Plumx Metrics ApiSAFE
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Overview
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
e1ba289846fdOBSERVED · 2026-10-08Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| openclaw | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: plumx-metrics-api
description: "Track research impact beyond citations via PlumX altmetrics API"
metadata:
openclaw:
emoji: "📊"
category: "literature"
subcategory: "metadata"
keywords: ["PlumX", "altmetrics", "research impact", "social media metrics", "usage statistics", "scholarly metrics"]
source: "https://plumanalytics.com/"
---
# PlumX Metrics API
## Overview
PlumX (by Elsevier/Plum Analytics) tracks 5 categories of research impact metrics beyond traditional citations: Usage, Captures, Mentions, Social Media, and Citations. It covers 130M+ research artifacts including articles, datasets, presentations, and videos. Available via Elsevier's API infrastructure. Requires an Elsevier API key.
## Metric Categories
| Category | What it measures | Examples |
|----------|-----------------|---------|
| **Usage** | Reading/viewing | Abstract views, PDF downloads, HTML views |
| **Captures** | Saving for later | Mendeley readers, CiteULike bookmarks |
| **Mentions** | Commentary | Blog posts, news articles, Wikipedia refs |
| **Social Media** | Sharing/discussion | Tweets, Facebook shares, Reddit posts |
| **Citations** | Formal references | Scopus, CrossRef, PubMed citations |
## API Endpoints
### Base URL
```
https://api.elsevier.com/analytics/plumx/
```
### Get Metrics by DOI
```bash
curl -H "X-ELS-APIKey: $ELSEVIER_API_KEY" \
"https://api.elsevier.com/analytics/plumx/doi/10.1038/nature14539"
```
### Get Metrics by Other IDs
```bash
# By PubMed ID
curl -H "X-ELS-APIKey: $ELSEVIER_API_KEY" \
"https://api.elsevier.com/analytics/plumx/pmid/25428114"
# By ISBN
curl -H "X-ELS-APIKey: $ELSEVIER_API_KEY" \
"https://api.elsevier.com/analytics/plumx/isbn/9780262035613"
# By Scopus ID
curl -H "X-ELS-APIKey: $ELSEVIER_API_KEY" \
"https://api.elsevier.com/analytics/plumx/scopusId/84920765826"
```
## Response Structure
```json
{
"count_categories": [
{
"name": "capture",
"total": 15432,
"count_types": [
{"name": "READER_COUNT", "total": 15432, "sources": [
{"name": "Mendeley", "total": 15432}
]}
]
},
{
"name": "socialMedia",
"total": 3250,
"count_types": [
{"name": "TWEET_COUNT", "total": 2800},
{"name": "FACEBOOK_COUNT", "total": 450}
]
},
{
"name": "citation",
"total": 2100,
"count_types": [
{"name": "Scopus", "total": 1800},
{"name": "CrossRef", "total": 2100}
]
},
{
"name": "usage",
"total": 45000,
"count_types": [
{"name": "ABSTRACT_VIEWS", "total": 30000},
{"name": "LINK_OUTS", "total": 15000}
]
},
{
"name": "mention",
"total": 85,
"count_types": [
{"name": "NEWS_COUNT", "total": 45},
{"name": "BLOG_COUNT", "total": 25},
{"name": "WIKIPEDIA_COUNT", "total": 15}
]
}
]
}
```
## Python Usage
```python
import os
import requests
API_KEY = os.environ["ELSEVIER_API_KEY"]
BASE_URL = "https://api.elsevier.com/analytics/plumx"
HEADERS = {"X-ELS-APIKey": API_KEY, "Accept": "application/json"}
def get_plumx_metrics(doi: str) -> dict:
"""Get PlumX metrics for a paper by DOI."""
resp = requests.get(
f"{BASE_URL}/doi/{doi}",
headers=HEADERS,
)
resp.raise_for_status()
data = resp.json()
metrics = {}
for cat in data.get("count_categories", []):
category_name = cat["name"]
metrics[category_name] = {
"total": cat["total"],
"breakdown": {},
}
for ct in cat.get("count_types", []):
metrics[category_name]["breakdown"][ct["name"]] = ct["total"]
return metrics
def compare_impact(dois: list) -> list:
"""Compare PlumX metrics across multiple papers."""
results = []
for doi in dois:
metrics = get_plumx_metrics(doi)
results.append({
"doi": doi,
"citations": metrics.get("citation", {}).get("total", 0),
"captures": metrics.get("capture", {}).get("total", 0),
"social": metrics.get("socialMedia", {}).get("total", 0),
"usage": metrics.get("usage", {}).get("total", 0),
"mentions": metrics.get("mention", {}).get("total", 0),
})
return results
# Example: analyze a paper's multi-dimensional impact
metrics = get_plumx_metrics("10.1038/nature14539")
for category, data in metrics.items():
print(f"\n{category.upper()} (total: {data['total']})")
for metric_type, count in data["breakdown"].items():
print(f" {metric_type}: {count}")
# Example: compare two papers
# comparison = compare_impact([
# "10.1038/nature14539",
# "10.1126/science.aax2342",
# ])
```
## PlumX vs Other Altmetric Services
| Feature | PlumX | Altmetric.com | Crossref Event Data |
|---------|-------|---------------|---------------------|
| Metric categories | 5 comprehensive | Attention Score | Events only |
| Coverage | 130M+ artifacts | 30M+ outputs | DOI-based |
| Social media | Twitter, Facebook, Reddit | Twitter, Reddit, News | Twitter, Reddit, Wikipedia |
| Usage data | Yes (views, downloads) | No | No |
| Capture data | Yes (Mendeley readers) | Mendeley readers | No |
| Free access | Limited | Limited widget | Full API free |
## References
- [PlumX Metrics](https://plumanalytics.com/learn/about-metrics/)
- [Elsevier Developer Portal](https://dev.elsevier.com/)
- [PlumX API Documentation](https://dev.elsevier.com/documentation/PlumXMetricsAPI.wadl)Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
e1ba289846fdfull audit observations/trust-audit/skill/brycewang-stanford__plumx-metrics-api.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | e1ba289846fd | SAFE | B | 89 | first audit |
Questions
What does the Plumx Metrics Api skill do?
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Is Plumx Metrics Api safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Plumx Metrics Api access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Plumx Metrics Api work with?
Its documentation mentions openclaw. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (e1ba289846fd), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.