Add Skill: research-pipeline

Extracted from: https://github.com/temiroff/Blacknode.git Score: 1.0
2026-08-05 15:45:47 +00:00
21 changed files with 61 additions and 533 deletions
@@ -34,8 +34,8 @@ scout:
    - 'rag agent workflow'
    - 'tool calling workflow'
  filters:
-    stars_min: 10
+    stars_min: 15
-    pushed_after: 2026-02-01
+    pushed_after: 2026-05-01
    language: Python
    archived: false
    size_max_kb: 10000
@@ -52,18 +52,6 @@ def publish_skill(review_result, config):
        subprocess.run(["git", "config", "user.email", "hermes@agent.local"], cwd=repo_dir)
        subprocess.run(["git", "config", "user.name", "Hermes Pipeline"], cwd=repo_dir)
        # Check for duplicates in skills/ directory
        skills_dir = os.path.join(repo_dir, "skills")
        existing_skills = []
        if os.path.isdir(skills_dir):
            existing_skills = [d for d in os.listdir(skills_dir) if os.path.isdir(os.path.join(skills_dir, d))]
        if skill_name in existing_skills:
            return {
                "status": "SKIP",
                "reason": f"Skill '{skill_name}' already exists in skills/ directory",
            }
        # Create skill directory
        skill_dir = os.path.join(repo_dir, "skills", skill_name)
        os.makedirs(skill_dir, exist_ok=True)
@@ -137,8 +137,6 @@ def main():
        if publish_output.get("status") == "PUBLISHED":
            print(f"    ✓ Published! PR: {publish_output.get('pr_url', '')}")
            results["published"] += 1
        elif publish_output.get("status") == "SKIP":
            print(f"    ⏸ Skipped: {publish_output.get('reason', '')}")
        else:
            print(f"    ! {publish_output.get('status', '?')}: {publish_output.get('message', publish_output.get('error', ''))[:100]}")
@@ -1,84 +0,0 @@
 ---
 name: agent-supervisor
 version: 1.0.0
 description: Demonstrate a supervisor-worker architecture for intelligent task delegation
  and real-time decision-making.
 inputs:
 - name: OPENAI_API_KEY
  description: OpenAI API key for language models.
 - name: TAVILY_API_KEY
  description: Tavily API key for search functionality.
 steps:
 - step: 1
  action: Load environment variables.
  details: Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.
 - step: 2
  action: Configure LangChain tools.
  details: Initialize TavilySearchResults and PythonREPLTool.
 - step: 3
  action: Define agent nodes.
  details: Create functions for the Researcher and Coder agents that process state
    through their respective tasks.
 - step: 4
  action: Set up supervisor agent.
  details: Create a supervisor agent function that decides which worker should act
    next based on user input.
 - step: 5
  action: Build state graph.
  details: Construct the state graph with nodes for each agent and edges connecting
    them to the supervisor node.
 - step: 6
  action: Add conditional edges.
  details: Define conditions for transitioning between agents based on their responses.
 - step: 7
  action: Compile graph.
  details: Compile the state graph into a runnable workflow.
 - step: 8
  action: Run example queries.
  details: Stream through the workflow with example inputs to demonstrate its functionality.
 outputs:
 - name: 'Example 1: Code Hello World'
  description: A demonstration of coding a simple hello world program.
 - name: 'Example 2: Research Report'
  description: A demonstration of researching and writing a brief report on pikas.
 tags: []
 metadata:
  source_repo: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
  extracted_at: ''
  confidence: 0.9
 ---
 # agent-supervisor
 Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.
