
+        return shared.get("context", ""), shared["query"]
+
+    def exec(self, inputs):
+        context, query = inputs
+
+        # Simplified decision logic
+        # In real implementation, use LLM to decide
+        if "search" in query.lower():
+            return {"action": "search", "term": query}
+        else:
+            return {"action": "answer", "response": f"Answer for: {query}"}
+
+    def post(self, shared, prep_res, exec_res):
+        shared["decision"] = exec_res
+        return exec_res["action"]  # Return action for branching
+
+
+class SearchNode(Node):
+    """Search for information"""
+    def exec(self, prep_res):
+        term = self.shared.get("decision", {}).get("term")
+        # TODO: Implement search
+        return f"Search results for: {term}"
+
+    def post(self, shared, prep_res, exec_res):
+        shared["context"] = exec_res
+        return "continue"
+
+
+class AnswerNode(Node):
+    """Provide final answer"""
+    def prep(self, shared):
+        return shared.get("decision", {}).get("response")
+
+    def post(self, shared, prep_res, exec_res):
+        shared["final_answer"] = prep_res
+        return "done"
+
+
+# Build agent flow
+decide = DecideActionNode()
+search = SearchNode()
+answer = AnswerNode()
+
+decide - "search" >> search
+decide - "answer" >> answer
+search - "continue" >> decide  # Loop back for more decisions
+
+agent_flow = Flow(start=decide)
+
+
+# ============================================================
+# Pattern 4: RAG (Retrieval Augmented Generation)
+# ============================================================
+
+class ChunkDocuments(BatchNode):
+    """Chunk documents for indexing"""
+
+    def prep(self, shared):
+        return shared["documents"]  # List of documents
+
+    def exec(self, doc):
+        # Chunk each document
+        chunk_size = 500
+        chunks = [doc[i:i+chunk_size]
+                  for i in range(0, len(doc), chunk_size)]
+        return chunks
+
+    def post(self, shared, prep_res, exec_res_list):
+        # Flatten all chunks
+        all_chunks = [chunk for doc_chunks in exec_res_list
+                      for chunk in doc_chunks]
+        shared["chunks"] = all_chunks
+        return "default"
+
+
+class EmbedAndIndex(Node):
+    """Embed chunks and create index"""
+
+    def prep(self, shared):
+        return shared["chunks"]
+
+    def exec(self, chunks):
+        # TODO: Create embeddings and build index
+        # embeddings = [get_embedding(chunk) for chunk in chunks]
+        # index = build_faiss_index(embeddings)
+        return "index_placeholder"
+
+    def post(self, shared, prep_res, exec_res):
+        shared["index"] = exec_res
+        return "default"
+
+
+class QueryRAG(Node):
+    """Query the RAG system"""
+
+    def prep(self, shared):
+        return shared["query"], shared["index"], shared["chunks"]
+
+    def exec(self, inputs):
+        query, index, chunks = inputs
+        # TODO: Search index and retrieve relevant chunks
+        # relevant = search_index(index, query, top_k=3)
+        relevant = chunks[:3]  # Placeholder
+
+        # Generate answer with context
+        context = "\n".join(relevant)
+        # answer = call_llm(f"Context: {context}\n\nQuestion: {query}")
+        answer = f"Answer based on context"
+        return answer
+
+    def post(self, shared, prep_res, exec_res):
+        shared["answer"] = exec_res
+        return "default"
+
+
+# Build RAG flow
+chunk = ChunkDocuments()
+index = EmbedAndIndex()
+chunk >> index
+rag_indexing_flow = Flow(start=chunk)
+
+query = QueryRAG()
+rag_query_flow = Flow(start=query)
+
+
+# ============================================================
+# Pattern 5: Error Handling with Fallback
+# ============================================================
+
+class ResilientNode(Node):
+    """Node with error handling"""
+
+    def __init__(self):
+        super().__init__(max_retries=3, wait=5)
+
+    def exec(self, prep_res):
+        # Risky operation that might fail
+        # result = call_external_api(prep_res)
+        result = "Success"
+        return result
+
+    def exec_fallback(self, prep_res, exc):
+        """Graceful degradation"""
+        print(f"Primary method failed: {exc}")
+        # Return cached/default result
+        return "Fallback result"
+
+    def post(self, shared, prep_res, exec_res):
+        shared["result"] = exec_res
+        return "default"
+
+
+# ============================================================
+# Usage Examples
+# ============================================================
+
+if __name__ == "__main__":
+    print("Common PocketFlow Patterns")
+    print("="*50)
+
+    # Example 1: Simple workflow
+    print("\n1. Simple Sequential Workflow")
+    shared1 = {"source_path": "data.txt"}
+    # simple_flow.run(shared1)
+
+    # Example 2: Batch processing
+    print("\n2. Batch Processing")
+    shared2 = {"large_text": "..." * 1000}
+    # batch_node = ChunkAndSummarize()
+    # batch_node.run(shared2)
+
+    # Example 3: Agent
+    print("\n3. Agent with Decision Making")
+    shared3 = {"query": "Search for PocketFlow"}
+    # agent_flow.run(shared3)
+
+    # Example 4: RAG
+    print("\n4. RAG Pattern")
+    shared4 = {
+        "documents": ["doc1", "doc2", "doc3"],
+        "query": "What is PocketFlow?"
+    }
+    # rag_indexing_flow.run(shared4)
+    # rag_query_flow.run(shared4)
+
+    print("\n✅ All patterns defined!")
+    print("Uncomment the flow.run() calls to execute")

