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.
npx skills add beita6969/ScienceClaw --skill citation-analysisgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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.
[](https://agentmods.dev/skills/beita6969/scienceclaw/citation-analysis)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/citation-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/citation-analysis/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.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/citation-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/citation-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.01762 |
| Opus 5 | $0.00030 | $0.00881 |
| Sonnet 5 | $0.00012 | $0.00352 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
citation-analysis scanned grade A with 0 findings 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 10d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
- Impact Factor: Citations in year N to papers published in N-1 and N-2
- CiteScore: Citations over 4 years / documents over 4 years
- h5-index: h-index of articles published in the last 5 years
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.
- 10d ago First seen · 205 lines · 60 tokens per session scan A 204b16a5db12
citation-analysis is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 1,762 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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