scholar-search-x49

Search academic papers and authors via the Scholar API (scholar.x49.ai). Use for finding papers by topic, keyword, or natural-language query, discovering authors, getting search suggestions, looking up paper metadata, checking journal rankings (JCR/CCF/FQBJCR), or filtering by open access, year range, paper type, or venue. Covers all academic disciplines. Triggers when user mentions academic papers, scholarly articles, research papers, literature search, journal articles, author lookup, citation counts, open access, or wants to search scholarly databases.

wahahaaaa123@wahahaaaa123

Install

openclaw skills install @wahahaaaa123/scholar-search-x49

Scholar Search

Search academic papers and authors via the Scholar API at https://scholar.x49.ai.

When to Use This Skill

Use this skill when the user needs to:

Search for academic papers by topic, keyword, or natural-language query
Find papers from specific years, venues, authors, or institutions
Get search autocomplete suggestions
Look up detailed metadata for one or more papers by their paper_ref
Find researchers by name and get their profiles
List an author's representative or recent papers
Check journal rankings (JCR, CCF, FQBJCR tiers)
Filter by open access, paper type, or citation count

API Configuration

The API base URL is https://scholar.x49.ai/api/v1.

Authentication uses a Bearer token. Resolve the key in this order:

Environment variable SCHOLAR_API_KEY
Built-in free key: psk_tLzPCmJdUw5oAHGeXL2H_fMrDdSyiF_SBJfn2p5uCO4

Users can get their own higher-quota key at: https://scholar.x49.ai/docs?section=api-keys

Always construct API calls like this:

bash

SCHOLAR_KEY="${SCHOLAR_API_KEY:-psk_tLzPCmJdUw5oAHGeXL2H_fMrDdSyiF_SBJfn2p5uCO4}"
BASE="https://scholar.x49.ai/api/v1"

Endpoints

1. Paper Search: `POST /papers/search`

The primary search endpoint. Supports two modes via the same URL:

Standard (mode=standard): Exact keyword matching, larger result pool
Semantic (mode=semantic): Natural-language understanding, more relevant results

The two modes consume separate monthly quotas.

bash

SCHOLAR_KEY="${SCHOLAR_API_KEY:-psk_tLzPCmJdUw5oAHGeXL2H_fMrDdSyiF_SBJfn2p5uCO4}"
curl -s "https://scholar.x49.ai/api/v1/papers/search" \
  -H "Authorization: Bearer ${SCHOLAR_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "transformer attention mechanism",
    "mode": "standard",
    "limit": 10,
    "sort": "relevance",
    "filters": {}
  }'

Request body fields:

Field	Type	Required	Meaning
`query`	string	Conditionally	Natural-language query text. If omitted, provide at least one `author_ref` in filters
`mode`	string	No	`standard` (default) or `semantic`
`limit`	integer	No	Results per call, 1-100. Default varies
`sort`	string	No	`relevance`, `most_cited`, `newest`, or `oldest`

Filters object (nested under filters):

Filter	Type	Meaning
`year_from` / `year_to`	integer	Publication year range
`paper_types`	string[]	e.g. `["article", "review"]`
`venues`	string[]	Journal or conference names
`concepts`	string[]	Topic or concept labels
`institutions`	string[]	Institution names
`author_refs`	string[]	Public author references
`open_access`	boolean	`true` for open-access papers only

Key response fields:

data.items[] — Paper cards in this batch
data.items[].paper_ref — Stable public reference (use for batch/detail lookups)
data.items[].authors[].author_ref — Use for author detail and papers
data.facets — Aggregated filter values (years, concepts, venues, institutions, paper_types, venue_quality)
meta.total_results — Total matches under current filters
meta.limit — The result cap you requested

2. Search Suggestions: `GET /search/suggestions`

Get autocomplete suggestions and filter candidate values.

bash

curl -s "${BASE}/search/suggestions?query=transformer&type=all&limit=5" \
  -H "Authorization: Bearer ${KEY}"

Parameters (query string):

Parameter	Type	Required	Meaning
`query`	string	Yes	The typed prefix
`type`	string	No	`all` (default), `author`, `venue`, `concept`, `institution`, `paper_type`, `year`
`limit`	integer	No	Max suggestions to return

Response: Mixed or typed suggestion items with counts.

3. Paper Batch Lookup: `POST /papers/batch`

Expand one or more paper_ref values into full metadata.

bash

curl -s "${BASE}/papers/batch" \
  -H "Authorization: Bearer ${KEY}" \
  -H "Content-Type: application/json" \
  -d '{"paper_refs":["pap_ebcabae1be4244f3adba","pap_4748c234f5384d0eaee2"]}'

Request body:

Field	Type	Required	Meaning
`paper_refs`	string[]	Yes	Public paper references from search results

Response: Array of full paper cards, same structure as search items.