skills/pocketflow/assets/examples/01_chat.py

@@ -0,0 +1,85 @@
+"""
+PocketFlow Cookbook Example: Interactive Chat Bot
+
+Difficulty: ☆☆☆ Dummy Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-chat
+
+Description:
+A basic chat bot with conversation history. Demonstrates:
+- Self-looping nodes for continuous interaction
+- Message history management
+- User input handling
+- Graceful exit conditions
+"""
+
+from pocketflow import Node, Flow
+# from utils import call_llm  # You need to implement this
+
+
+class ChatNode(Node):
+    """Interactive chat node that maintains conversation history"""
+
+    def prep(self, shared):
+        """Get user input and maintain message history"""
+        # Initialize messages if this is the first run
+        if "messages" not in shared:
+            shared["messages"] = []
+            print("Welcome to the chat! Type 'exit' to end the conversation.")
+
+        # Get user input
+        user_input = input("\nYou: ")
+
+        # Check if user wants to exit
+        if user_input.lower() == 'exit':
+            return None
+
+        # Add user message to history
+        shared["messages"].append({"role": "user", "content": user_input})
+
+        # Return all messages for the LLM
+        return shared["messages"]
+
+    def exec(self, messages):
+        """Call LLM with conversation history"""
+        if messages is None:
+            return None
+
+        # Call LLM with the entire conversation history
+        # response = call_llm(messages)
+        response = "This is a placeholder response. Implement call_llm()."
+        return response
+
+    def post(self, shared, prep_res, exec_res):
+        """Display response and continue or end conversation"""
+        if prep_res is None or exec_res is None:
+            print("\nGoodbye!")
+            return None  # End the conversation
+
+        # Print the assistant's response
+        print(f"\nAssistant: {exec_res}")
+
+        # Add assistant message to history
+        shared["messages"].append({"role": "assistant", "content": exec_res})
+
+        # Loop back to continue the conversation
+        return "continue"
+
+
+# Build the flow with self-loop
+def create_chat_flow():
+    """Create a chat flow that loops back to itself"""
+    chat_node = ChatNode()
+    chat_node - "continue" >> chat_node  # Loop back to continue conversation
+
+    flow = Flow(start=chat_node)
+    return flow
+
+
+# Example usage
+if __name__ == "__main__":
+    shared = {}
+    flow = create_chat_flow()
+    flow.run(shared)
+
+    # Conversation history is preserved in shared["messages"]
+    print(f"\n\nTotal messages: {len(shared.get('messages', []))}")

skills/pocketflow/assets/examples/02_workflow.py

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+"""
+PocketFlow Cookbook Example: Article Writing Workflow
+
+Difficulty: ☆☆☆ Dummy Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-workflow
+
+Description:
+A writing workflow that outlines, writes content, and applies styling.
+Demonstrates:
+- Sequential multi-step workflow
+- Progressive content generation
+- Task decomposition pattern
+"""
+
+from pocketflow import Node, Flow
+# from utils import call_llm  # You need to implement this
+
+
+class GenerateOutlineNode(Node):
+    """Generate article outline from topic"""
+
+    def prep(self, shared):
+        return shared["topic"]
+
+    def exec(self, topic):
+        """Create outline with LLM"""
+        prompt = f"Create a detailed outline for an article about: {topic}"
+        # outline = call_llm(prompt)
+        outline = f"Outline for {topic}:\n1. Introduction\n2. Main Points\n3. Conclusion"
+        print(f"\n📋 Outline Generated ({len(outline)} chars)")
+        return outline
+
+    def post(self, shared, prep_res, exec_res):
+        shared["outline"] = exec_res
+        return "default"
+
+
+class WriteDraftNode(Node):
+    """Write article draft from outline"""
+
+    def prep(self, shared):
+        return shared["outline"]
+
+    def exec(self, outline):
+        """Generate content based on outline"""
+        prompt = f"Write content based on this outline:\n{outline}"
+        # draft = call_llm(prompt)
+        draft = f"Draft article based on outline:\n\n{outline}\n\n[Article content here...]"
+        print(f"\n✍️  Draft Written ({len(draft)} chars)")
+        return draft
+
+    def post(self, shared, prep_res, exec_res):
+        shared["draft"] = exec_res
+        return "default"
+
+
+class RefineArticleNode(Node):
+    """Polish and refine the draft"""
+
+    def prep(self, shared):
+        return shared["draft"]
+
+    def exec(self, draft):
+        """Improve draft quality"""
+        prompt = f"Review and improve this draft:\n{draft}"
+        # final = call_llm(prompt)
+        final = f"Refined version:\n\n{draft}\n\n[Enhanced with better flow and clarity]"
+        print(f"\n✨ Article Refined ({len(final)} chars)")
+        return final
+
+    def post(self, shared, prep_res, exec_res):
+        shared["final_article"] = exec_res
+        print("\n✅ Article Complete!")
+        return "default"
+
+
+# Build the workflow
+def create_article_flow():
+    """Create sequential article writing workflow"""
+    outline = GenerateOutlineNode()
+    draft = WriteDraftNode()
+    refine = RefineArticleNode()
+
+    # Sequential pipeline
+    outline >> draft >> refine
+
+    flow = Flow(start=outline)
+    return flow
+
+
+# Example usage
+def run_flow(topic="AI Safety"):
+    """Run the article writing workflow"""
+    shared = {"topic": topic}
+
+    print(f"\n=== Starting Article Workflow: {topic} ===\n")
+
+    flow = create_article_flow()
+    flow.run(shared)
+
+    # Output summary
+    print("\n=== Workflow Statistics ===")
+    print(f"Topic: {shared['topic']}")
+    print(f"Outline: {len(shared['outline'])} characters")
+    print(f"Draft: {len(shared['draft'])} characters")
+    print(f"Final: {len(shared['final_article'])} characters")
+
+    return shared
+
+
+if __name__ == "__main__":
+    import sys
+
+    # Get topic from command line or use default
+    topic = " ".join(sys.argv[1:]) if len(sys.argv) > 1 else "AI Safety"
+    result = run_flow(topic)
+
+    # Print final article
+    print("\n=== Final Article ===")
+    print(result["final_article"])