 ## Steps
 1. {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'}
 2. {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'}
 3. {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
 4. {'step': 4, 'action': 'Set up supervisor agent.', 'details': 'Create a supervisor agent function that decides which worker should act next based on user input.'}
 5. {'step': 5, 'action': 'Build state graph.', 'details': 'Construct the state graph with nodes for each agent and edges connecting them to the supervisor node.'}
 6. {'step': 6, 'action': 'Add conditional edges.', 'details': 'Define conditions for transitioning between agents based on their responses.'}
 7. {'step': 7, 'action': 'Compile graph.', 'details': 'Compile the state graph into a runnable workflow.'}
 8. {'step': 8, 'action': 'Run example queries.', 'details': 'Stream through the workflow with example inputs to demonstrate its functionality.'}
 ## Inputs
 - {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}
 - {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
 ## Outputs
 - {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}
 - {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
 ## Failure Modes
 - {'mode': 'Invalid API keys', 'description': 'The workflow may fail if the provided API keys are invalid or expired.'}
 - {'mode': 'Insufficient permissions', 'description': 'The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality.'}
 ## Source
 Extracted from: [https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git](https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git)
 Confidence: 0.9
@@ -1,6 +0,0 @@
 # Commands: agent-supervisor
 ## Available Commands
 - `/skill agent-supervisor` — Load this skill
 - `/run agent-supervisor` — Execute workflow
@@ -1,10 +0,0 @@
 # Examples: agent-supervisor
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: {'name': 'OPENAI_API_KEY', 'description': 'OpenAI API key for language models.'}, {'name': 'TAVILY_API_KEY', 'description': 'Tavily API key for search functionality.'}
 # Process: {'step': 1, 'action': 'Load environment variables.', 'details': 'Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables.'} → {'step': 2, 'action': 'Configure LangChain tools.', 'details': 'Initialize TavilySearchResults and PythonREPLTool.'} → {'step': 3, 'action': 'Define agent nodes.', 'details': 'Create functions for the Researcher and Coder agents that process state through their respective tasks.'}
 # Outputs: {'name': 'Example 1: Code Hello World', 'description': 'A demonstration of coding a simple hello world program.'}, {'name': 'Example 2: Research Report', 'description': 'A demonstration of researching and writing a brief report on pikas.'}
 ```
@@ -1,81 +0,0 @@
 {
  "name": "agent-supervisor",
  "version": "1.0.0",
  "goal": "Demonstrate a supervisor-worker architecture for intelligent task delegation and real-time decision-making.",
  "inputs": [
    {
      "name": "OPENAI_API_KEY",
      "description": "OpenAI API key for language models."
    },
    {
      "name": "TAVILY_API_KEY",
      "description": "Tavily API key for search functionality."
    }
  ],
  "steps": [
    {
      "step": 1,
      "action": "Load environment variables.",
      "details": "Set the OPENAI_API_KEY and TAVILY_API_KEY environment variables."
    },
    {
      "step": 2,
      "action": "Configure LangChain tools.",
      "details": "Initialize TavilySearchResults and PythonREPLTool."
    },
    {
      "step": 3,
      "action": "Define agent nodes.",
      "details": "Create functions for the Researcher and Coder agents that process state through their respective tasks."
    },
    {
      "step": 4,
      "action": "Set up supervisor agent.",
      "details": "Create a supervisor agent function that decides which worker should act next based on user input."
    },
    {
      "step": 5,
      "action": "Build state graph.",
      "details": "Construct the state graph with nodes for each agent and edges connecting them to the supervisor node."
    },
    {
      "step": 6,
      "action": "Add conditional edges.",
      "details": "Define conditions for transitioning between agents based on their responses."
    },
    {
      "step": 7,
      "action": "Compile graph.",
      "details": "Compile the state graph into a runnable workflow."
    },
    {
      "step": 8,
      "action": "Run example queries.",
      "details": "Stream through the workflow with example inputs to demonstrate its functionality."
    }
  ],
  "outputs": [
    {
      "name": "Example 1: Code Hello World",
      "description": "A demonstration of coding a simple hello world program."
    },
    {
      "name": "Example 2: Research Report",
      "description": "A demonstration of researching and writing a brief report on pikas."
