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skills/research-pipeline/SKILL.md
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---
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.
description: 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 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}'')`'
- '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'
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. Install required dependencies: `pip install -r requirements.txt`
1. Ensure NVIDIA NIM API key is set in the environment or editor UI
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`
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}')`
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
```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
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```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
# 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 to the summary file
```
skills/research-pipeline/metadata.json
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{
"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 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}')`"
"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"
],
"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
}