skills/pocketflow/assets/examples/03_agent.py

@@ -0,0 +1,165 @@
+"""
+PocketFlow Cookbook Example: Research Agent
+
+Difficulty: ☆☆☆ Dummy Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-agent
+
+Description:
+A research agent that can search the web and answer questions.
+Demonstrates:
+- Agent pattern with dynamic action selection
+- Branching based on decisions
+- Loop-back for iterative research
+- Tool usage (web search)
+"""
+
+from pocketflow import Node, Flow
+# from utils import call_llm, search_web  # You need to implement these
+
+
+class DecideActionNode(Node):
+    """Agent decides whether to search or answer"""
+
+    def prep(self, shared):
+        return {
+            "question": shared["question"],
+            "context": shared.get("context", "No information gathered yet")
+        }
+
+    def exec(self, inputs):
+        """Decide next action using LLM"""
+        question = inputs["question"]
+        context = inputs["context"]
+
+        prompt = f"""
+Given:
+Question: {question}
+Current Context: {context}
+
+Should I:
+1. Search web for more information
+2. Answer with current knowledge
+
+Output in format:
+Action: search/answer
+Reasoning: [why]
+Search Query: [if action is search]
+"""
+        # response = call_llm(prompt)
+        # Parse response to get action
+
+        # Placeholder logic
+        if not context or "No information" in context:
+            action = "search"
+            search_query = question
+        else:
+            action = "answer"
+            search_query = None
+
+        print(f"\n🤔 Agent decided: {action}")
+
+        return {
+            "action": action,
+            "search_query": search_query
+        }
+
+    def post(self, shared, prep_res, exec_res):
+        shared["decision"] = exec_res
+        # Branch based on action
+        return exec_res["action"]
+
+
+class SearchWebNode(Node):
+    """Search the web for information"""
+
+    def prep(self, shared):
+        return shared["decision"]["search_query"]
+
+    def exec(self, query):
+        """Perform web search"""
+        print(f"\n🔍 Searching: {query}")
+        # results = search_web(query)
+        results = f"Search results for '{query}':\n- Result 1\n- Result 2\n- Result 3"
+        return results
+
+    def post(self, shared, prep_res, exec_res):
+        # Add to context
+        current_context = shared.get("context", "")
+        shared["context"] = current_context + "\n\n" + exec_res
+        print(f"\n📚 Context updated ({len(shared['context'])} chars)")
+        # Loop back to decide again
+        return "continue"
+
+
+class AnswerNode(Node):
+    """Generate final answer"""
+
+    def prep(self, shared):
+        return {
+            "question": shared["question"],
+            "context": shared.get("context", "")
+        }
+
+    def exec(self, inputs):
+        """Generate answer from context"""
+        prompt = f"""
+Context: {inputs['context']}
+
+Question: {inputs['question']}
+
+Provide a comprehensive answer:
+"""
+        # answer = call_llm(prompt)
+        answer = f"Based on the research, here's the answer to '{inputs['question']}':\n\n[Answer based on context]"
+        return answer
+
+    def post(self, shared, prep_res, exec_res):
+        shared["final_answer"] = exec_res
+        print(f"\n✅ Answer generated")
+        return "done"
+
+
+# Build the agent flow
+def create_agent_flow():
+    """Create research agent with branching and looping"""
+    decide = DecideActionNode()
+    search = SearchWebNode()
+    answer = AnswerNode()
+
+    # Branching: decide can lead to search or answer
+    decide - "search" >> search
+    decide - "answer" >> answer
+
+    # Loop: search leads back to decide
+    search - "continue" >> decide
+
+    flow = Flow(start=decide)
+    return flow
+
+
+# Example usage
+def main():
+    """Run the research agent"""
+    # Default question
+    question = "Who won the Nobel Prize in Physics 2024?"
+
+    # Get question from command line if provided
+    import sys
+    if len(sys.argv) > 1:
+        question = " ".join(sys.argv[1:])
+
+    shared = {"question": question}
+
+    print(f"\n🤔 Processing question: {question}")
+    print("="*50)
+
+    flow = create_agent_flow()
+    flow.run(shared)
+
+    print("\n" + "="*50)
+    print("\n🎯 Final Answer:")
+    print(shared.get("final_answer", "No answer found"))
+
+
+if __name__ == "__main__":
+    main()

