# Citation Analysis

Analyze citation networks, compute bibliometric indicators, and identify research fronts.

- Repository: https://github.com/beita6969/ScienceClaw (1024 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add beita6969/ScienceClaw --skill citation-analysis -g -a claude-code -y`
Codex: `npx skills add beita6969/ScienceClaw --skill citation-analysis -g -a codex -y`
Cursor: `npx skills add beita6969/ScienceClaw --skill citation-analysis -g -a cursor -y`
Gemini CLI: `npx skills add beita6969/ScienceClaw --skill citation-analysis -g -a gemini-cli -y`

## Description

Analyze citation networks, compute bibliometric indicators, and identify research fronts. Use when: user asks about citation patterns, h-index, co-authorship networks, research trends, or bibliometric analysis. NOT for: literature searching (use literature-search) or writing papers (use paper-writing).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Citation Analysis

Analyze citation networks, compute bibliometric indicators, and identify research fronts using Semantic Scholar, OpenAlex, and CrossRef data.

## When to Use

- "What's the h-index of this author?"
- "Show me the citation network for this paper"
- "Identify the most influential papers in this field"
- "Map the co-authorship network in this area"
- "What are the emerging research fronts in NLP?"
- "Analyze citation trends for CRISPR papers over time"

## When NOT to Use

- Finding papers by topic (use literature-search)
- Reading or summarizing papers (use scienceclaw-summarization)
- Writing papers (use paper-writing)
- Statistical analysis unrelated to citations (use statsmodels-stats)

## Bibliometric Indicators

### Author-Level Metrics

```python
import numpy as np

def h_index(citations: list[int]) -> int:
    """Compute h-index from a list of citation counts."""
    sorted_c = sorted(citations, reverse=True)
    h = 0
    for i, c in enumerate(sorted_c):
        if c >= i + 1:
            h = i + 1
        else:
            break
    return h

def g_index(citations: list[int]) -> int:
    """Compute g-index: largest g such that top g papers have >= g^2 citations."""
    sorted_c = sorted(citations, reverse=True)
    cumsum = np.cumsum(sorted_c)
    g = 0
    for i in range(len(sorted_c)):
        if cumsum[i] >= (i + 1) ** 2:
            g = i + 1
    return g

def i10_index(citations: list[int]) -> int:
    """Number of papers with 10+ citations."""
    return sum(1 for c in citations if c >= 10)
```

### Paper-Level Metrics

- **Citation count**: Raw count from Semantic Scholar / OpenAlex
- **Field-weighted citation impact (FWCI)**: Citations / expected citations in field
- **Percentile rank**: Position relative to same-year, same-field papers
- **Citation velocity**: Citations per year since publication

### Journal-Level Metrics

The rest: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/citation-analysis/SKILL.md