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Paper discovery via recommendation APIs (OpenAlex, CrossRef citation networks)
Passed 21 of 23 test cases with the skill, and 19 of 23 without it.
2 fixed19 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
What are the 6 ordered stages of a paper discovery reading list pipeline using OpenAlex, starting from initial seed selection through final output?
| Checked | Without skill | With skill |
|---|---|---|
| The pipeline stages list seed selection, concept expansion, forward citation, backward citation, deduplication, and ranking in sequence. | Missed | Passed |
The instructions your model reads when the skill loads.
Leverage the OpenAlex and CrossRef APIs to discover related papers, traverse citation networks, and build comprehensive reading lists programmatically.
OpenAlex indexes over 250 million academic works and provides a free, no-key-required API that supports:
Base URL: https://api.openalex.org
CrossRef URL: https://api.crossref.org
Use OpenAlex's concept graph and citation data to discover related work from seed papers.
import requests
HEADERS = {"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"}
WORK_ID = "W2741809807" # OpenAlex work ID
# Get the seed paper's concepts
response = requests.get(
f"https://api.openalex.org/works/{WORK_ID}",
headers=HEADERS
)
paper = response.json()
concepts = [c["id"] for c in paper.get("concepts", [])[:3]]
# Find works sharing the same concepts, sorted by citations
for concept_id in concepts:
related = requests.get(
test cases passed, vs 19 / 23 without
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