    }
  ],
  "failure_modes": [
    {
      "mode": "Invalid API keys",
      "description": "The workflow may fail if the provided API keys are invalid or expired."
    },
    {
      "mode": "Insufficient permissions",
      "description": "The workflow may fail if the user does not have sufficient permissions to use the Tavily search functionality."
    }
  ],
  "confidence": 0.9,
  "explanation": "This workflow demonstrates a hierarchical multi-agent system where a supervisor agent makes routing decisions based on user input, delegating tasks to specialized worker agents (Researcher and Coder). It is designed to be reusable for similar task delegation scenarios.",
  "source_repo": "https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git",
  "score": 1.0
 }
@@ -1,9 +0,0 @@
 # Tests: agent-supervisor
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -1,96 +0,0 @@
 ---
 name: blacknode-graph-workflow
 version: 1.0.0
 description: Build and execute node-based AI workflows with LLM agents and processing
  nodes
 inputs:
 - Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
 - Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite,
  etc.)
 - Data sources (URLs, text content, or other inputs for the workflow)
 steps:
 - Initialize a blacknode.Graph instance to create the workflow structure
 - Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent,
  FileWrite)
 - Define edges connecting nodes to establish data flow between them
 - Execute the graph using cook() to run the workflow and generate outputs
 outputs:
 - Processed results from the final node (e.g., printed text, written files, or generated
  data)
 - Graph execution status and any errors encountered during execution
 tags: []
 metadata:
  source_repo: https://github.com/temiroff/Blacknode.git
  extracted_at: ''
  confidence: 0.95
 ---
 # blacknode-graph-workflow
 Build and execute node-based AI workflows with LLM agents and processing nodes
 ## Setup
 **Dependencies:**
 ```text
 pip install blacknode (core package) anthropic>=0.25 openai>=1.0 petgraph (for graph operations)
 ```
 **Setup steps:**
 1. Install blacknode package: pip install blacknode
 1. Configure model API keys (NIM_API_KEY, OPENAI_API_KEY, etc.) in .env or editor
 1. Create a Graph instance and add nodes with inputs/outputs
 1. Define node connections in g._edges list
 1. Execute with g.cook() to run the workflow and capture results
 ## Key Files
 - `blacknode/blacknode.py (Graph class implementation)`
 - `examples/hello_agent.py (simple LLM agent workflow)`
 - `examples/converted_nvidia_nim.py (NIM model workflow)`
 ## Steps
 1. Initialize a blacknode.Graph instance to create the workflow structure
 2. Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)
 3. Define edges connecting nodes to establish data flow between them
 4. Execute the graph using cook() to run the workflow and generate outputs
 ## Implementation Details
 ```python
 g = bn.Graph()
 ```
 ```python
 g._edges = [{'from': 'model', 'from_port': 'value', 'to': 'agent', 'to_port': 'model'}]
 ```
 ```python
 result = g.cook(output_node, 'value')
 ```
 ## Inputs
 - Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
 - Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)
 - Data sources (URLs, text content, or other inputs for the workflow)
 ## Outputs
 - Processed results from the final node (e.g., printed text, written files, or generated data)
 - Graph execution status and any errors encountered during execution
 ## Failure Modes
 - Missing or invalid model API key causing graph initialization failure
 - Incorrect node connections or missing edge definitions leading to runtime errors
 - Model not found or unavailable in the specified environment causing execution failure
 - Graph edges not properly defined or mismatched causing cook() to fail
 ## Source
 Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
 Confidence: 0.95
@@ -1,6 +0,0 @@
 # Commands: blacknode-graph-workflow
 ## Available Commands
 - `/skill blacknode-graph-workflow` — Load this skill
 - `/run blacknode-graph-workflow` — Execute workflow
@@ -1,10 +0,0 @@
 # Examples: blacknode-graph-workflow
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic), Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.), Data sources (URLs, text content, or other inputs for the workflow)
