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Literature Search

Community
yorkeccak
literature-search

Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv. Natural language queries powered by Valyu semantic search.

Overview

Publisheryorkeccak
Repositoryscientific-skills
Skill nameliterature-search
Stars
64
Forks
7
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yorkeccak on GitHub. Read the source before you install it.

Installation

Install the Literature Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yorkeccak/scientific-skills/tree/main/skills/literature-search
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yorkeccak/scientific-skills.git /tmp/scientific-skills
mkdir -p .claude/skills
cp -r /tmp/scientific-skills/skills/literature-search .claude/skills/literature-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Literature Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Literature Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Literature Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Literature Search

Search across all major scientific literature databases (PubMed, arXiv, bioRxiv, medRxiv) simultaneously using natural language queries powered by Valyu's semantic search API.

Why This Skill is Powerful

  • No API Parameter Parsing: Just pass natural language queries directly - no need to construct complex search parameters
  • Semantic Search: Understands the meaning of your query, not just keyword matching
  • Full-Text Access: Returns complete article content, not just abstracts
  • Image Links: Includes figures and images from papers
  • Comprehensive Coverage: Search across PubMed, arXiv, bioRxiv, and medRxiv simultaneously
  • Unified Results: Get results from all sources in a single query

Requirements

  1. Node.js 18+ (uses built-in fetch)
  2. Valyu API key from https://platform.valyu.ai ($10 free credits)

CRITICAL: Script Path Resolution

The scripts/search commands in this documentation are relative to this skill's installation directory.

Before running any command, locate the script using:

bash
LITERATURE_SCRIPT=$(find ~/.claude/plugins/cache -name "search" -path "*/literature-search/*/scripts/*" -type f 2>/dev/null | head -1)

Then use the full path for all commands:

bash
$LITERATURE_SCRIPT "CRISPR gene editing advances" 15

API Key Setup Flow

When you run a search and receive "setup_required": true, follow this flow:

  1. Ask the user for their API key: "To search scientific literature, I need your Valyu API key. Get one free ($10 credits) at https://platform.valyu.ai"

  2. Once the user provides the key, run:

    bash
    scripts/search setup <api-key>
  3. Retry the original search.

When to Use This Skill

  • Comprehensive literature reviews across all domains
  • Finding all relevant research on a topic
  • Cross-domain scientific discovery
  • Combining biomedical, physics, and preprint literature
  • Emerging research across disciplines

Output Format

json
{
  "success": true,
  "type": "literature_search",
  "query": "CRISPR gene editing advances",
  "result_count": 15,
  "results": [
    {
      "title": "Article Title",
      "url": "https://...",
      "content": "Full article text with figures...",
      "source": "pubmed|arxiv|biorxiv|medrxiv",
      "relevance_score": 0.95,
      "images": ["https://example.com/figure1.jpg"]
    }
  ],
  "cost": 0.025
}

Processing Results

With jq

bash
# Get article titles
scripts/search "query" 20 | jq -r '.results[].title'

# Get URLs
scripts/search "query" 20 | jq -r '.results[].url'

# Extract full content
scripts/search "query" 20 | jq -r '.results[].content'

# Filter by source
scripts/search "query" 20 | jq -r '.results[] | select(.source == "arxiv") | .title'

Common Use Cases

Comprehensive Literature Review

bash
# Search across all sources for thorough review
scripts/search "mechanisms of cellular senescence" 100

Cross-Disciplinary Research

bash
# Find papers spanning multiple fields
scripts/search "quantum computing applications in drug discovery" 50

Recent Developments

bash
# Get latest preprints and publications
scripts/search "foundation models for protein folding" 30

Medical Research

bash
# Search biomedical literature comprehensively
scripts/search "immunotherapy checkpoint inhibitors resistance" 40

Error Handling

All commands return JSON with success field:

json
{
  "success": false,
  "error": "Error message"
}

Exit codes:

  • 0 - Success
  • 1 - Error (check JSON for details)

API Endpoint

  • Base URL: https://api.valyu.ai/v1
  • Endpoint: /search
  • Authentication: X-API-Key header

Architecture

scripts/
├── search          # Bash wrapper
└── search.mjs      # Node.js CLI

Direct API calls using Node.js built-in fetch(), zero external dependencies.

Adding to Your Project

If you're building an AI project and want to integrate Literature Search directly into your application, use the Valyu SDK:

Python Integration

python
from valyu import Valyu

client = Valyu(api_key="your-api-key")

response = client.search(
    query="your search query here",
    included_sources=["valyu/valyu-pubmed", "valyu/valyu-arxiv", "valyu/valyu-biorxiv", "valyu/valyu-medrxiv"],
    max_results=20
)

for result in response["results"]:
    print(f"Title: {result['title']}")
    print(f"URL: {result['url']}")
    print(f"Content: {result['content'][:500]}...")

TypeScript Integration

typescript
import { Valyu } from "valyu-js";

const client = new Valyu("your-api-key");

const response = await client.search({
  query: "your search query here",
  includedSources: ["valyu/valyu-pubmed", "valyu/valyu-arxiv", "valyu/valyu-biorxiv", "valyu/valyu-medrxiv"],
  maxResults: 20
});

response.results.forEach((result) => {
  console.log(`Title: ${result.title}`);
  console.log(`URL: ${result.url}`);
  console.log(`Content: ${result.content.substring(0, 500)}...`);
});

See the Valyu docs for full integration examples and SDK reference.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Literature Search AI skill do?

Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv. Natural language queries powered by Valyu semantic search.

Why use Literature Search on TypingMind?

Because you install it once and use it with any model. Literature Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Literature Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yorkeccak/scientific-skills/tree/main/skills/literature-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Literature Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Literature Search?

As many as you like. As long as a model supports skills, you can use Literature Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Literature Search AI skill free?

Yes. It is published on GitHub by yorkeccak under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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