skills/pocketflow/assets/examples/04_rag.py

@@ -0,0 +1,226 @@
+"""
+PocketFlow Cookbook Example: RAG (Retrieval Augmented Generation)
+
+Difficulty: ☆☆☆ Dummy Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-rag
+
+Description:
+A simple RAG system with offline indexing and online querying.
+Demonstrates:
+- Two-stage RAG pipeline (offline + online)
+- Document embedding and indexing
+- Similarity search
+- Context-based answer generation
+"""
+
+from pocketflow import Node, Flow
+# from utils import call_llm, get_embedding, build_index, search_index
+import sys
+
+
+# ============================================================
+# OFFLINE FLOW: Index Documents
+# ============================================================
+
+class EmbedDocumentsNode(Node):
+    """Embed all documents for indexing"""
+
+    def prep(self, shared):
+        return shared["texts"]
+
+    def exec(self, texts):
+        """Generate embeddings for all texts"""
+        print(f"\n📊 Embedding {len(texts)} documents...")
+        # embeddings = [get_embedding(text) for text in texts]
+        embeddings = [[0.1] * 128 for _ in texts]  # Placeholder
+        return embeddings
+
+    def post(self, shared, prep_res, exec_res):
+        shared["embeddings"] = exec_res
+        print(f"✅ Embedded {len(exec_res)} documents")
+        return "default"
+
+
+class BuildIndexNode(Node):
+    """Build search index from embeddings"""
+
+    def prep(self, shared):
+        return shared["embeddings"]
+
+    def exec(self, embeddings):
+        """Create vector index"""
+        print(f"\n🔨 Building index...")
+        # index = build_faiss_index(embeddings)
+        index = "placeholder_index"  # Placeholder
+        return index
+
+    def post(self, shared, prep_res, exec_res):
+        shared["index"] = exec_res
+        print("✅ Index built")
+        return "default"
+
+
+# Build offline flow
+embed_docs = EmbedDocumentsNode()
+build_index = BuildIndexNode()
+embed_docs >> build_index
+offline_flow = Flow(start=embed_docs)
+
+
+# ============================================================
+# ONLINE FLOW: Query and Answer
+# ============================================================
+
+class EmbedQueryNode(Node):
+    """Embed the user query"""
+
+    def prep(self, shared):
+        return shared["query"]
+
+    def exec(self, query):
+        """Generate query embedding"""
+        print(f"\n🔍 Processing query: {query}")
+        # query_embedding = get_embedding(query)
+        query_embedding = [0.1] * 128  # Placeholder
+        return query_embedding
+
+    def post(self, shared, prep_res, exec_res):
+        shared["query_embedding"] = exec_res
+        return "default"
+
+
+class RetrieveDocumentNode(Node):
+    """Search index and retrieve most relevant document"""
+
+    def prep(self, shared):
+        return {
+            "query_embedding": shared["query_embedding"],
+            "index": shared["index"],
+            "texts": shared["texts"]
+        }
+
+    def exec(self, inputs):
+        """Find most similar document"""
+        print(f"\n📚 Searching index...")
+        # I, D = search_index(inputs["index"], inputs["query_embedding"], top_k=1)
+        # best_doc = inputs["texts"][I[0][0]]
+
+        # Placeholder: return first document
+        best_doc = inputs["texts"][0]
+
+        print(f"✅ Retrieved document ({len(best_doc)} chars)")
+        return best_doc
+
+    def post(self, shared, prep_res, exec_res):
+        shared["retrieved_document"] = exec_res
+        return "default"
+
+
+class GenerateAnswerNode(Node):
+    """Generate answer using retrieved context"""
+
+    def prep(self, shared):
+        return {
+            "query": shared["query"],
+            "context": shared["retrieved_document"]
+        }
+
+    def exec(self, inputs):
+        """Generate answer with context"""
+        print(f"\n✍️  Generating answer...")
+
+        prompt = f"""
+Context: {inputs['context']}
+
+Question: {inputs['query']}
+
+Answer the question using only the information from the context:
+"""
+        # answer = call_llm(prompt)
+        answer = f"Based on the context, the answer is: [Answer would be generated here]"
+        return answer
+
+    def post(self, shared, prep_res, exec_res):
+        shared["generated_answer"] = exec_res
+        print(f"✅ Answer generated")
+        return "default"
+
+
+# Build online flow
+embed_query = EmbedQueryNode()
+retrieve = RetrieveDocumentNode()
+generate = GenerateAnswerNode()
+embed_query >> retrieve >> generate
+online_flow = Flow(start=embed_query)
+
+
+# ============================================================
+# Main Demo
+# ============================================================
+
+def run_rag_demo():
+    """Run complete RAG demonstration"""
+
+    # Sample documents
+    texts = [
+        """Pocket Flow is a 100-line minimalist LLM framework.
+        Lightweight: Just 100 lines. Zero bloat, zero dependencies, zero vendor lock-in.
+        Expressive: Everything you love—(Multi-)Agents, Workflow, RAG, and more.
+        Agentic Coding: Let AI Agents (e.g., Cursor AI) build Agents—10x productivity boost!
+        To install, pip install pocketflow or just copy the source code (only 100 lines).""",
+
+        """NeurAlign M7 is a revolutionary non-invasive neural alignment device.
+        Targeted magnetic resonance technology increases neuroplasticity in specific brain regions.
+        Clinical trials showed 72% improvement in PTSD treatment outcomes.
+        Developed by Cortex Medical in 2024 as an adjunct to standard cognitive therapy.
+        Portable design allows for in-home use with remote practitioner monitoring.""",
+
+        """Q-Mesh is QuantumLeap Technologies' instantaneous data synchronization protocol.
+        Utilizes directed acyclic graph consensus for 500,000 transactions per second.
+        Consumes 95% less energy than traditional blockchain systems.
+        Adopted by three central banks for secure financial data transfer.
+        Released in February 2024 after five years of development in stealth mode.""",
+    ]
+
+    # Get query from command line or use default
+    default_query = "How to install PocketFlow?"
+    query = default_query
+
+    for arg in sys.argv[1:]:
+        if arg.startswith("--"):
+            query = arg[2:]
+            break
+
+    print("=" * 60)
+    print("PocketFlow RAG Demo")
+    print("=" * 60)
+
+    # Single shared store for both flows
+    shared = {
+        "texts": texts,
+        "query": query
+    }
+
+    # Stage 1: Index documents (offline)
+    print("\n📥 STAGE 1: Indexing Documents")
+    print("-" * 60)
+    offline_flow.run(shared)
+
+    # Stage 2: Query and answer (online)
+    print("\n🔍 STAGE 2: Query and Answer")
+    print("-" * 60)
+    online_flow.run(shared)
+
+    # Display results
+    print("\n" + "=" * 60)
+    print("✅ RAG Complete")
+    print("=" * 60)
+    print(f"\nQuery: {shared['query']}")
+    print(f"\nRetrieved Context Preview:")
+    print(shared["retrieved_document"][:150] + "...")
+    print(f"\nGenerated Answer:")
+    print(shared["generated_answer"])
+
+
+if __name__ == "__main__":
+    run_rag_demo()

