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Traversing Citations

Community
wentorai
traversing-citations

Backward (references) and forward (citing papers) traversal via OpenAlex, with relevance filtering and deduplication

Overview

Publisherwentorai
RepositoryResearch-Claw
Skill nametraversing-citations
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 Traversing Citations 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/traversing-citations
  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/traversing-citations .claude/skills/traversing-citations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Traversing Citations 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 Traversing Citations 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 Traversing Citations 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.

Traversing Citation Networks

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

Follow citations backward (a paper's references) and forward (papers citing it) through OpenAlex, driven by rp_cite.

Core principle: Only expand papers that already scored >= 7, and only enqueue citations that pass relevance filtering. Filter before traversing to avoid explosion.

Why OpenAlex, not Scopus or Semantic Scholar: the personal Scopus key cannot return references or run forward-citation (REFEID) searches — both 403. Semantic Scholar was dropped from this pipeline: in testing it returned data: null for the references of papers it had clearly indexed (e.g. it reported 18 references but served none), failing silently. OpenAlex returned the full reference list for those same papers. OpenAlex is the single citation source here.

How to Traverse

One command, both directions

bash
rp_cite "10.1056/NEJMoa2300696" --direction both --limit 50 \
   

Returns:

json
{
  "seed": {"doi": "...", "title": "...", "openalex_id": "W..."},
  "backward": [ {normalized work: doi,title,year,abstract,cited_by,oa_pdf_url}, ... ],
  "forward":  [ {normalized work, sorted by cited_by desc}, ... ]
}
  • backward = referenced_works (what this paper cites), resolved to full metadata with abstracts in batch.
  • forward = papers whose cites field includes the seed, sorted by citation count (most-cited citing papers first — usually the highest-signal follow-ups).

Both arrays already carry abstracts, so you can score immediately with evaluating-paper-relevance without another fetch.

Filtering before enqueue

For each returned citation, score for relevance to the query (use the same rubric as evaluating-paper-relevance). Cheap pre-filter signals already in the record:

  • Title contains query keywords → strong
  • Abstract contains query terms → strong
  • Recent (forward citations < 3 yrs old) → prioritize
  • High cited_by → prioritize

Only enqueue citations scoring >= 5. Then evaluate enqueued papers normally.

Deduplicate

Before enqueuing, check papers-reviewed.json by DOI; skip if present. After evaluating any traversed paper, add it to papers-reviewed.json regardless of score, so it is never re-processed from another seed.

Record the relationship in citations/citation-graph.json:

json
{ "10.1056/nejmoa2300696": { "references": ["10.x/...", ...], "cited_by": ["10.y/...", ...] } }

Use only citation-graph.json for relationships and SUMMARY.md for findings — do not create ad-hoc files like forward_citation_dois.txt.

Smart Traversal Limits

  • Expand only seeds scoring >= 7.
  • Enqueue only citations scoring >= 5.
  • Depth <= 2 levels (seed → its citations → their citations, then stop).
  • Checkpoint with the user after every 50 total papers.
  • --limit caps each direction per call; raise deliberately, not by default.

Coverage Notes (honest limits)

  • OpenAlex reference coverage is high but not universal — a few works expose no referenced_works. When backward is empty for a paper that clearly has references, note it (⚠️ No references in OpenAlex) and continue; do not assume zero references.
  • Forward-citation counts track OpenAlex's graph, which can lag the very newest papers by weeks.

Common Mistakes

  • Hand-writing curl / using Semantic Scholar → use rp_cite (OpenAlex). SS is removed.
  • Following all citations → exponential blow-up; filter to >= 5 first.
  • Not deduping → check papers-reviewed.json before and after; add every evaluated paper.
  • Going too deep → cap at 2 levels and checkpoint at 50 papers.
  • Treating empty backward as truth → may be a coverage gap; flag it.

Next Steps

Evaluate enqueued papers with evaluating-paper-relevance; update SUMMARY.md and citation-graph.json; checkpoint at 50 papers.

Frequently asked questions

What does the Traversing Citations AI skill do?

Backward (references) and forward (citing papers) traversal via OpenAlex, with relevance filtering and deduplication

Why use Traversing Citations on TypingMind?

Because you install it once and use it with any model. Traversing Citations 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 Traversing Citations in TypingMind?

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

Which AI models can use Traversing Citations?

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 Traversing Citations?

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

Is the Traversing Citations 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.

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