Add Skill: research-pipeline

Extracted from: https://github.com/temiroff/Blacknode.git
Score: 1.0

pipeline/publisher.py

@@ -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)

run.py

@@ -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]}")
 

skills/langgraph-workflow-creation/SKILL.md

@@ -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

skills/langgraph-workflow-creation/commands.md

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

skills/langgraph-workflow-creation/examples.md

@@ -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
-```

skills/langgraph-workflow-creation/metadata.json

@@ -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
-}

skills/langgraph-workflow-creation/tests.md

@@ -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

skills/research-pipeline/SKILL.md

@@ -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
 

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` 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
 ```

skills/research-pipeline/metadata.json

@@ -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
 }