---
name: semanticscholar-skill
description: Use when searching academic papers, looking up citations, finding authors, or getting paper recommendations using the Semantic Scholar API. Triggers on queries about research papers, academic search, citation analysis, or literature discovery.
license: MIT
homepage: https://github.com/Agents365-ai/semanticscholar-skill
compatibility: Requires python3 and the `requests` package. Set S2_API_KEY for higher rate limits (request at https://www.semanticscholar.org/product/api#api-key). Works unauthenticated with strict rate limits.
platforms: [macos, linux, windows]
metadata: {"openclaw":{"requires":{"bins":["python3"],"env":["S2_API_KEY"]},"emoji":"📚"},"hermes":{"tags":["semantic-scholar","academic","paper-search","citation","literature","research"],"category":"research","requires_tools":["python3"],"related_skills":["asta-skill","zotero-research-assistant","literature-review","paper-reader"]},"author":"Agents365-ai","version":"0.3.0"}
---

# Semantic Scholar Search Workflow

Search academic papers via the Semantic Scholar API using a structured 4-phase workflow.

**Critical rule:** NEVER make multiple sequential Bash calls for API requests. Always write ONE Python script that runs all searches, then execute it once. All rate limiting is handled inside `s2.py` automatically.

## Phase 1: Understand & Plan

Parse the user's intent and choose a search strategy:

### Decision Tree

| User wants... | Strategy | Function |
|---------------|----------|----------|
| Broad topic exploration | Relevance search | `search_relevance()` |
| Precise technical terms, exact phrases | Bulk search with boolean operators | `search_bulk()` with `build_bool_query()` |
| Specific passages or methods | Snippet search | `search_snippets()` |
| Known paper by title | Title match | `match_title()` |
| Known paper by DOI/PMID/ArXiv | Direct lookup | `get_paper()` |
| Papers citing a known work | Citation traversal | `get_citations()` |
| Related to one paper | Single-seed recommendations | `find_similar()` |
| Related to multiple papers | Multi-seed recommendations | `recommend()` |
| Find a researcher | Author search | `search_authors()` |
| Researcher's profile | Author details | `get_author()` |
| Researcher's publications | Author papers | `get_author_papers()` |

### Query Construction Rules

- **Ambiguous terms** (e.g., "stem cells" could mean mesenchymal or stem-like T cells): Use `build_bool_query()` with exact phrases and exclusions
  - Example: `build_bool_query(phrases=["stem-like T cells"], required=["CD4", "TCF7"], excluded=["mesenchymal", "hematopoietic stem cell"])`
- **Multi-context queries** (e.g., "topic X in cancer AND autoimmunity"): Plan separate searches, deduplicate with `deduplicate()`
- **Broad topics**: Use `search_relevance()` with filters (year, venue, fieldsOfStudy, minCitationCount)

### Plan Filters

| Filter | Use when |
|--------|----------|
| `year="2020-"` | Recent work only |
| `publication_date="2024-01-01:2024-06-30"` | Precise date range (YYYY-MM-DD) |
| `fields_of_study="Medicine"` | Restrict to domain |
| `min_citations=10` | Only established papers |
| `pub_types="Review"` | Find reviews/meta-analyses |
| `pub_types="ClinicalTrial"` | Clinical trials only |
| `open_access=True` | Only open access papers |

**Checkpoint:** Before proceeding, verify: (1) search strategy matches user intent, (2) filters are appropriate, (3) query is specific enough to avoid irrelevant results.

## Phase 2: Execute Search

Write ONE Python script. Example:

```python
import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

# Build precise query
q = build_bool_query(
    phrases=["stem-like T cells"],
    required=["CD4", "IBD"],
    excluded=["mesenchymal"]
)
papers = search_bulk(q, max_results=30, year="2018-", fields_of_study="Medicine")
papers = deduplicate(papers)

print(format_results(papers, "Stem-like CD4 T cells in IBD"))
```

Execute with: `python3 /tmp/s2_search.py`

**Rules:**
- Import everything from s2: `from s2 import *`
- Write script to `/tmp/s2_search.py` (or similar temp path)
- One Bash call to execute. Never chain multiple API calls via separate Bash invocations.
- Rate limiting, retries, and backoff are automatic inside s2.py

**Checkpoint:** Verify the script ran successfully (no exceptions) and returned results. If 0 results, broaden the query or relax filters before presenting.

