Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/semantic-scholarWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/raja21068/autoresearch/semantic-scholar)<a href="https://agentmods.dev/skills/raja21068/autoresearch/semantic-scholar"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/semantic-scholar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/semantic-scholar"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/semantic-scholar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.02797 |
| Opus 5 | $0.00040 | $0.01399 |
| Sonnet 5 | $0.00016 | $0.00559 |
| Haiku 4.5 | $0.00008 | $0.00280 |
Grade B, and why
semantic-scholar scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
[ -z "$SCRIPT" ] && SCRIPT=$(find ~/.claude/skills/semantic-scholar/ -name "semantic_scholar_fetch.py" 2>/dev/null | head -1) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
If `semantic_scholar_fetch.py` is not found, fall back to inline Python using `urllib` against `https://api.semanticscholar.org/graph/v1/paper/search`. How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Scholar Paper Search
Search topic or paper ID: $ARGUMENTS
Role & Positioning
This skill is the published venue counterpart to /arxiv:
| Skill | Source | Best for |
|---|---|---|
/arxiv |
arXiv API | Latest preprints, cutting-edge unrefereed work |
/semantic-scholar |
Semantic Scholar API | Published journal/conference papers (IEEE, ACM, Springer, etc.) with citation counts, venue info, TLDR |
Do NOT duplicate arXiv's job. If results contain an externalIds.ArXiv field, the paper is also on arXiv — note this but do not re-fetch from arXiv.
Constants
- MAX_RESULTS = 10 — Default number of search results.
- FETCH_SCRIPT —
tools/semantic_scholar_fetch.pyrelative to the project root. Fall back to inline Python if not found. - DEFAULT_FILTERS — For general research queries, apply these by default to reduce noise:
--fields-of-study "Computer Science,Engineering"--publication-types JournalArticle,Conference
Overrides (append to arguments):
/semantic-scholar "topic" - max: 20— return up to 20 results/semantic-scholar "topic" - type: journal— only journal articles/semantic-scholar "topic" - type: conference— only conference papers/semantic-scholar "topic" - min-citations: 50— only highly-cited papers/semantic-scholar "topic" - year: 2022-— papers from 2022 onward/semantic-scholar "topic" - fields: all— remove default field-of-study filter/semantic-scholar "topic" - sort: citations— bulk search sorted by citation count/semantic-scholar "DOI:10.1109/..."— fetch a single paper by DOI
Workflow
Step 1: Parse Arguments
Parse $ARGUMENTS for directives:
- Query or ID: main search term, or a paper identifier:
- DOI:
10.1109/TWC.2024.1234567 - Semantic Scholar ID:
f9314fd99be5f2b1b3efcfab87197d578160d553 - ArXiv:
ARXIV:2006.10685 - Corpus:
CorpusId:219792180
- DOI:
- max: N: override MAX_RESULTS- type: journal|conference|review|all: map to--publication-types- min-citations: N: map to--min-citations- year: RANGE: map to--year(e.g.2022-,2020-2024)- fields: FIELDS: override--fields-of-study(useallto remove filter)- sort: citations|date: usesearch-bulkwith--sort citationCount:descorpublicationDate:desc
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 220 lines · 80 tokens per session scan B e10788c04c95
semantic-scholar is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 80 tokens to every session and 2,797 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (enumerates other installed skills, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…