Traversing Citation Networks

Traversing Citation Networks is a skill for Claude Code from kthorn/research-superpower. It costs 22 tokens per session (2,317 once invoked), scanned A, original, MIT.

A guide for following relevant references and citing papers through Semantic Scholar, a research-paper search and citation database. It filters results and removes duplicates while moving backward or forward through a paper's citation network.

In plain words
What is it for?
Use it to find papers cited by a relevant study, papers that cite it, or broader related work for a research question.
Why use it?
It helps find related research without collecting every paper connected to the original one, including papers that are off-topic or repeated.

Skill for Claude Code

Written for Claude Code: when-to-use in frontmatter.

Part of the research-superpower plugin — 10 skills, 1 hook shipped together

Good fit Use it to find papers cited by a relevant study, papers that cite it, or broader related work for a research question.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kthorn/research-superpower/traversing-citations
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add kthorn/research-superpower --skill traversing-citations
Clone the repo
git clone --depth 1 https://github.com/kthorn/research-superpower

Made for: Claude Code.

Or install research-superpower, the plugin that ships this one along with the rest of its 10 skills, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for Traversing Citation Networks

README.md
[![agentmods](https://agentmods.dev/badge/skills/kthorn/research-superpower/traversing-citations/github.svg)](https://agentmods.dev/skills/kthorn/research-superpower/traversing-citations)
Your own site
<a href="https://agentmods.dev/skills/kthorn/research-superpower/traversing-citations"><img src="https://agentmods.dev/badge/skills/kthorn/research-superpower/traversing-citations/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for Traversing Citation Networks

Your own site · 80×15
<a href="https://agentmods.dev/skills/kthorn/research-superpower/traversing-citations"><img src="https://agentmods.dev/badge/skills/kthorn/research-superpower/traversing-citations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,317 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.02317
Opus 5 $0.00011 $0.01158
Sonnet 5 $0.00004 $0.00463
Haiku 4.5 $0.00002 $0.00232

Measured 9d ago against content hash 98589c5a07bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

Traversing Citation Networks scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example.2023?fields=paperId,title,year"
skills/research/traversing-citations/SKILL.md · 288 lines

How it starts

The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Traversing Citation Networks

Overview

Intelligently follow citations backward (references) and forward (citing papers) using Semantic Scholar API.

Core principle: Only follow citations relevant to user's query. Avoid exponential explosion by filtering before traversing.

When to Use

Use this skill when:

  • Found a highly relevant paper (score ≥ 7)
  • Need to find related work
  • User asks "what papers cite this?"
  • Building comprehensive understanding of a topic

When NOT to use:

  • Paper scored < 7 (not relevant enough to follow)
  • Already at 50 papers (check with user first)
  • Citations look off-topic from abstract

Citation Traversal Strategy

1. Get Paper ID from Semantic Scholar

Lookup by DOI:

curl "https://api.semanticscholar.org/graph/v1/paper/DOI:10.1234/example.2023?fields=paperId,title,year"

Response:

{
  "paperId": "abc123def456",
  "title": "Paper Title",
  "year": 2023
}

Save paperId - needed for citations/references queries

2. Backward Traversal (References)

Get references from paper:

curl "https://api.semanticscholar.org/graph/v1/paper/abc123def456/references?fields=contexts,intents,title,year,abstract,externalIds&limit=100"

Response format:

{
  "data": [
    {
      "citedPaper": {
        "paperId": "xyz789",
        "title": "Referenced Paper Title",
        "year": 2020,
        "abstract": "...",
        "externalIds": {
          "DOI": "10.5678/referenced.2020",
          "PubMed": "87654321"
        }
      },
      "contexts": [
        "...as described in previous work [15]...",
        "...we used the method from [15] to..."
      ],
      "intents": ["methodology", "background"]
    }
  ]
}

Filter for relevance:

For each reference, check:

  1. Context keywords: Do citation contexts mention user's query terms?
    • Example: If user asks about "IC50 values", look for contexts mentioning "IC50", "activity", "potency"
  2. Title match: Does title contain relevant keywords?
  3. Intent: Is intent "methodology" or "result" (more relevant) vs "background" (less relevant)?

Read the full file on GitHub · 288 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 288 lines · 22 tokens per session scan A 98589c5a07bd

Subscribe to this mod's changes

Traversing Citation Networks is a skill published in the GitHub repository kthorn/research-superpower (123 stars, last pushed 10mo ago), licensed MIT. It adds 22 tokens to every session and 2,317 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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