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Open syllabus api

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/43-wentorai-research-plugins/skills/domains/education/open-syllabus-api

Analyze most-taught books and texts via Open Syllabus analyticsFrom its SKILL.md

Install
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill open-syllabus-api

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SKILL.md

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Open Syllabus API

Overview

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.

Explorer Interface

Web Search

# 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 Access

# 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"

Query Parameters

ParameterDescriptionExample
querySearch textquery=machine+learning
fieldAcademic disciplinefield=Computer Science
countryCountry filtercountry=US
institutionInstitution filterinstitution=Harvard
year_fromStart yearyear_from=2020
year_toEnd yearyear_to=2026
limitResults per pagelimit=50

Key Metrics

MetricDescription
Teaching Score0-100 normalized frequency of syllabi appearances
CountRaw number of syllabi featuring the title
RankPosition in overall or field-specific ranking
Co-assignmentTitles frequently taught alongside this one

Python Usage

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


def get_co_assigned(title_id: int, limit: int = 20,
                    token: str = "") -> list:
    """Get titles frequently co-assigned with a given title."""
    headers = {"Authorization": f"Bearer {token}"} if token else {}
    resp = requests.get(
        f"{BASE_URL}/titles/{title_id}/co-assigned",
        headers=headers,
        params={"limit": limit},
    )
    resp.raise_for_status()
    return resp.json().get("results", [])


def get_field_rankings(field: str, limit: int = 50,
                       token: str = "") -> list:
    """Get most-taught titles in a field."""
    headers = {"Authorization": f"Bearer {token}"} if token else {}
    resp = requests.get(
        f"{BASE_URL}/rankings",
        headers=headers,
        params={"field": field, "limit": limit},
    )
    resp.raise_for_status()
    return resp.json().get("results", [])


# Example: find most-taught economics texts
# results = search_titles("microeconomics", field="Economics")
# for r in results:
#     print(f"#{r['rank']} {r['title']} — {r['authors']}")
#     print(f"  Teaching Score: {r['teaching_score']} "
#           f"({r['count']} syllabi)")

Top Assigned Works (Examples)

RankTitleAuthorField
1The Elements of StyleStrunk & WhiteWriting
2The RepublicPlatoPhilosophy
3A Manual for WritersTurabianWriting
~10Thinking, Fast and SlowKahnemanPsychology
~50Introduction to AlgorithmsCLRSCS

Use Cases

  1. Curriculum design: Find canonical texts in a discipline
  2. Textbook market research: Identify widely adopted materials
  3. Teaching trends: Track changes in assigned readings over time
  4. Interdisciplinary mapping: Discover texts bridging fields
  5. Academic publishing: Understand teaching impact vs. citation impact

References

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.