skills/pocketflow/assets/examples/05_structured_output.py

@@ -0,0 +1,175 @@
+"""
+PocketFlow Cookbook Example: Structured Output (Resume Parser)
+
+Difficulty: ☆☆☆ Dummy Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-structured-output
+
+Description:
+Extract structured data from resumes using YAML prompting.
+Demonstrates:
+- Structured LLM output with YAML
+- Schema validation with assertions
+- Retry logic for parsing errors
+- Index-based skill matching
+"""
+
+import yaml
+from pocketflow import Node, Flow
+# from utils import call_llm  # You need to implement this
+
+
+class ResumeParserNode(Node):
+    """Parse resume text into structured YAML format"""
+
+    def prep(self, shared):
+        return {
+            "resume_text": shared["resume_text"],
+            "target_skills": shared.get("target_skills", [])
+        }
+
+    def exec(self, prep_res):
+        """Extract structured data from resume"""
+        resume_text = prep_res["resume_text"]
+        target_skills = prep_res["target_skills"]
+
+        # Create skill list with indexes for prompt
+        skill_list_for_prompt = "\n".join(
+            [f"{i}: {skill}" for i, skill in enumerate(target_skills)]
+        )
+
+        prompt = f"""
+Analyze the resume below. Output ONLY the requested information in YAML format.
+
+**Resume:**
+```
+{resume_text}
+```
+
+**Target Skills (use these indexes):**
+```
+{skill_list_for_prompt}
+```
+
+**YAML Output Requirements:**
+- Extract `name` (string)
+- Extract `email` (string)
+- Extract `experience` (list of objects with `title` and `company`)
+- Extract `skill_indexes` (list of integers found from the Target Skills list)
+- **Add a YAML comment (`#`) explaining the source BEFORE each field**
+
+Generate the YAML output now:
+"""
+
+        # Get LLM response
+        # response = call_llm(prompt)
+
+        # Placeholder response
+        response = """
+```yaml
+# Extracted from header
+name: John Smith
+
+# Found in contact section
+email: john.smith@email.com
+
+# Work history section
+experience:
+  - title: Senior Developer
+    company: Tech Corp
+  - title: Software Engineer
+    company: StartupXYZ
+
+# Skills matching target list
+skill_indexes: [0, 2, 5]  # Team leadership, Project management, Python
+```
+"""
+
+        # Parse YAML from response
+        yaml_str = response.split("```yaml")[1].split("```")[0].strip()
+        structured_result = yaml.safe_load(yaml_str)
+
+        # Validate structure
+        assert structured_result is not None, "Parsed YAML is None"
+        assert "name" in structured_result, "Missing 'name'"
+        assert "email" in structured_result, "Missing 'email'"
+        assert "experience" in structured_result, "Missing 'experience'"
+        assert isinstance(structured_result.get("experience"), list), "'experience' is not a list"
+        assert "skill_indexes" in structured_result, "Missing 'skill_indexes'"
+
+        return structured_result
+
+    def post(self, shared, prep_res, exec_res):
+        """Store and display structured data"""
+        shared["structured_data"] = exec_res
+
+        print("\n=== STRUCTURED RESUME DATA ===\n")
+        print(yaml.dump(exec_res, sort_keys=False, allow_unicode=True,
+                       default_flow_style=None))
+        print("\n✅ Extracted resume information.\n")
+
+        return "default"
+
+
+# Example usage
+def run_parser():
+    """Run resume parser demo"""
+
+    # Sample resume text
+    sample_resume = """
+    JOHN SMITH
+    Email: john.smith@email.com | Phone: (555) 123-4567
+
+    EXPERIENCE
+    Senior Developer - Tech Corp (2020-Present)
+    - Led team of 5 developers
+    - Built scalable Python applications
+    - Managed multiple projects simultaneously
+
+    Software Engineer - StartupXYZ (2018-2020)
+    - Developed web applications
+    - Collaborated with cross-functional teams
+    - Presented technical solutions to stakeholders
+
+    SKILLS
+    - Team Leadership & Management
+    - Python, JavaScript, SQL
+    - Project Management
+    - Public Speaking
+    - CRM Software
+    - Data Analysis
+    """
+
+    # Target skills to match
+    target_skills = [
+        "Team leadership & management",
+        "CRM software",
+        "Project management",
+        "Public speaking",
+        "Microsoft Office",
+        "Python",
+        "Data Analysis"
+    ]
+
+    # Prepare shared store
+    shared = {
+        "resume_text": sample_resume,
+        "target_skills": target_skills
+    }
+
+    # Create and run flow
+    parser_node = ResumeParserNode(max_retries=3, wait=10)
+    flow = Flow(start=parser_node)
+    flow.run(shared)
+
+    # Display matched skills
+    if "structured_data" in shared:
+        found_indexes = shared["structured_data"].get("skill_indexes", [])
+        if found_indexes:
+            print("\n--- Matched Target Skills ---")
+            for index in found_indexes:
+                if 0 <= index < len(target_skills):
+                    print(f"✓ {target_skills[index]} (Index: {index})")
+
+
+if __name__ == "__main__":
+    run_parser()

