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 its content using an AI agent, and write the summary to a file.	URL of the Wikipedia page	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}')`	Path of the summary file		source_repo extracted_at confidence https://github.com/temiroff/Blacknode.git 0.95

research-pipeline

Fetch a Wikipedia page, summarise its content 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
2. 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

from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn

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')

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 Confidence: 0.95