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Getting Started With Research Superpowers

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brycewang-stanford
Getting Started with Research Superpowers

Introduction to literature search & review skills - systematic paper finding, screening, extraction, and citation traversal

Overview

Publisherbrycewang-stanford
RepositoryAuto-Empirical-Research-Skills
Skill nameGetting Started with Research Superpowers
Stars
3.8K
Forks
479
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Getting Started With Research Superpowers 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.
  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/brycewang-stanford/Auto-Empirical-Research-Skills.git /tmp/Auto-Empirical-Research-Skills
mkdir -p .claude/skills
cp -r /tmp/Auto-Empirical-Research-Skills/skills/05-kthorn-research-superpower/getting-started .claude/skills/brycewang-stanford-getting-started-with-research-superpowers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Getting Started With Research Superpowers 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 Getting Started With Research Superpowers 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 Getting Started With Research Superpowers 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.

Getting Started with Research Superpowers

Research Superpowers gives Claude Code systematic workflows for literature searching and review.

Focus: Finding, screening, and extracting data from published papers. NOT for analyzing experimental data or designing experiments.

What You Can Do

Use these skills for systematic literature reviews:

  • Search literature - PubMed and Semantic Scholar integration
  • Build screening rubrics - Define and test relevance criteria collaboratively
  • Screen papers - Two-stage screening (abstract → deep dive) with scoring
  • Extract data - Find specific methods, results, measurements from papers
  • Traverse citations - Smart backward/forward citation following
  • Large-scale screening - Parallel subagent processing for 50+ papers
  • Track findings - Organized research sessions with summaries, PDFs, and deduplication

Available Skills

Literature Search & Review Skills (skills/research/)

  • answering-research-questions - Main orchestration workflow (search → screen → extract → synthesize)
  • building-screening-rubrics - Collaborative rubric design with test-driven refinement
  • searching-literature - PubMed search with keyword optimization
  • evaluating-paper-relevance - Two-stage screening (abstract → deep dive)
  • subagent-driven-review - Parallel screening for large searches (50+ papers)
  • checking-chembl - Check if medicinal chemistry papers have curated SAR data in ChEMBL
  • traversing-citations - Semantic Scholar citation network traversal
  • finding-open-access-papers - Unpaywall API to find free versions of paywalled papers
  • cleaning-up-research-sessions - Safe cleanup of intermediate files after research complete

Basic Workflow

When user asks a literature search question:

  1. Read answering-research-questions skill - Main orchestration
  2. Announce: "I'm using the Answering Research Questions skill"
  3. Parse query - Extract keywords, data types, constraints
  4. Create research folder - Propose name, initialize tracking
  5. Optional: Build rubric - For large searches (50+ papers), use building-screening-rubrics skill
  6. Search → Screen → Extract → Traverse - Follow the workflow
  7. Check in regularly - Every 10 papers, checkpoint every 50

Research Session Folders

Each query creates a folder in research-sessions/:

research-sessions/YYYY-MM-DD-query-description/
├── SUMMARY.md              # Main findings
├── papers-reviewed.json    # Deduplication tracking (DOI → status)
├── papers/                 # Downloaded PDFs and supplementary data
└── citations/              # Citation graph tracking

Core Principles

For systematic literature review:

  • Precision over breadth - Find papers with specific data you need, not just topical matches
  • Test-driven screening - Build and validate rubrics before bulk processing
  • Smart citation following - Only traverse relevant citations to avoid exponential explosion
  • Deduplicate aggressively - Track ALL reviewed papers by DOI (even non-relevant)
  • Cache abstracts - Save for re-screening when rubrics change
  • Report progress - Update user every 10 papers as work proceeds
  • Checkpoint frequently - Ask to continue or stop every 50 papers
  • Reproducible - Save rubrics, queries, and methodology with research sessions

API Information

PubMed E-utilities (no key required):

  • Search: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi
  • Details: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi
  • Full text: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi

Semantic Scholar (free tier works, optional key for higher limits):

  • Paper: https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}
  • References: https://api.semanticscholar.org/graph/v1/paper/{id}/references
  • Citations: https://api.semanticscholar.org/graph/v1/paper/{id}/citations

Finding Skills

Use the find-skills script to search for relevant skills:

bash
# From project directory
./scripts/find-skills              # List all skills
./scripts/find-skills literature   # Search for "literature"
./scripts/find-skills 'cite|ref'   # Regex search

Remember

  • Always start by reading the relevant research skill
  • Announce skill usage when you begin
  • Track everything in the research folder
  • Check in with user regularly during long searches
  • Deduplicate using papers-reviewed.json (DOI as key)

Frequently asked questions

What does the Getting Started With Research Superpowers AI skill do?

Introduction to literature search & review skills - systematic paper finding, screening, extraction, and citation traversal

Why use Getting Started With Research Superpowers on TypingMind?

Because you install it once and use it with any model. Getting Started With Research Superpowers 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 Getting Started With Research Superpowers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/05-kthorn-research-superpower/getting-started. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Getting Started With Research Superpowers?

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 Getting Started With Research Superpowers?

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

Is the Getting Started With Research Superpowers AI skill free?

It is published on GitHub by brycewang-stanford. 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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