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FrontierScholar

FrontierScholar 知沿学术

A research wiki of about 1.01M computer-science arXiv papers, built for coding agents. Papers are organised into domains, topics and subtopics; each subtopic has pages on its research problems, method families, datasets and benchmarks, and a frontier page of open gaps and rising approaches. Every claim points back to arXiv ids.

This repository has the fs command-line client and the fs-cli agent skill. Website and API documentation: https://frontier-scholar.com.

Install

pip install frontier-scholar      # Python 3.8+, no dependencies
fs --version                      # CLI and API versions, data snapshot

fs talks to the public endpoint https://frontier-scholar.com and needs no key (see Limits).

Agent skill

skills/fs-cli/ tells a coding agent (Claude Code, Codex, Cursor, Gemini CLI, pi, ...) when and how to use fs:

npx skills add FrontierScholar/FrontierScholar
# or
gh skill install FrontierScholar/FrontierScholar

or copy skills/fs-cli/ into your agent's skills folder. Then ask in plain language, e.g. "Use fs: what are the open problems of adaptive retrieval, and which approaches are rising?"

Commands

Command What it does
fs select "QUERY" The next level of the tree (domains, topics, subtopics), ranked by a query
fs search paper "QUERY" Papers ranked by title, abstract and dataset names; --before / --after dates
fs search wiki [index|pages|frontier|bench] "QUERY" Subtopic overviews, problem / method / resource pages, frontier gaps and trends, benchmarks
fs read TARGET... A paper (original abstract, date, subtopics), a paper's pages, wiki pages, folders, a subtopic's papers
fs related ID... Papers the wiki groups with the given ones
fs meta [wiki|dates] ID... Titles and dates, subtopics, first-version date checks
fs select "adaptive retrieval" --topk 3
fs search paper "adaptive retrieval" --before 2025-01-01
fs search wiki frontier "adaptive retrieval" --status open
fs read 2601.04618
fs meta dates 2601.04618 2604.15621 --before 2026-06-01

Answers are JSON (wiki pages print as Markdown). They are lookups and keyword (BM25) rankings over one fixed data snapshot: nothing is generated. Wiki pages are summaries written from the papers; the only original text is a paper's abstract. fs COMMAND -h lists every option and output field.

From Python:

from frontier_scholar import Client

r = Client().call("search/paper", {"query": "adaptive retrieval", "topk": 5})
for p in r["items"]:
    print(p["paper_id"], p["date"], p["title"])

The HTTP API is POST https://frontier-scholar.com/api/v1/fs/<command> with the arguments as JSON; see the documentation.

Limits

Without a key, each IP gets 10 requests a minute and 300 a day, and calls are smaller (--topk up to 20, read up to 5 targets, meta up to 50 ids, no --grep); curl https://frontier-scholar.com/api/v1/fs/quota shows what is left. With a key from the FrontierScholar team, set FS_API_KEY (in the environment, never in code); calls then use that account's limits. FS_BASE points fs at another service.

Layout

src/frontier_scholar/   the fs CLI and its client (standard library only)
skills/fs-cli/          the agent skill: SKILL.md, references/, CHANGELOG.md
tests/                  CLI tests

The earlier frontier-scholar SDK and skills are on the legacy-sdk branch.

License

Apache-2.0, see LICENSE.

Metadata

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