Add Skill: research-pipeline

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

skills/multi-agent-sequential-workflow/SKILL.md

@@ -1,84 +0,0 @@
----
-name: multi-agent-sequential-workflow
-version: 1.0.0
-description: Gather and process information from multiple agents to generate a comprehensive
-  travel guide.
-inputs:
-- User query with location
-steps:
-- 'Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel
-  to gather raw facts about the destination.'
-- 'Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into
-  a structured travel guide based on the user''s request and raw information provided
-  by the researcher.'
-- 'Step 3: Writer agent (agent.py) formats the final response, including the structured
-  guide content and prominently features the ''Suggested Web Pages'' section.'
-outputs:
-- Structured travel guide with key sections
-- Final client response
-tags: []
-metadata:
-  source_repo: https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git
-  extracted_at: ''
-  confidence: 0.95
----
-
-# multi-agent-sequential-workflow
-
-Gather and process information from multiple agents to generate a comprehensive travel guide.
-
-## Setup
-
-**Dependencies:**
-
-```text
-pip install python3 fastapi uvicorn strands bedrock-model
-```
-
-**Setup steps:**
-
-1. Install required dependencies using pip
-1. Set up environment variables for API keys and model IDs
-
-## Key Files
-
-- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/agent.py - Contains the multi-agent workflow logic.`
-- `agents/aws_strands/05-agent-strands-multiagent-workflow-sequential/app.py - FastAPI app to handle user queries.`
-
-## Steps
-
-1. Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.
-2. Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.
-3. Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
-
-## Implementation Details
-
-```python
-research_output = researcher_agent(query, stream=False)
-```
-
-```python
-guide_output = travel_guide_agent(planner_prompt, stream=False)
-```
-
-```python
-final_response = writer_agent(writer_prompt, stream=False)
-```
-
-## Inputs
-
-- User query with location
-
-## Outputs
-
-- Structured travel guide with key sections
-- Final client response
-
-## Failure Modes
-
-- Network issues during API calls could lead to incomplete data collection or processing failures
-
-## Source
-
-Extracted from: [https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git](https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git)
-Confidence: 0.95

skills/multi-agent-sequential-workflow/commands.md

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

skills/multi-agent-sequential-workflow/examples.md

@@ -1,10 +0,0 @@
-# Examples: multi-agent-sequential-workflow
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: User query with location
-# Process: Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination. → Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher. → Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section.
-# Outputs: Structured travel guide with key sections, Final client response
-```

skills/multi-agent-sequential-workflow/metadata.json

@@ -1,24 +0,0 @@
-{
-  "name": "multi-agent-sequential-workflow",
-  "version": "1.0.0",
-  "goal": "Gather and process information from multiple agents to generate a comprehensive travel guide.",
-  "inputs": [
-    "User query with location"
-  ],
-  "steps": [
-    "Step 1: Researcher agent (agent.py) uses LangGraph create_react_agent with BedrockModel to gather raw facts about the destination.",
-    "Step 2: Travel Guide Generator agent (agent.py) synthesizes the gathered data into a structured travel guide based on the user's request and raw information provided by the researcher.",
-    "Step 3: Writer agent (agent.py) formats the final response, including the structured guide content and prominently features the 'Suggested Web Pages' section."
-  ],
-  "outputs": [
-    "Structured travel guide with key sections",
-    "Final client response"
-  ],
-  "failure_modes": [
-    "Network issues during API calls could lead to incomplete data collection or processing failures"
-  ],
-  "confidence": 0.95,
-  "explanation": "This workflow is specific but can be adapted for other types of guides or information gathering tasks.",
-  "source_repo": "https://github.com/omerbsezer/Fast-LLM-Agent-MCP.git",
-  "score": 1.0
-}

skills/research-pipeline/SKILL.md

@@ -0,0 +1,90 @@
+---
+name: research-pipeline
+version: 1.0.0
+description: 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` 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()`.'
+- '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: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
+  fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
+- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
+  print(f''Summary written to: {result}'')`'
+outputs:
+- Path of the summary file
+tags: []
+metadata:
+  source_repo: https://github.com/temiroff/Blacknode.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# research-pipeline
+
+Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.
+
+## Setup
+
+**Dependencies:**
+
+```text
+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. Install required dependencies: `pip install -r requirements.txt`
+
+## Key Files
+
+- `examples/research_pipeline.py - Contains the research pipeline workflow`
+
+## 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`
+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: Initialize `g = bn.Graph()`.
+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: 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: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`
+
+## Implementation Details
+
+```python
+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')
+```
+
+```python
+url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
+```
+
+## Inputs
+
+- URL of the Wikipedia page
+
+## Outputs
+
+- Path of 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
+
+## Source
+
+Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
+Confidence: 0.95

skills/research-pipeline/commands.md

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

skills/research-pipeline/examples.md

@@ -0,0 +1,10 @@
+# Examples: research-pipeline
+
+## Usage Example
+
+```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()`.
+# Outputs: Path of the summary file
+```

skills/research-pipeline/metadata.json

@@ -0,0 +1,26 @@
+{
+  "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.",
+  "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 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()`.",
+    "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: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`",
+    "Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`"
+  ],
+  "outputs": [
+    "Path of 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"
+  ],
+  "confidence": 0.95,
+  "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
+}

skills/research-pipeline/tests.md

@@ -1,4 +1,4 @@
-# Tests: multi-agent-sequential-workflow
+# Tests: research-pipeline
 
 ## Test Checklist