robert-mcdermott

name

dimensions-ai

description

Query the Dimensions.ai scholarly research database using its DSL API via Python. TRIGGER when: user asks about publications, grants, clinical trials, patents, researchers, research organizations, funding data, citation metrics, research output, scholarly data, academic papers, principal investigators, GRID IDs, ORCID, or Dimensions. Use this skill to build Python scripts that authenticate, query, paginate, and return structured data from the Dimensions Analytics API.

robert-mcdermott

#!/bin/zsh
set -euo pipefail

echo "Checking Homebrew..."
if ! command -v brew >/dev/null 2>&1; then
  echo "Homebrew not found. Installing it for Apple silicon..."
  /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  eval "$(/opt/homebrew/bin/brew shellenv)"
else
  # Prefer the Apple silicon Homebrew path if present

robert-mcdermott

docker run -it --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
-p 8900:8000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
nvcr.io/nvidia/vllm:25.09-py3 \
vllm serve "Qwen/Qwen3-1.7B"

robert-mcdermott

# Define API key and endpoint
$ApiKey = "sk-12345"
$ApiEndpoint = "http://localhost:11434/v1/chat/completions"

<#
System message.
You can use this to give the AI instructions on what to do, how to act or how to respond to future prompts.
Default value for ChatGPT = "You are a helpful assistant."
#>
$AiSystemMessage = "You are a helpful AI assistant."

robert-mcdermott

#!/bin/bash

base_dir=~/.ollama/models
manifest_dir=$base_dir/manifests/registry.ollama.ai
blob_dir=$base_dir/blobs
publicmodels_dir=~/llm-models

find "$publicmodels_dir" -type l -exec rm {} +

mkdir -p "$publicmodels_dir"

robert-mcdermott

# pip install scikit-learn numpy ollama
import ollama
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

text1 = ollama.embeddings(model='nomic-embed-text', prompt='The sky is blue because of rayleigh scattering')
text2 = ollama.embeddings(model='nomic-embed-text', prompt='The sky is cloudy and grey today')

vec1 = np.array(text1['embedding']).reshape(1, -1)
vec2 = np.array(text2['embedding']).reshape(1, -1)

robert-mcdermott

import argparse
import requests
from langchain.llms import Ollama
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import GPT4AllEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA

def main(args):

robert-mcdermott

# pip install chromadb==0.4.15 # need to pin to this version for current langchain version
from langchain.llms import Ollama
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import GPT4AllEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA

ollama = Ollama(base_url='http://localhost:11434', model='zephyr:latest')

robert-mcdermott

import plotly.express as px
import matplotlib.pyplot as plt
from collections import Counter
import re
import sys
import nltk
from nltk.corpus import stopwords

def plot_word_frequencies_matplot(file_path, top):
    # Load stop words

robert-mcdermott

import os
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse

def is_valid(url, base_url):
    parsed = urlparse(url)
    return bool(parsed.netloc) and parsed.netloc == urlparse(base_url).netloc

def is_binary(url):