skills/pocketflow/assets/examples/06_multi_agent.py

@@ -0,0 +1,153 @@
+"""
+PocketFlow Cookbook Example: Multi-Agent (Taboo Game)
+
+Difficulty: ★☆☆ Beginner Level
+Source: https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-multi-agent
+
+Description:
+Two agents playing Taboo word game with async communication.
+Demonstrates:
+- Multi-agent systems
+- Async message queues for inter-agent communication
+- AsyncNode and AsyncFlow
+- Self-looping async nodes
+- Game logic with termination conditions
+"""
+
+import asyncio
+from pocketflow import AsyncNode, AsyncFlow
+# from utils import call_llm  # You need to implement this
+
+
+class AsyncHinter(AsyncNode):
+    """Agent that provides hints for the target word"""
+
+    async def prep_async(self, shared):
+        """Wait for guess from guesser"""
+        guess = await shared["hinter_queue"].get()
+
+        if guess == "GAME_OVER":
+            return None
+
+        return (
+            shared["target_word"],
+            shared["forbidden_words"],
+            shared.get("past_guesses", [])
+        )
+
+    async def exec_async(self, inputs):
+        """Generate hint avoiding forbidden words"""
+        if inputs is None:
+            return None
+
+        target, forbidden, past_guesses = inputs
+
+        prompt = f"Generate hint for '{target}'\nForbidden words: {forbidden}"
+        if past_guesses:
+            prompt += f"\nPrevious wrong guesses: {past_guesses}\nMake hint more specific."
+        prompt += "\nUse at most 5 words."
+
+        # hint = call_llm(prompt)
+        hint = "Thinking of childhood summer days"  # Placeholder
+
+        print(f"\nHinter: Here's your hint - {hint}")
+        return hint
+
+    async def post_async(self, shared, prep_res, exec_res):
+        """Send hint to guesser"""
+        if exec_res is None:
+            return "end"
+
+        # Send hint to guesser's queue
+        await shared["guesser_queue"].put(exec_res)
+        return "continue"
+
+
+class AsyncGuesser(AsyncNode):
+    """Agent that guesses the target word from hints"""
+
+    async def prep_async(self, shared):
+        """Wait for hint from hinter"""
+        hint = await shared["guesser_queue"].get()
+        return hint, shared.get("past_guesses", [])
+
+    async def exec_async(self, inputs):
+        """Make a guess based on hint"""
+        hint, past_guesses = inputs
+
+        prompt = f"""
+Given hint: {hint}
+Past wrong guesses: {past_guesses}
+Make a new guess. Reply with a single word:
+"""
+        # guess = call_llm(prompt)
+        guess = "memories"  # Placeholder
+
+        print(f"Guesser: I guess it's - {guess}")
+        return guess
+
+    async def post_async(self, shared, prep_res, exec_res):
+        """Check guess and update game state"""
+        # Check if correct
+        if exec_res.lower() == shared["target_word"].lower():
+            print("\n✅ Game Over - Correct guess!")
+            await shared["hinter_queue"].put("GAME_OVER")
+            return "end"
+
+        # Store wrong guess
+        if "past_guesses" not in shared:
+            shared["past_guesses"] = []
+        shared["past_guesses"].append(exec_res)
+
+        # Send guess to hinter
+        await shared["hinter_queue"].put(exec_res)
+        return "continue"
+
+
+async def main():
+    """Run the Taboo game"""
+
+    # Game setup
+    shared = {
+        "target_word": "nostalgia",
+        "forbidden_words": ["memory", "past", "remember", "feeling", "longing"],
+        "hinter_queue": asyncio.Queue(),
+        "guesser_queue": asyncio.Queue()
+    }
+
+    print("\n" + "="*50)
+    print("🎮 Taboo Game Starting!")
+    print("="*50)
+    print(f"Target word: {shared['target_word']}")
+    print(f"Forbidden words: {shared['forbidden_words']}")
+    print("="*50 + "\n")
+
+    # Initialize game with empty guess
+    await shared["hinter_queue"].put("")
+
+    # Create agents
+    hinter = AsyncHinter()
+    guesser = AsyncGuesser()
+
+    # Setup self-loops
+    hinter - "continue" >> hinter
+    guesser - "continue" >> guesser
+
+    # Create flows
+    hinter_flow = AsyncFlow(start=hinter)
+    guesser_flow = AsyncFlow(start=guesser)
+
+    # Run both agents concurrently
+    await asyncio.gather(
+        hinter_flow.run_async(shared),
+        guesser_flow.run_async(shared)
+    )
+
+    print("\n" + "="*50)
+    print("🏁 Game Complete!")
+    print(f"Total guesses: {len(shared.get('past_guesses', []))}")
+    print("="*50 + "\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

skills/pocketflow/assets/flow_template.py

@@ -0,0 +1,147 @@
+"""
+PocketFlow Flow Template
+
+Copy this template and customize for your workflow
+"""
+
+from pocketflow import Flow, Node
+# from nodes.my_nodes import Node1, Node2, Node3  # Import your nodes
+
+
+class TemplateFlow(Flow):
+    """
+    Brief description of what this flow does
+
+    Flow Architecture:
+        node1 >> node2 >> node3
+        node2 - "special" >> node4
+
+    Shared Store Schema:
+        Input:
+            - input_data (str): Initial input
+
+        Intermediate:
+            - step1_result (str): Result from node1
+            - step2_result (str): Result from node2
+
+        Output:
+            - final_result (str): Final output
+    """
+
+    def __init__(self):
+        """Initialize the flow with nodes and connections"""
+
+        # TODO: Create your nodes
+        node1 = Node1()
+        node2 = Node2()
+        node3 = Node3()
+
+        # TODO: Define flow connections
+
+        # Simple sequence
+        node1 >> node2 >> node3
+
+        # Branching (conditional)
+        # node2 - "error" >> error_handler
+        # node2 - "success" >> node3
+
+        # Looping
+        # node3 - "retry" >> node1
+
+        # Initialize with start node
+        super().__init__(start=node1)
+
+
+# Example with actual implementation
+class SimpleWorkflow(Flow):
+    """Example: Simple 3-step workflow"""
+
+    def __init__(self):
+        # Step 1: Load data
+        load = LoadNode()
+
+        # Step 2: Process
+        process = ProcessNode()
+
+        # Step 3: Save
+        save = SaveNode()
+
+        # Connect
+        load >> process >> save
+
+        super().__init__(start=load)
+
+
+class ConditionalWorkflow(Flow):
+    """Example: Workflow with branching"""
+
+    def __init__(self):
+        # Create nodes
+        validate = ValidateNode()
+        process_valid = ProcessValidNode()
+        process_invalid = ProcessInvalidNode()
+        finalize = FinalizeNode()
+
+        # Branching based on validation
+        validate - "valid" >> process_valid
+        validate - "invalid" >> process_invalid
+
+        # Both paths lead to finalize
+        process_valid >> finalize
+        process_invalid >> finalize
+
+        super().__init__(start=validate)
+
+
+class LoopingWorkflow(Flow):
+    """Example: Workflow with retry loop"""
+
+    def __init__(self):
+        # Create nodes
+        attempt = AttemptNode()
+        verify = VerifyNode()
+        finish = FinishNode()
+
+        # Setup loop
+        attempt >> verify
+
+        # Branching: success or retry
+        verify - "success" >> finish
+        verify - "retry" >> attempt  # Loop back
+
+        # Optional: max attempts check
+        verify - "failed" >> finish
+
+        super().__init__(start=attempt)
+
+
+class NestedWorkflow(Flow):
+    """Example: Flow containing sub-flows"""
+
+    def __init__(self):
+        # Create sub-flows
+        preprocessing_flow = PreprocessFlow()
+        processing_flow = ProcessFlow()
+        postprocessing_flow = PostprocessFlow()
+
+        # Connect sub-flows
+        preprocessing_flow >> processing_flow >> postprocessing_flow
+
+        super().__init__(start=preprocessing_flow)
+
+
+# Example usage
+if __name__ == "__main__":
+    # Create flow
+    flow = SimpleWorkflow()
+
+    # Prepare shared store
+    shared = {
+        "input_data": "Hello, PocketFlow!"
+    }
+
+    # Run flow
+    flow.run(shared)
+
+    # Check results
+    print(f"Final result: {shared.get('final_result')}")