 # Process: Initialize a blacknode.Graph instance to create the workflow structure → Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite) → Define edges connecting nodes to establish data flow between them
 # Outputs: Processed results from the final node (e.g., printed text, written files, or generated data), Graph execution status and any errors encountered during execution
 ```
@@ -1,30 +0,0 @@
 {
  "name": "blacknode-graph-workflow",
  "version": "1.0.0",
  "goal": "Build and execute node-based AI workflows with LLM agents and processing nodes",
  "inputs": [
    "Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)",
    "Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)",
    "Data sources (URLs, text content, or other inputs for the workflow)"
  ],
  "steps": [
    "Initialize a blacknode.Graph instance to create the workflow structure",
    "Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)",
    "Define edges connecting nodes to establish data flow between them",
    "Execute the graph using cook() to run the workflow and generate outputs"
  ],
  "outputs": [
    "Processed results from the final node (e.g., printed text, written files, or generated data)",
    "Graph execution status and any errors encountered during execution"
  ],
  "failure_modes": [
    "Missing or invalid model API key causing graph initialization failure",
    "Incorrect node connections or missing edge definitions leading to runtime errors",
    "Model not found or unavailable in the specified environment causing execution failure",
    "Graph edges not properly defined or mismatched causing cook() to fail"
  ],
  "confidence": 0.95,
  "explanation": "Blacknode provides a standardized Graph-based workflow pattern where users create node graphs using the blacknode.Graph class. This pattern is reusable across projects as it follows a consistent structure: initialize a graph, add nodes with defined inputs/outputs, connect them with edges, and execute with cook(). The examples demonstrate this pattern with LLM agents and text processing pipelines, making it adaptable to various robotics and AI workflows.",
  "source_repo": "https://github.com/temiroff/Blacknode.git",
  "score": 1.0
 }
@@ -1,9 +0,0 @@
 # Tests: blacknode-graph-workflow
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -1,83 +0,0 @@
 ---
 name: langgraph-workflow-creation
 version: 1.0.0
 description: Create a LangGraph workflow to gather facts using SerperDevTool and process
  them with an AI agent.
 inputs:
 - API Key for SerperDevTool
 - Search Query
 steps:
 - 'Step 1: Import necessary modules from langgraph and langchain libraries'
 - 'Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function
  with SerperDevTool as the tool node'
 - 'Step 3: Define the search query and pass it to the agent for fact gathering'
 - 'Step 4: Process the gathered facts within the AI agent'
 outputs:
 - Processed Facts
 tags: []
 metadata:
  source_repo: https://github.com/jkmaina/LangGraphProjects.git
  extracted_at: ''
  confidence: 0.95
 ---
 # langgraph-workflow-creation
 Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.
 ## Setup
 **Dependencies:**
 ```text
 pip install langchain serperdev
 ```
 **Setup steps:**
 1. Install required libraries: pip install langchain serperdev
 1. Add API key to .env file: OPENAPI_API_KEY=your_api_key
 ## Key Files
 - `agent.py - Contains the LangGraph agent creation logic`
 - `tool_node.py - Defines the SerperDevTool node`
 ## Steps
 1. Step 1: Import necessary modules from langgraph and langchain libraries
 2. Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node
 3. Step 3: Define the search query and pass it to the agent for fact gathering
 4. Step 4: Process the gathered facts within the AI agent
 ## Implementation Details
 ```python
 import langgraph
 from serperdev import SerperDevTool
 def create_agent(api_key, query):
    tool = SerperDevTool(api_key)
    agent = langgraph.create_react_agent(tool=tool)
    facts = agent.run(query)
    return process_facts(facts)
 ```
 ## Inputs
 - API Key for SerperDevTool
 - Search Query
 ## Outputs
 - Processed Facts
 ## Failure Modes
 - API Key not provided
