Add Skill: agent-supervisor

Extracted from: https://github.com/extrawest/multi_agent_workflow_demo_in_langgraph.git
Score: 1.0

pipeline/publisher.py

@@ -52,6 +52,18 @@ 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)

run.py

@@ -137,6 +137,8 @@ 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]}")
 

skills/agent-supervisor/SKILL.md

@@ -0,0 +1,84 @@
+---
+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

skills/agent-supervisor/commands.md

@@ -0,0 +1,6 @@
+# Commands: agent-supervisor
+
+## Available Commands
+
+- `/skill agent-supervisor` — Load this skill
+- `/run agent-supervisor` — Execute workflow

skills/agent-supervisor/examples.md

@@ -0,0 +1,10 @@
+# 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.'}
+```

skills/agent-supervisor/metadata.json

@@ -0,0 +1,81 @@
+{
+  "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
+}

skills/agent-supervisor/tests.md

@@ -0,0 +1,9 @@
+# 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

skills/research-pipeline/SKILL.md

@@ -1,27 +1,26 @@
 ---
 name: research-pipeline
 version: 1.0.0
-description: Fetch a Wikipedia page, summarise it using an AI agent, and write the
+description: Fetch a Wikipedia page, summarise its content using an AI agent, and
-  summary to a file.
+  write the summary to a file.
 inputs:
 - URL of the Wikipedia page
 steps:
-- 'Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key)
+- 'Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap
-  and blacknode'
+  import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
-- 'Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`'
+- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
-- 'Step 3: Create a Graph instance `g`'
+  API key is set.'
-- 'Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)'
+- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
-- 'Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect
+- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
-  it to the Literal node'
+  value=''URL of the Wikipedia page''); fetcher = g.node(''HTTPGet''); summarise =
-- 'Step 6: Add an LLMAgent node with system prompt ''You are a technical writer. Summarise
+  g.node(''LLMAgent'', system=''You are a technical writer. Summarise the text in
-  the text in 3 bullet points.'' and model NIM_MODEL (`summarise`), connecting its
+  3 bullet points.'', model=NIM_MODEL); writer = g.node(''FileWrite'', path=''summary.txt'')`'
-  input to the output of `fetcher`'
+- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
-- 'Step 7: Add a FileWrite node to write the summary to a file named ''summary.txt''
+  fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
-  (`writer`), connecting its input to the output of `summarise`'
+- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
-- 'Step 8: Cook the graph starting from the writer node and print the path where the
+  print(f''Summary written to: {result}'')`'
-  summary is written'
 outputs:
-- Path to the summary file
+- Path of the summary file
 tags: []
 metadata:
   source_repo: https://github.com/temiroff/Blacknode.git
@@ -31,7 +30,7 @@ metadata:
 
 # research-pipeline
 
-Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.
+Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.
 
 ## Setup
 
@@ -43,8 +42,8 @@ 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 UI
+1. Ensure NVIDIA NIM API key is set in the environment or editor
-1. Install required dependencies using `pip install -r requirements.txt`
+1. Install required dependencies: `pip install -r requirements.txt`
 
 ## Key Files
 
@@ -52,39 +51,25 @@ pip install anthropic>=0.25 docker>=7.1 openai>=1.0
 
 ## Steps
 
-1. Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode
+1. Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`
-2. Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`
+2. Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.
-3. Step 3: Create a Graph instance `g`
+3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
-4. Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)
+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')`
-5. Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node
+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')`
-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`
+6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
-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
+from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
-import blacknode as bn
 ```
 
 ```python
-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')
-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')
+url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
-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
@@ -93,11 +78,11 @@ print(f'Summary written to: {result}')
 
 ## Outputs
 
-- Path to the summary file
+- Path of the summary file
 
 ## Failure Modes
 
-- If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly
+- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty
 
 ## Source
 

skills/research-pipeline/examples.md

@@ -5,6 +5,6 @@
 ```python
 # How to use this skill
 # Inputs: URL of the Wikipedia page
-# 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`
+# 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()`.
-# Outputs: Path to the summary file
+# Outputs: Path of the summary file
 ```

skills/research-pipeline/metadata.json

@@ -1,28 +1,26 @@
 {
   "name": "research-pipeline",
   "version": "1.0.0",
-  "goal": "Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.",
+  "goal": "Fetch a Wikipedia page, summarise its content 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 (NIM_MODEL, require_nim_api_key) and blacknode",
+    "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 using `require_nim_api_key()`",
+    "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 `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', 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 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node",
+    "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 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 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
-    "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 to the summary file"
+    "Path of the summary file"
   ],
   "failure_modes": [
-    "If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly"
+    "If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty"
   ],
   "confidence": 0.95,
-  "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.",
+  "explanation": "This workflow can be adapted to fetch and summarise any Wikipedia page or similar content source.",
   "source_repo": "https://github.com/temiroff/Blacknode.git",
   "score": 1.0
 }