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Research Assistant

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coleam00
research_assistant

Search academic papers, find citations, and explore research using Semantic Scholar. Use when user asks about scientific papers, research, citations, or academic topics.

Overview

Publishercoleam00
Repositorycustom-agent-with-skills
Skill nameresearch_assistant
Stars
107
Forks
42
Bundled files
2
Links
Skill sourceRepository
  • 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 coleam00 on GitHub. Read the source before you install it.

Installation

Install the Research Assistant 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/coleam00/custom-agent-with-skills/tree/main/skills/research_assistant
  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/coleam00/custom-agent-with-skills.git /tmp/custom-agent-with-skills
mkdir -p .claude/skills
cp -r /tmp/custom-agent-with-skills/skills/research_assistant .claude/skills/research_assistant
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Assistant 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 Research Assistant 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 Research Assistant 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.

Research Assistant Skill

Search and explore academic research using the Semantic Scholar API. Access over 214 million papers, 2.49 billion citations, and 79 million authors across all fields of science.

When to Use

  • User asks about scientific or academic papers
  • User wants to find research on a topic
  • User asks for citations or references
  • User wants to understand the state of research in a field
  • User asks "what does the research say about..."
  • User wants to find papers by a specific author
  • User needs to find papers related to another paper

Available Operations

  1. Paper Search: Find papers by keywords, title, or abstract
  2. Author Lookup: Find papers by a specific researcher
  3. Citation Exploration: Find papers that cite or are cited by a paper
  4. Paper Recommendations: Get papers similar to a given paper
  5. Paper Details: Get full metadata including abstract, authors, venue, year

Instructions

When a user asks about research or academic papers:

Step 1: Understand the Query

Identify what the user wants:

  • Topic search: "papers about transformer architectures"
  • Author search: "papers by Geoffrey Hinton"
  • Citation search: "what papers cite this one"
  • Recommendations: "papers similar to Attention Is All You Need"

Step 2: Load API Reference

ALWAYS read references/api_reference.md before making API calls to ensure you use the correct endpoints and parameters.

Step 3: Construct the Search

IMPORTANT: Make ONE well-constructed search query. Do not make multiple API calls with variations of the same query. The API has rate limits, and multiple calls waste quota.

For topic searches, use the paper search endpoint:

https://api.semanticscholar.org/graph/v1/paper/search?query=YOUR_QUERY

Tips for effective single queries:

  • Use specific, focused keywords (e.g., "transformer attention mechanism" not just "transformer")
  • Include relevant filters like year, minCitationCount, or fieldsOfStudy to narrow results
  • Request enough fields in one call: title,authors,year,citationCount,abstract,tldr
  • If the first query returns no results, ask the user to refine their search rather than trying variations

Key parameters:

  • query: Search terms (required)
  • fields: Which data to return (see API reference)
  • limit: Number of results (default 10, max 100)
  • offset: For pagination

Step 4: Present Results

Format results clearly with:

  • Paper title
  • Authors (first author et al. if many)
  • Year published
  • Citation count
  • Brief abstract or TLDR if available
  • Link to paper (use paperId to construct URL)

Common City Coordinates (for reference, similar to weather skill pattern)

Not applicable - this skill uses text-based search queries.

Paper ID Formats

Semantic Scholar accepts multiple ID formats:

FormatExample
Semantic Scholar ID649def34f8be52c8b66281af98ae884c09aef38b
DOIDOI:10.18653/v1/N18-3011
ArXivARXIV:2106.15928
ACLACL:W12-3903
PubMedPMID:19872477
Corpus IDCorpusId:215416146

Resources

ALWAYS read these before making API calls:

  • references/api_reference.md - Complete Semantic Scholar API documentation
  • references/search_tips.md - Advanced search techniques and query optimization

Examples

Example 1: Topic Search

User asks: "Find papers about large language models from 2023"

  1. Load API reference
  2. Call: https://api.semanticscholar.org/graph/v1/paper/search?query=large+language+models&year=2023&fields=title,authors,year,citationCount,abstract&limit=10
  3. Format results with titles, authors, years, and citation counts

Example 2: Author Search

User asks: "What has Yann LeCun published recently?"

  1. First find author ID via author search
  2. Then get author's papers via author endpoint
  3. Filter/sort by year for recent work

Example 3: Citation Analysis

User asks: "What papers cite 'Attention Is All You Need'?"

  1. Find paper ID for "Attention Is All You Need"
  2. Use citations endpoint to get citing papers
  3. Present most influential citations (by citation count)

Example 4: Research Overview

User asks: "What does the research say about sleep and memory?"

  1. Search for papers on "sleep memory consolidation"
  2. Get top cited papers to find influential work
  3. Summarize key findings from abstracts
  4. Provide references for further reading

Notes

  • The API is free and does not require authentication for basic use
  • Rate limit: 1000 requests/second shared among unauthenticated users
  • For heavy use, request an API key for dedicated rate limits
  • Always include the fields parameter to get useful data back
  • Papers include TLDRs (AI-generated summaries) when available
  • Citation counts are updated regularly but may lag slightly

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 Research Assistant AI skill do?

Search academic papers, find citations, and explore research using Semantic Scholar. Use when user asks about scientific papers, research, citations, or academic topics.

Why use Research Assistant on TypingMind?

Because you install it once and use it with any model. Research Assistant 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 Research Assistant in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coleam00/custom-agent-with-skills/tree/main/skills/research_assistant. 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 Research Assistant?

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 Research Assistant?

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

Is the Research Assistant AI skill free?

It is published on GitHub by coleam00. Check the repository for licensing terms. 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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