agent-skills/skills/research-pipeline/SKILL.md

name, version, description, inputs, steps, outputs, tags, metadata

name	version	description	inputs	steps	outputs	tags	metadata
research-pipeline	1.0.0	Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.	URL of the Wikipedia page	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` Step 4: Add a Literal node for the URL of the Wikipedia page (`url`) Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node 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	Path to the summary file		source_repo extracted_at confidence https://github.com/temiroff/Blacknode.git 0.95

research-pipeline

Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.

Setup

Dependencies:

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
2. Install required dependencies using 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 (NIM_MODEL, require_nim_api_key) and blacknode
2. Step 2: Require NVIDIA NIM API key using require_nim_api_key()
3. Step 3: Create a Graph instance g
4. Step 4: Add a Literal node for the URL of the Wikipedia page (url)
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: 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

from _bootstrap import NIM_MODEL, require_nim_api_key
import blacknode as bn

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.out('value') >> fetcher.inp('url')
fetcher.out('text') >> summarise.inp('prompt')
summarise.out('text') >> writer.inp('text')

result = g.cook(writer, 'path')
print(f'Summary written to: {result}')

Inputs

• URL of the Wikipedia page

Outputs

• Path to the summary file

Failure Modes

• If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly

Source

Extracted from: https://github.com/temiroff/Blacknode.git Confidence: 0.95