4. Author Search: `GET /authors/search`

Find authors by name.

bash

curl -s "${BASE}/authors/search?query=Hyeongwon+Kang&limit=5" \
  -H "Authorization: Bearer ${KEY}"

Parameters (query string):

Parameter	Type	Required	Meaning
`query`	string	Yes	Author name or keyword
`limit`	integer	No	Max results to return

Response: Author candidates with author_ref, name, last_known_institution, metrics (citation_count, paper_count), and identifiers (orcid).

5. Author Detail: `GET /authors/{author_ref}`

Show the full author profile.

bash

curl -s "${BASE}/authors/aut_d2610a19ad8c4b1ca298" \
  -H "Authorization: Bearer ${KEY}"

Response includes: name, affiliations (with country, type, years), aliases, identifiers (orcid), metrics (citation_count, h_index, i10_index, paper_count, two_year_mean_citedness), top_venues, topics (with paper_count and score).

6. Author Papers: `GET /authors/{author_ref}/papers`

List representative or recent papers for one author.

bash

curl -s "${BASE}/authors/aut_d2610a19ad8c4b1ca298/papers?limit=10" \
  -H "Authorization: Bearer ${KEY}"

Parameters (query string):

Parameter	Type	Required	Meaning
`limit`	integer	No	Number of papers to return

Response: Paper cards, same structure as search items. Default sort is most_cited.

Common Usage Patterns

Pattern 1: Quick topic search

User asks: "Find recent papers on CRISPR gene editing"

Call POST /papers/search with mode=standard, sort=newest, reasonable limit
Present results as a formatted list with title, authors, year, venue, citation count
Note the paper_ref values for any follow-up requests

Pattern 2: Semantic / discovery search

User asks: "How does transfer learning help in medical image analysis?"

Call POST /papers/search with mode=semantic - this mode understands natural-language queries
Present the most relevant results

Pattern 3: Author workflow

User asks: "Find papers by Yann LeCun"

Call GET /authors/search?query=Yann+LeCun to find the author
Pick the best match from results
Call GET /authors/{author_ref} for profile details
Call GET /authors/{author_ref}/papers for their publications

Pattern 4: Paper detail expansion

User asks: "Get more details on these papers" (after seeing search results)

Collect paper_ref values from prior results
Call POST /papers/batch with those refs
Present the full metadata

Pattern 5: Filtered search

User asks: "Find highly-cited open-access review papers on machine learning from Nature or Science published 2022-2025"

Build the search with appropriate filters:

json

{
  "query": "machine learning",
  "mode": "standard",
  "sort": "most_cited",
  "limit": 10,
  "filters": {
    "year_from": 2022,
    "year_to": 2025,
    "paper_types": ["review"],
    "venues": ["Nature", "Science"],
    "open_access": true
  }
}

Pattern 6: Suggestions for autocomplete

User asks: "What are popular topics related to CRISPR?"

Call GET /search/suggestions?query=CRISPR&type=concept&limit=10
Present the concept suggestions with counts

Presenting Results

When displaying search results to the user, format them clearly:

text

### [Paper Title](landing_page_url)
- **Authors**: Author1, Author2, Author3
- **Year**: 2024 | **Venue**: Journal Name (JCR Q1)
- **Citations**: 88 | **Type**: article | **Open Access**: No
- **paper_ref**: pap_xxxxx
> Abstract snippet (first 200 chars)...

For author results:

text

### [Author Name]
- **Institution**: University Name
- **Papers**: 42 | **Citations**: 1,230 | **h-index**: 15
- **ORCID**: 0000-xxxx-xxxx-xxxx
- **author_ref**: aut_xxxxx

Notes

The metrics.relevance field in search results is typically null - this is expected
Parse JSON responses with python3 -m json.tool for readable output when debugging
Use URL encoding for query parameters with special characters (spaces become + or %20)
All date/time fields use ISO 8601 format
The venue.quality_signals array contains JCR, FQBJCR, and CCF tier information when available

scholar-search-x49

Install

Scholar Search

When to Use This Skill

API Configuration

Endpoints

1. Paper Search: POST /papers/search

2. Search Suggestions: GET /search/suggestions

3. Paper Batch Lookup: POST /papers/batch

4. Author Search: GET /authors/search

5. Author Detail: GET /authors/{author_ref}

6. Author Papers: GET /authors/{author_ref}/papers

Common Usage Patterns

Pattern 1: Quick topic search

Pattern 2: Semantic / discovery search

Pattern 3: Author workflow

Pattern 4: Paper detail expansion

Pattern 5: Filtered search

Pattern 6: Suggestions for autocomplete

Presenting Results

Notes

Related skills

1. Paper Search: `POST /papers/search`

2. Search Suggestions: `GET /search/suggestions`

3. Paper Batch Lookup: `POST /papers/batch`

4. Author Search: `GET /authors/search`

5. Author Detail: `GET /authors/{author_ref}`

6. Author Papers: `GET /authors/{author_ref}/papers`