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Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
Based on SOC occupation classification
Showing SKILL.md
| name | open-syllabus-api |
| description | Analyze most-taught books and texts via Open Syllabus analytics |
| metadata | {"openclaw":{"emoji":"📖","category":"domains","subcategory":"education","keywords":["Open Syllabus","syllabi analytics","teaching data","textbook rankings","curriculum analysis","higher education"],"source":"https://opensyllabus.org/"}} |
Open Syllabus analyzes 20M+ college course syllabi from 7,000+ institutions in 140+ countries, tracking which books, articles, and media are most frequently assigned in higher education. The Explorer provides teaching frequency rankings and co-assignment patterns. Useful for curriculum research, textbook selection, and understanding disciplinary norms. Free for basic search; institutional subscription for full API access.
# The primary interface is the web explorer:
# https://explorer.opensyllabus.org/
# Search by title, author, or field
# Filter by country, institution, discipline, year range
# API requires institutional subscription
# Base URL: https://api.opensyllabus.org/v1/
# Search titles
curl -H "Authorization: Bearer $OS_TOKEN" \
"https://api.opensyllabus.org/v1/titles?query=republic+plato&limit=20"
# Get title details
curl -H "Authorization: Bearer $OS_TOKEN" \
"https://api.opensyllabus.org/v1/titles/12345"
# Co-assignment analysis
curl -H "Authorization: Bearer $OS_TOKEN" \
"https://api.opensyllabus.org/v1/titles/12345/co-assigned?limit=20"
# Rankings by field
curl -H "Authorization: Bearer $OS_TOKEN" \
"https://api.opensyllabus.org/v1/rankings?field=Economics&limit=50"
| Parameter | Description | Example |
|---|---|---|
query | Search text | query=machine+learning |
field | Academic discipline | field=Computer Science |
country | Country filter | country=US |
institution | Institution filter | institution=Harvard |
year_from | Start year | year_from=2020 |
year_to | End year | year_to=2026 |
limit | Results per page | limit=50 |
| Metric | Description |
|---|---|
| Teaching Score | 0-100 normalized frequency of syllabi appearances |
| Count | Raw number of syllabi featuring the title |
| Rank | Position in overall or field-specific ranking |
| Co-assignment | Titles frequently taught alongside this one |
import requests
BASE_URL = "https://api.opensyllabus.org/v1"
def search_titles(query: str, field: str = None,
country: str = None,
limit: int = 20, token: str = "") -> list:
"""Search Open Syllabus for assigned titles."""
headers = {"Authorization": f"Bearer {token}"} if token else {}
params = {"query": query, "limit": limit}
if field:
params["field"] = field
if country:
params["country"] = country
resp = requests.get(
f"{BASE_URL}/titles",
headers=headers,
params=params,
)
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("results", []):
results.append({
"title": item.get("title"),
"authors": item.get("authors"),
"teaching_score": item.get("teaching_score"),
"count": item.get("appearance_count"),
"rank": item.get("rank"),
"top_fields": item.get("top_fields", []),
})
return results
() -> :
headers = {: } token {}
resp = requests.get(
,
headers=headers,
params={: limit},
)
resp.raise_for_status()
resp.json().get(, [])
() -> :
headers = {: } token {}
resp = requests.get(
,
headers=headers,
params={: field, : limit},
)
resp.raise_for_status()
resp.json().get(, [])
| Rank | Title | Author | Field |
|---|---|---|---|
| 1 | The Elements of Style | Strunk & White | Writing |
| 2 | The Republic | Plato | Philosophy |
| 3 | A Manual for Writers | Turabian | Writing |
| ~10 | Thinking, Fast and Slow | Kahneman | Psychology |
| ~50 | Introduction to Algorithms | CLRS | CS |