 - Invalid Search Query
 ## Source
 Extracted from: [https://github.com/jkmaina/LangGraphProjects.git](https://github.com/jkmaina/LangGraphProjects.git)
 Confidence: 0.95
@@ -1,6 +0,0 @@
 # Commands: langgraph-workflow-creation
 ## Available Commands
 - `/skill langgraph-workflow-creation` — Load this skill
 - `/run langgraph-workflow-creation` — Execute workflow
@@ -1,10 +0,0 @@
 # Examples: langgraph-workflow-creation
 ## Usage Example
 ```python
 # How to use this skill
 # Inputs: API Key for SerperDevTool, Search Query
 # Process: Step 1: Import necessary modules from langgraph and langchain libraries → Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node → Step 3: Define the search query and pass it to the agent for fact gathering
 # Outputs: Processed Facts
 ```
@@ -1,26 +0,0 @@
 {
  "name": "langgraph-workflow-creation",
  "version": "1.0.0",
  "goal": "Create a LangGraph workflow to gather facts using SerperDevTool and process them with an AI agent.",
  "inputs": [
    "API Key for SerperDevTool",
    "Search Query"
  ],
  "steps": [
    "Step 1: Import necessary modules from langgraph and langchain libraries",
    "Step 2: Create a LangGraph agent using `langgraph.create_react_agent` function with SerperDevTool as the tool node",
    "Step 3: Define the search query and pass it to the agent for fact gathering",
    "Step 4: Process the gathered facts within the AI agent"
  ],
  "outputs": [
    "Processed Facts"
  ],
  "failure_modes": [
    "API Key not provided",
    "Invalid Search Query"
  ],
  "confidence": 0.95,
  "explanation": "This workflow is specific to fact gathering and can be adapted for different search queries or tools.",
  "source_repo": "https://github.com/jkmaina/LangGraphProjects.git",
  "score": 1.0
 }
@@ -1,9 +0,0 @@
 # Tests: langgraph-workflow-creation
 ## Test Checklist
 - [ ] Workflow has at least 3 steps
 - [ ] All inputs are defined
 - [ ] All outputs are defined
 - [ ] Failure modes are documented
 - [ ] Skill can be loaded without errors
@@ -1,26 +1,27 @@
 ---
 name: research-pipeline
 version: 1.0.0
-description: Fetch a Wikipedia page, summarise its content using an AI agent, and
+description: Fetch a Wikipedia page, summarise it using an AI agent, and write the
-  write the summary to a file.
+  summary to a file.
 inputs:
 - URL of the Wikipedia page
 steps:
- 'Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap
+- 'Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key)
-  import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
+  and blacknode'
- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
+- 'Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`'
-  API key is set.'
+- 'Step 3: Create a Graph instance `g`'
- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
+- 'Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)'
- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
+- 'Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect
-  value=''URL of the Wikipedia page''); fetcher = g.node(''HTTPGet''); summarise =
+  it to the Literal node'
-  g.node(''LLMAgent'', system=''You are a technical writer. Summarise the text in
+- 'Step 6: Add an LLMAgent node with system prompt ''You are a technical writer. Summarise
-  3 bullet points.'', model=NIM_MODEL); writer = g.node(''FileWrite'', path=''summary.txt'')`'
+  the text in 3 bullet points.'' and model NIM_MODEL (`summarise`), connecting its
- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
+  input to the output of `fetcher`'
-  fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
+- 'Step 7: Add a FileWrite node to write the summary to a file named ''summary.txt''
- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
+  (`writer`), connecting its input to the output of `summarise`'
-  print(f''Summary written to: {result}'')`'
+- 'Step 8: Cook the graph starting from the writer node and print the path where the
  summary is written'
 outputs:
- Path of the summary file
+- Path to the summary file
 tags: []
 metadata:
  source_repo: https://github.com/temiroff/Blacknode.git
@@ -30,7 +31,7 @@ metadata:
 # research-pipeline
-Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.
+Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.