### Worked Examples

**Example 1: Author workflow** — "Find papers by Yann LeCun on self-supervised learning"

```python
import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

authors = search_authors("Yann LeCun", max_results=5)
print(format_authors(authors))

# Use the first match's ID to get their papers
author_id = authors[0]["authorId"]
papers = get_author_papers(author_id, max_results=50)
# Filter locally for topic
ssl_papers = [p for p in papers if "self-supervised" in (p.get("title") or "").lower()]
print(format_results(ssl_papers, "Yann LeCun - Self-Supervised Learning"))
```

**Example 2: Citation chain** — "Who cited the Transformer paper and what did they build on?"

```python
import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

paper = get_paper("DOI:10.48550/arXiv.1706.03762")
print(f"Title: {paper['title']}, Citations: {paper['citationCount']}")

# Get top-cited papers that cite this one
citing = get_citations(paper["paperId"], max_results=50)
citing_papers = [c["citingPaper"] for c in citing if c.get("citingPaper")]
citing_papers.sort(key=lambda p: p.get("citationCount", 0), reverse=True)
print(format_results(citing_papers, "Most-cited papers citing Attention Is All You Need"))
```

**Example 3: Multi-seed recommendations with BibTeX export** — "Find papers like these two but not about NLP"

```python
import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

recs = recommend(
    positive_ids=["DOI:10.1038/nature14539", "ARXIV:2010.11929"],
    negative_ids=["ARXIV:1706.03762"],
    limit=20
)
print(format_results(recs, "Vision papers like Deep Learning & ViT, excluding NLP"))

# Export BibTeX for top results
bib_data = batch_papers([r["paperId"] for r in recs[:10]], fields="title,citationStyles")
print(export_bibtex(bib_data))
```

## Phase 3: Summarize & Present

- Use `format_results()` for consistent output (summary table + top-10 details)
- If user's language is Chinese, present summaries in Chinese
- Always note total results count and search strategy used
- Highlight most relevant papers based on the user's specific question

## Phase 4: User Interaction Loop

After presenting results, **always offer these options:**

1. **Translate** — titles/summaries to Chinese (or other language)
2. **Details** — full abstract for specific paper numbers
3. **Refine** — narrow or expand search with different terms/filters
4. **Similar** — find papers similar to a specific result (`find_similar()`)
5. **Citations** — who cited a specific paper (`get_citations()`)
6. **Export** — save results via `export_bibtex()`, `export_markdown()`, or `export_json()`
7. **Done** — end search session

Loop until user says done. Each follow-up uses the same single-script pattern.

---

## API Quick Reference

### Helper Module (`s2.py`)

```python
import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *
```

### Paper Search Functions

| Function | Purpose | Max Results |
|----------|---------|-------------|
| `search_relevance(query, **filters)` | Simple broad search | 1,000 |
| `search_bulk(query, sort=..., **filters)` | Boolean precise search | 10,000,000 |
| `search_snippets(query, **filters)` | Full-text passage search | 1,000 |
| `match_title(title)` | Exact title match | 1 |
| `get_paper(paper_id)` | Single paper details | — |
| `get_citations(paper_id, max_results)` | Who cited this | 10,000 |
| `get_references(paper_id, max_results)` | What this cites | 10,000 |
| `find_similar(paper_id, limit, pool)` | Single-seed recommendations | 500 |
| `recommend(positive_ids, negative_ids, limit)` | Multi-seed recommendations | 500 |
| `batch_papers(ids, fields)` | Batch lookup (≤500) | — |

### Author Functions

| Function | Purpose | Max Results |
|----------|---------|-------------|
| `search_authors(query, max_results)` | Find researchers by name | 1,000 |
| `get_author(author_id)` | Author profile (affiliations, h-index) | — |
| `get_author_papers(author_id, max_results)` | Author's publications | 10,000 |
| `get_paper_authors(paper_id, max_results)` | Paper's author list | 1,000 |
| `batch_authors(ids, fields)` | Batch author lookup (≤1000) | — |

### Filter Parameters (kwargs)

`year`, `publication_date`, `venue`, `fields_of_study`, `min_citations`, `pub_types`, `open_access`