skills/pocketflow/assets/node_template.py

@@ -0,0 +1,124 @@
+"""
+PocketFlow Node Template
+
+Copy this template and customize for your needs
+"""
+
+from pocketflow import Node
+# from utils.call_llm import call_llm  # Uncomment if using LLM
+
+
+class TemplateNode(Node):
+    """
+    Brief description of what this node does
+
+    Shared Store Schema:
+        Input:
+            - key1 (type): description
+            - key2 (type): description
+
+        Output:
+            - result_key (type): description
+
+    Actions:
+        - "default": Normal flow
+        - "error": If something goes wrong
+        - "retry": If needs retry
+    """
+
+    def prep(self, shared):
+        """
+        Prepare data from shared store
+
+        Args:
+            shared (dict): Shared data store
+
+        Returns:
+            Any: Data to pass to exec()
+        """
+        # TODO: Get data from shared store
+        input_data = shared.get("input_key")
+
+        # Optional: Add validation
+        if not input_data:
+            raise ValueError("Missing required input")
+
+        return input_data
+
+    def exec(self, prep_res):
+        """
+        Execute the main logic (can fail and retry)
+
+        Args:
+            prep_res: Data from prep()
+
+        Returns:
+            Any: Result to pass to post()
+        """
+        # TODO: Implement your logic here
+
+        # Example: Call LLM
+        # result = call_llm(f"Process: {prep_res}")
+
+        # Example: Process data
+        result = f"Processed: {prep_res}"
+
+        return result
+
+    def post(self, shared, prep_res, exec_res):
+        """
+        Save results and return action
+
+        Args:
+            shared (dict): Shared data store
+            prep_res: Original data from prep()
+            exec_res: Result from exec()
+
+        Returns:
+            str: Action name for flow control
+        """
+        # TODO: Save results to shared store
+        shared["result_key"] = exec_res
+
+        # Optional: Conditional actions
+        # if some_condition:
+        #     return "special_action"
+
+        return "default"
+
+    def exec_fallback(self, prep_res, exc):
+        """
+        Optional: Handle errors gracefully
+
+        Args:
+            prep_res: Data from prep()
+            exc: The exception that occurred
+
+        Returns:
+            Any: Fallback result (passed to post as exec_res)
+        """
+        # TODO: Implement fallback logic
+        print(f"Error occurred: {exc}")
+
+        # Option 1: Re-raise the exception
+        # raise exc
+
+        # Option 2: Return fallback value
+        return "Fallback result"
+
+
+# Example usage
+if __name__ == "__main__":
+    # Create node with retry settings
+    node = TemplateNode(max_retries=3, wait=5)
+
+    # Create shared store
+    shared = {
+        "input_key": "test input"
+    }
+
+    # Run node
+    action = node.run(shared)
+
+    print(f"Action: {action}")
+    print(f"Result: {shared.get('result_key')}")

skills/pocketflow/assets/template/README.md

@@ -0,0 +1,80 @@
+# PocketFlow Project Template
+
+This template provides a best-practice structure for PocketFlow projects.
+
+Source: https://github.com/The-Pocket/PocketFlow-Template-Python
+
+## Project Structure
+
+```
+template/
+├── main.py              # Entry point
+├── flow.py              # Flow definition
+├── nodes.py             # Node implementations
+├── utils.py             # Utility functions (LLM wrappers, etc.)
+└── requirements.txt     # Python dependencies
+```
+
+## Quick Start
+
+1. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. **Configure your LLM:**
+   Edit `utils.py` and implement `call_llm()` for your provider (OpenAI, Anthropic, or Gemini)
+
+3. **Set API key:**
+   ```bash
+   export OPENAI_API_KEY=sk-...
+   # or
+   export ANTHROPIC_API_KEY=sk-ant-...
+   # or
+   export GEMINI_API_KEY=...
+   ```
+
+4. **Run:**
+   ```bash
+   python main.py
+   ```
+
+## Customization
+
+- **Add nodes:** Create new node classes in `nodes.py`
+- **Modify flow:** Update connections in `flow.py`
+- **Add utilities:** Implement helpers in `utils.py`
+- **Update logic:** Customize `main.py` for your use case
+
+## Best Practices Demonstrated
+
+1. **Separation of Concerns:**
+   - `nodes.py` - Node logic only
+   - `flow.py` - Flow orchestration only
+   - `utils.py` - Reusable utilities
+   - `main.py` - Application entry point
+
+2. **Factory Pattern:**
+   - `create_qa_flow()` makes flow reusable
+   - Easy to test and modify
+
+3. **Clear Data Flow:**
+   - Shared store pattern for data passing
+   - Explicit state management
+
+4. **Configuration:**
+   - Environment variables for API keys
+   - requirements.txt for dependencies
+
+## Next Steps
+
+1. Implement your `call_llm()` function
+2. Add your business logic to nodes
+3. Define your workflow in flow.py
+4. Run and iterate!
+
+## Resources
+
+- **PocketFlow Docs:** https://the-pocket.github.io/PocketFlow/
+- **GitHub:** https://github.com/The-Pocket/PocketFlow
+- **Examples:** See the cookbook/ directory for more patterns

