Searching Literature logo

Searching Literature

Community
wentorai
searching-literature

Scopus relevance search (main) enriched with OpenAlex abstracts and OA links, via the rp_* tools pipeline

Overview

Publisherwentorai
RepositoryResearch-Claw
Skill namesearching-literature
Stars
859
Forks
117
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 wentorai on GitHub. Read the source before you install it.

Installation

Install the Searching Literature 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/wentorai/Research-Claw/tree/main/skills/searching-literature
  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/wentorai/Research-Claw.git /tmp/Research-Claw
mkdir -p .claude/skills
cp -r /tmp/Research-Claw/skills/searching-literature .claude/skills/searching-literature
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Searching Literature 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 Searching Literature 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 Searching Literature 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.

Searching Scientific Literature

RC native tools — this skill's pipeline ships as four built-in RC tools. Call them directly; do not shell out to any rp.py script or write curl:

  • rp_search({ query, limit?, min_year? }) — Scopus relevance search enriched with OpenAlex abstracts + OA PDF links. The Elsevier key is built in.
  • rp_abstracts({ dois }) — batch abstracts + OA links for a DOI list (OpenAlex, no key).
  • rp_cite({ doi, direction?, limit? }) — citation traversal (direction: both / backward / forward).
  • rp_fulltext({ doi, out? }) — OA full text (Elsevier ScienceDirect OA → OpenAlex OA fallback); pass out to also save the text to a file.

Results return inline as JSON (there is no --json <file> flag). To persist a result set, save the returned JSON with the workspace file tools.

Overview

Discovery runs on one backbone: Scopus (relevance-ranked, all-publisher coverage, real citation counts), with OpenAlex layered on only to supply the data Scopus's personal-key tier withholds (abstracts, OA full-text links). Both are driven by the the rp_* tools tool — you do not hand-write curl.

Core principle: Scopus decides what and in what order; OpenAlex fills in abstracts.

Key entitlement reality (tested): a personal Scopus API key exposes the STANDARD search view only — title, DOI, EID, citation count, year, OA flag. It does not return abstracts, references, or forward citations. That is why abstracts come from OpenAlex and citation traversal lives in traversing-citations (also OpenAlex). Do not try to pull abstracts/refs from Scopus; the calls 403.

How to Search

1. Build the Scopus query

Scopus query syntax (richer than plain keywords — use it):

  • TITLE-ABS-KEY( ... ) — search title, abstract, keywords (the default workhorse)
  • AND / OR / AND NOT — boolean
  • W/n — proximity (e.g. BTK W/3 selectivity)
  • PUBYEAR > 2018 — date constraint
  • DOCTYPE(ar) — article type (ar=article, re=review)

Examples:

TITLE-ABS-KEY(("BTK" OR "Bruton tyrosine kinase") AND inhibitor AND (selectivity OR "off-target"))
TITLE-ABS-KEY("CRISPR" AND cardiomyocyte) AND PUBYEAR > 2019

2. Run the search

bash
rp_search "TITLE-ABS-KEY(BTK inhibitor selectivity)" \
    --limit 50 --min-year 2015

This single command:

  1. Pages Scopus by relevancy (citation counts included).
  2. Batch-fetches OpenAlex by DOI (50/call) to attach abstract, oa_status, oa_pdf_url.
  3. Writes candidate records ready for scoring.

Each record:

json
{
  "scopus_id": "85...", "eid": "2-s2.0-85...", "doi": "10.1016/...",
  "title": "...", "year": "2023", "cited_by": 42, "oa": true,
  "source": "Journal of Medicinal Chemistry",
  "abstract": "We report ...", "openalex_id": "W...",
  "oa_status": "gold", "oa_pdf_url": "https://..."
}

3. Report

Announce: 🔎 Scopus: N papers · M with abstracts (OpenAlex). Then hand the candidate list to evaluating-paper-relevance for scoring.

Coverage Notes (honest limits)

  • Abstract gaps: OpenAlex lacks abstracts for some closed papers (notably parts of Elsevier/ACS). Expect roughly 50–80% abstract coverage. Papers with "abstract": "" must be scored from title + journal + citation count only, or deferred — never silently dropped. Flag them: ⚠️ No abstract (score from title).
  • Scopus quota: 20,000 search results/week on the personal key. The pipeline paginates 25/call, so a 50-result search costs 2 calls. Budget accordingly for large sweeps.
  • No Scopus abstracts/refs/fulltext-search on this key — by design, see above.

Quick Reference

TaskCommand
Search + enrichrp_search "<scopus query>" --limit N
Narrowadd AND, PUBYEAR > Y, DOCTYPE(ar), more specific TITLE-ABS-KEY
Broadenuse OR, drop constraints, add synonyms

Common Mistakes

  • Hand-writing curl to Scopus → use the rp_* tools; it handles paging, rate limits, caching.
  • Expecting abstracts from Scopus → they come from OpenAlex; some will be missing.
  • Dropping abstract-less papers → score them from title/citations or defer, don't hide them.
  • Ignoring the weekly quota → 20k Scopus results/week; don't burn it on over-broad sweeps.

Next Steps

After search: score with evaluating-paper-relevance; expand high scorers with traversing-citations.

Frequently asked questions

What does the Searching Literature AI skill do?

Scopus relevance search (main) enriched with OpenAlex abstracts and OA links, via the rp_* tools pipeline

Why use Searching Literature on TypingMind?

Because you install it once and use it with any model. Searching Literature 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 Searching Literature in TypingMind?

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

Which AI models can use Searching Literature?

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 Searching Literature?

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

Is the Searching Literature AI skill free?

It is published on GitHub by wentorai. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