- `year`: `"2020-"`, `"-2019"`, `"2016-2020"`
- `publication_date`: `"2024-01-01:2024-06-30"` (YYYY-MM-DD range, open-ended OK)
- `pub_types`: `Review`, `JournalArticle`, `Conference`, `ClinicalTrial`, `MetaAnalysis`, `Dataset`, `Book`, `CaseReport`, `Editorial`, `LettersAndComments`, `News`, `Study`, `BookSection`

### Boolean Query Syntax (bulk search only)

| Syntax | Example | Meaning |
|--------|---------|---------|
| `"..."` | `"deep learning"` | Exact phrase |
| `+` | `+transformer` | Must include |
| `-` | `-survey` | Exclude |
| `\|` | `CNN \| RNN` | OR |
| `*` | `neuro*` | Prefix wildcard |
| `()` | `(CNN \| RNN) +attention` | Grouping |

Use `build_bool_query(phrases, required, excluded, or_terms)` to construct safely.

### Output Functions

| Function | Purpose |
|----------|---------|
| `format_table(papers, max_rows=30)` | Markdown summary table |
| `format_details(papers, max_papers=10)` | Detailed entries with TLDR/abstract |
| `format_results(papers, query_desc)` | Combined: summary + table + details |
| `format_authors(authors, max_rows=20)` | Author table (name, affiliations, h-index) |
| `export_bibtex(papers)` | BibTeX entries (requires `citationStyles` field) |
| `export_markdown(papers, query_desc)` | Full markdown report saved to file |
| `export_json(papers, path)` | JSON export saved to file |
| `deduplicate(papers)` | Remove duplicates by paperId |

### Supported ID Formats

`DOI:10.1038/...`, `ARXIV:2106.15928`, `PMID:19872477`, `PMCID:PMC2323569`, `CorpusId:215416146`, `ACL:2020.acl-main.447`, `DBLP:conf/acl/...`, `MAG:3015453090`, `URL:https://...`

### Paper Fields

Default: `title,year,citationCount,authors,venue,externalIds,tldr`

Additional: `abstract`, `references`, `citations`, `openAccessPdf`, `publicationDate`, `publicationVenue`, `fieldsOfStudy`, `s2FieldsOfStudy`, `journal`, `isOpenAccess`, `referenceCount`, `influentialCitationCount`, `citationStyles`, `embedding`, `textAvailability`

Author fields: `name`, `affiliations`, `paperCount`, `citationCount`, `hIndex`, `homepage`, `externalIds`, `papers`

### Rate Limiting

Handled automatically by `s2.py`: 1.1s gap between requests, exponential backoff (2s→4s→8s→16s→32s, max 60s) on 429/504 errors, up to 5 retries.

### Troubleshooting

| Error | Cause | Fix |
|-------|-------|-----|
| `HTTPError 403` | Missing or invalid API key | Verify `S2_API_KEY` is set: `echo $S2_API_KEY` |
| `HTTPError 429` after 5 retries | Sustained rate limit exceeded | Wait 60s, reduce `max_results`, or split into smaller batches |
| `ModuleNotFoundError: s2` | Skill directory not on path | Verify skill is installed at `~/.claude/skills/` or `~/.openclaw/skills/` |
| `ModuleNotFoundError: requests` | `requests` not installed | `pip install requests` or `uv pip install requests` |
| 0 results returned | Query too specific or filters too narrow | Broaden query, remove filters, try `search_relevance()` instead of `search_bulk()` |
| `KeyError: 'data'` | Endpoint returned error object | Check `r.get("message")` for API error details |
| `tldr` field is empty | Not all papers have TLDR | Fall back to `abstract` field; bulk search never returns `tldr` |


---
Source attribution: GeorgeDoors888
Original project: https://github.com/GeorgeDoors888/GB-Power-Market-JJ/blob/main/openclaw-skills/skills/agents365-ai/semanticscholar-skill/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: GeorgeDoors888
Original project: https://github.com/GeorgeDoors888/GB-Power-Market-JJ/blob/main/openclaw-skills/skills/agents365-ai/semanticscholar-skill/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Product Management
Roles: Academic Writer, Data Scientist, Market Research Analyst, Product Manager, Researcher
Specialties: API
Tools: Semantic Scholar Api
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 3e626688b4b0e9f98f428f7e0f031f778f3f505f6dbf344b450f68f6883cb73b