skills/pocketflow/assets/template/flow.py

@@ -0,0 +1,37 @@
+"""
+PocketFlow Template - Flow Definition
+
+Source: https://github.com/The-Pocket/PocketFlow-Template-Python
+
+This module defines the QA flow by connecting nodes.
+"""
+
+from pocketflow import Flow
+from nodes import GetQuestionNode, AnswerNode
+
+
+def create_qa_flow():
+    """
+    Create a simple Question-Answer flow
+
+    Flow structure:
+        GetQuestionNode >> AnswerNode
+
+    Returns:
+        Flow: Configured QA flow
+    """
+    # Create nodes
+    get_question_node = GetQuestionNode()
+    answer_node = AnswerNode()
+
+    # Connect nodes sequentially
+    get_question_node >> answer_node
+
+    # Create flow with start node
+    qa_flow = Flow(start=get_question_node)
+
+    return qa_flow
+
+
+# For direct module execution
+qa_flow = create_qa_flow()

skills/pocketflow/assets/template/main.py

@@ -0,0 +1,35 @@
+"""
+PocketFlow Template - Main Entry Point
+
+Source: https://github.com/The-Pocket/PocketFlow-Template-Python
+
+This template demonstrates best practices for structuring a PocketFlow project.
+"""
+
+from flow import create_qa_flow
+
+
+def main():
+    """Main entry point for the application"""
+
+    # Prepare shared data store
+    shared = {
+        "question": "In one sentence, what's the end of universe?",
+        "answer": None
+    }
+
+    # Create and run the flow
+    qa_flow = create_qa_flow()
+    qa_flow.run(shared)
+
+    # Display results
+    print(f"\n{'='*60}")
+    print("Results:")
+    print(f"{'='*60}")
+    print(f"Question: {shared['question']}")
+    print(f"Answer: {shared['answer']}")
+    print(f"{'='*60}\n")
+
+
+if __name__ == "__main__":
+    main()

skills/pocketflow/assets/template/nodes.py

@@ -0,0 +1,56 @@
+"""
+PocketFlow Template - Node Definitions
+
+Source: https://github.com/The-Pocket/PocketFlow-Template-Python
+
+This module contains the node definitions for the QA flow.
+Each node implements the prep/exec/post pattern.
+"""
+
+from pocketflow import Node
+# from utils import call_llm  # Uncomment when implemented
+
+
+class GetQuestionNode(Node):
+    """Node to get user input"""
+
+    def prep(self, shared):
+        """Prepare: can access shared store but no data needed"""
+        return None
+
+    def exec(self, prep_res):
+        """Execute: get user input"""
+        question = input("\nEnter your question: ")
+        return question
+
+    def post(self, shared, prep_res, exec_res):
+        """Post: store question in shared store"""
+        shared["question"] = exec_res
+        print(f"✓ Question received: {exec_res}")
+        return "default"
+
+
+class AnswerNode(Node):
+    """Node to generate answer using LLM"""
+
+    def prep(self, shared):
+        """Prepare: get question from shared store"""
+        return shared.get("question", "")
+
+    def exec(self, question):
+        """Execute: call LLM to get answer"""
+        if not question:
+            return "No question provided"
+
+        # Call your LLM implementation
+        # answer = call_llm(question)
+
+        # Placeholder
+        answer = f"This is a placeholder answer to: {question}\nImplement call_llm() in utils.py"
+        return answer
+
+    def post(self, shared, prep_res, exec_res):
+        """Post: store answer in shared store"""
+        shared["answer"] = exec_res
+        print(f"✓ Answer generated ({len(exec_res)} chars)")
+        return "default"

skills/pocketflow/assets/template/requirements.txt

@@ -0,0 +1,20 @@
+# PocketFlow Template Requirements
+
+# Core framework
+pocketflow
+
+# LLM Providers (uncomment what you need)
+# openai>=1.0.0
+# anthropic>=0.18.0
+# google-generativeai>=0.3.0
+
+# Optional utilities
+# requests>=2.31.0
+# beautifulsoup4>=4.12.0
+# faiss-cpu>=1.7.4
+# numpy>=1.24.0
+
+# Development tools
+# pytest>=7.4.0
+# black>=23.0.0
+# flake8>=6.0.0

skills/pocketflow/assets/template/utils.py

@@ -0,0 +1,61 @@
+"""
+PocketFlow Template - Utility Functions
+
+Source: https://github.com/The-Pocket/PocketFlow-Template-Python
+
+This module contains utility functions like LLM wrappers.
+"""
+
+import os
+
+
+def call_llm(prompt):
+    """
+    Call your LLM provider
+
+    Args:
+        prompt (str): The prompt to send to the LLM
+
+    Returns:
+        str: The LLM response
+
+    TODO: Implement your LLM provider here
+    """
+
+    # Example: OpenAI
+    """
+    from openai import OpenAI
+    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+    response = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response.choices[0].message.content
+    """
+
+    # Example: Anthropic
+    """
+    from anthropic import Anthropic
+    client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
+    response = client.messages.create(
+        model="claude-sonnet-4-0",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response.content[0].text
+    """
+
+    # Example: Google Gemini
+    """
+    from google import genai
+    client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
+    response = client.models.generate_content(
+        model='gemini-2.0-flash-exp',
+        contents=prompt
+    )
+    return response.text
+    """
+
+    raise NotImplementedError(
+        "Implement your LLM provider in utils.py\n"
+        "See examples above for OpenAI, Anthropic, or Google Gemini"
+    )