一键导入
api-guide
api_guide skill
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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api_guide skill
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | api_guide |
| description | api_guide skill |
| metadata | {"short-description":"api_guide skill","category":"utilities","source":"claude-code-templates"} |
Base URL: https://api.openalex.org
Authentication: None required
Rate Limits:
?sample parameter for random samplinghttps://api.openalex.org/works?sample=20&seed=123
For large samples (10k+), use multiple seeds and deduplicate.
Incorrect: ?page=5, ?page=17 - This biases results!
1. Find entity ID: /authors?search=einstein
2. Use ID: /works?filter=authorships.author.id:A5023888391
Incorrect: /works?filter=author_name:Einstein - Names are ambiguous!
?per-page=200
This is 8x faster than default (25).
Default is only 25 results per page.
/works?filter=doi:10.1/abc|10.2/def|10.3/ghi
Up to 50 values per filter.
Making 100 separate calls when you can batch them is inefficient.
for attempt in range(max_retries):
try:
response = requests.get(url)
if response.status_code == 200:
return response.json()
except:
wait_time = 2 ** attempt
time.sleep(wait_time)
?mailto=yourname@example.edu
Increases from 1 req/sec → 10 req/sec.
/works - 240M+ scholarly documents/authors - Researcher profiles/sources - Journals, repositories, conferences/institutions - Universities, research organizations/topics - Subject classifications (3-level hierarchy)/publishers - Publishing organizations/funders - Funding agencies/text - Tag your own text with topics/keywords (POST)| Parameter | Description | Example |
|---|---|---|
filter= | Filter results | ?filter=publication_year:2020 |
search= | Full-text search | ?search=machine+learning |
sort= | Sort results | ?sort=cited_by_count:desc |
per-page= | Results per page (max 200) | ?per-page=200 |
page= | Page number | ?page=2 |
sample= | Random results | ?sample=50&seed=42 |
select= | Limit fields | ?select=id,title |
group_by= | Aggregate by field | ?group_by=publication_year |
mailto= | Email for polite pool | ?mailto=you@example.edu |
Single filter: ?filter=publication_year:2020
Multiple (AND): ?filter=publication_year:2020,is_oa:true
Values (OR): ?filter=type:journal-article|book
Negation: ?filter=type:!journal-article
Greater than: ?filter=cited_by_count:>100
Less than: ?filter=publication_year:<2020
Range: ?filter=publication_year:2020-2023
Repeat filter: ?filter=institutions.country_code:us,institutions.country_code:gb
Use + symbol: ?filter=institutions.country_code:us+gb
Both mean: "works with author from US AND author from GB"
Any of these: ?filter=institutions.country_code:us|gb|ca
Batch IDs: ?filter=doi:10.1/abc|10.2/def
Up to 50 values with pipes.
# Small sample
https://api.openalex.org/works?sample=20&seed=42
# Large sample (10k+) - make multiple requests
https://api.openalex.org/works?sample=1000&seed=1
https://api.openalex.org/works?sample=1000&seed=2
# Then deduplicate by ID
# Simple search
https://api.openalex.org/works?search=machine+learning
# Search specific field
https://api.openalex.org/works?filter=title.search:CRISPR
# Search + filter
https://api.openalex.org/works?search=climate&filter=publication_year:2023
# Step 1: Get author ID
https://api.openalex.org/authors?search=Heather+Piwowar
# Returns: "id": "https://openalex.org/A5023888391"
# Step 2: Get their works
https://api.openalex.org/works?filter=authorships.author.id:A5023888391
# Step 1: Get institution ID
https://api.openalex.org/institutions?search=MIT
# Returns: "id": "https://openalex.org/I136199984"
# Step 2: Get their works
https://api.openalex.org/works?filter=authorships.institutions.id:I136199984
https://api.openalex.org/works?filter=publication_year:>2020&sort=cited_by_count:desc&per-page=200
# All OA
https://api.openalex.org/works?filter=is_oa:true
# Gold OA only
https://api.openalex.org/works?filter=open_access.oa_status:gold
# Recent OA works about COVID from top institutions
https://api.openalex.org/works?filter=publication_year:2022,is_oa:true,title.search:covid,authorships.institutions.id:I136199984|I27837315
# Get specific works by DOI (up to 50 per request)
https://api.openalex.org/works?filter=doi:https://doi.org/10.1371/journal.pone.0266781|https://doi.org/10.1371/journal.pone.0267149&per-page=50
# Top topics
https://api.openalex.org/works?group_by=topics.id
# Papers per year
https://api.openalex.org/works?group_by=publication_year
# Most prolific institutions
https://api.openalex.org/works?group_by=authorships.institutions.id
# First page
https://api.openalex.org/works?filter=publication_year:2023&per-page=200
# Next pages
https://api.openalex.org/works?filter=publication_year:2023&per-page=200&page=2
{
"meta": {
"count": 240523418,
"db_response_time_ms": 42,
"page": 1,
"per_page": 25
},
"results": [
{ /* entity object */ }
]
}
https://api.openalex.org/works/W2741809807
→ Returns Work object directly (no meta/results wrapper)
{
"meta": { "count": 100 },
"group_by": [
{
"key": "https://openalex.org/T10001",
"key_display_name": "Artificial Intelligence",
"count": 15234
}
]
}
| Filter | Description | Example |
|---|---|---|
authorships.author.id | Author's OpenAlex ID | A5023888391 |
authorships.institutions.id | Institution's ID | I136199984 |
cited_by_count | Citation count | >100 |
is_oa | Is open access | true/false |
publication_year | Year published | 2020, >2020, 2018-2022 |
primary_location.source.id | Source (journal) ID | S137773608 |
topics.id | Topic ID | T10001 |
type | Document type | article, book, dataset |
has_doi | Has DOI | true/false |
has_fulltext | Has fulltext | true/false |
| Filter | Description |
|---|---|
last_known_institution.id | Current/last institution |
works_count | Number of works |
cited_by_count | Total citations |
orcid | ORCID identifier |
DOI: /works/https://doi.org/10.7717/peerj.4375
PMID: /works/pmid:29844763
ORCID: /authors/https://orcid.org/0000-0003-1613-5981
ROR: /institutions/https://ror.org/02y3ad647
ISSN: /sources/issn:0028-0836
?per-page=200 (8x fewer calls)?select=id,title,publication_year?mailto=you@example.edu for 10x speed boost200 - Success400 - Bad request (check filter syntax)403 - Rate limit exceeded (implement backoff)404 - Entity doesn't exist500 - Server error (retry with backoff)def fetch_with_retry(url, max_retries=5):
for attempt in range(max_retries):
try:
response = requests.get(url, timeout=30)
if response.status_code == 200:
return response.json()
elif response.status_code in [403, 500, 502, 503, 504]:
wait_time = 2 ** attempt
time.sleep(wait_time)
else:
response.raise_for_status()
except requests.exceptions.Timeout:
if attempt < max_retries - 1:
time.sleep(2 ** attempt)
else:
raise
raise Exception(f"Failed after {max_retries} retries")
?sample=per-page=200mailto=select=Invoke this skill with:
$api_guide [arguments]
Or let Codex auto-select based on your prompt.
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