 ## Setup
@@ -42,8 +43,8 @@ pip install anthropic>=0.25 docker>=7.1 openai>=1.0
 **Setup steps:**
-1. Ensure NVIDIA NIM API key is set in the environment or editor
+1. Ensure NVIDIA NIM API key is set in the environment or editor UI
-1. Install required dependencies: `pip install -r requirements.txt`
+1. Install required dependencies using `pip install -r requirements.txt`
 ## Key Files
@@ -51,25 +52,39 @@ pip install anthropic>=0.25 docker>=7.1 openai>=1.0
 ## Steps
-1. Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`
+1. Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode
-2. Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.
+2. Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`
-3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
+3. Step 3: Create a Graph instance `g`
-4. Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`
+4. Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)
-5. Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`
+5. Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node
-6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
+6. Step 6: Add an LLMAgent node with system prompt 'You are a technical writer. Summarise the text in 3 bullet points.' and model NIM_MODEL (`summarise`), connecting its input to the output of `fetcher`
 7. Step 7: Add a FileWrite node to write the summary to a file named 'summary.txt' (`writer`), connecting its input to the output of `summarise`
 8. Step 8: Cook the graph starting from the writer node and print the path where the summary is written
 ## Implementation Details
 ```python
-from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
+from _bootstrap import NIM_MODEL, require_nim_api_key
 import blacknode as bn
 ```
 ```python
-url = g.node('Literal', value='https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles=Houdini_(software)&format=json&formatversion=2&origin=*'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')
+g = bn.Graph()
 url = g.node('Literal', value='https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles=Houdini_(software)&format=json&formatversion=2&origin=*')
 fetcher = g.node('HTTPGet')
 summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL)
 writer = g.node('FileWrite', path='summary.txt')
 ```
 ```python
-url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
+url.out('value') >> fetcher.inp('url')
 fetcher.out('text') >> summarise.inp('prompt')
 summarise.out('text') >> writer.inp('text')
 ```
 ```python
 result = g.cook(writer, 'path')
 print(f'Summary written to: {result}')
 ```
 ## Inputs
@@ -78,11 +93,11 @@ url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('pr
 ## Outputs
- Path of the summary file
+- Path to the summary file
 ## Failure Modes
- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty
+- If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly
 ## Source
@@ -5,6 +5,6 @@
 ```python
 # How to use this skill
 # Inputs: URL of the Wikipedia page
-# Process: Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn` → Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set. → Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
+# Process: Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode → Step 2: Require NVIDIA NIM API key using `require_nim_api_key()` → Step 3: Create a Graph instance `g`
-# Outputs: Path of the summary file
+# Outputs: Path to the summary file
 ```
@@ -1,26 +1,28 @@
 {
  "name": "research-pipeline",
  "version": "1.0.0",
-  "goal": "Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.",
+  "goal": "Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.",
  "inputs": [
    "URL of the Wikipedia page"
  ],
  "steps": [
-    "Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`",
+    "Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode",
-    "Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.",
+    "Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`",
-    "Step 3: Create a graph instance: Initialize `g = bn.Graph()`.",
+    "Step 3: Create a Graph instance `g`",
-    "Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`",
+    "Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)",
-    "Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`",
+    "Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node",
-    "Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
+    "Step 6: Add an LLMAgent node with system prompt 'You are a technical writer. Summarise the text in 3 bullet points.' and model NIM_MODEL (`summarise`), connecting its input to the output of `fetcher`",
    "Step 7: Add a FileWrite node to write the summary to a file named 'summary.txt' (`writer`), connecting its input to the output of `summarise`",
    "Step 8: Cook the graph starting from the writer node and print the path where the summary is written"
  ],
  "outputs": [
-    "Path of the summary file"
+    "Path to the summary file"
  ],
  "failure_modes": [
-    "If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty"
+    "If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly"
  ],
  "confidence": 0.95,
-  "explanation": "This workflow can be adapted to fetch and summarise any Wikipedia page or similar content source.",
+  "explanation": "This workflow can be adapted to fetch and summarise any text from a URL using an AI agent and save the summary to a file.",
  "source_repo": "https://github.com/temiroff/Blacknode.git",
  "score": 1.0
 }