academic-research
Lingtai-AI/lingtai · Agent Skill
搜尋和下載學術論文,分析引用並撰寫 LaTeX 手稿。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | Lingtai-AI/lingtai ↗ |
| 技能資料夾 | academic-research |
| 星 | 698 |
| 附帶檔案 | 25 |
| 授權 | Apache-2.0 |
| 最近更新 | 2026年8月14日 |
| 連結 | 技能原始碼 ↗ · 儲存庫 ↗ |
Mozgi 檢查
我們在技能檔案中發現的內容。詳細資訊需訂閱。
安裝
技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。
把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。
Install the “academic-research” skill from mozgi.io (https://mozgi.io/skills/academic-research/) for me. 1. Download the archive: https://mozgi.io/skills/academic-research/download If it fails, take the skill folder from the source: https://github.com/Lingtai-AI/lingtai/tree/main/tui/internal/preset/skills/swiss-knife/reference/academic-research 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “academic-research” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/academic-research/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/Lingtai-AI/lingtai.git /tmp/lingtai mkdir -p .claude/skills cp -r /tmp/lingtai/tui/internal/preset/skills/swiss-knife/reference/academic-research .claude/skills/academic-research
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/academic-research。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/Lingtai-AI/lingtai.git /tmp/lingtai mkdir -p .agents/skills cp -r /tmp/lingtai/tui/internal/preset/skills/swiss-knife/reference/academic-research .agents/skills/academic-research
Codex 會從目前資料夾往上到儲存庫根目錄,在 .agents/skills 中尋找技能;用 /skills 或 $academic-research 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/Lingtai-AI/lingtai.git /tmp/lingtai mkdir -p .cursor/skills cp -r /tmp/lingtai/tui/internal/preset/skills/swiss-knife/reference/academic-research .cursor/skills/academic-research
Cursor 啟動時會載入技能;在 Agent 聊天輸入「/」呼叫。Cursor 也會讀取 .claude/skills 與 .agents/skills。所有專案:~/.cursor/skills。
git clone --depth 1 --branch main https://github.com/Lingtai-AI/lingtai.git /tmp/lingtai mkdir -p .gemini/skills cp -r /tmp/lingtai/tui/internal/preset/skills/swiss-knife/reference/academic-research .gemini/skills/academic-research
任務符合時,Gemini CLI 會提議啟用技能,由你確認。所有專案:~/.gemini/skills。
安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。
SKILL.md 內容
作者原文 · 唯讀Academic Research
> Nested swiss-knife reference. A modular skill: try the bundled script
> first, then load specific reference files only when you need to escape it.
Try this first
For 80% of "get me this paper" requests, the bundled script is the right answer.
It walks the open-access ladder, falls back automatically, and writes a manifest
the next session can resume from.
# Fetch by any identifier
python3 <skill-path>/scripts/fetch_paper.py 10.1103/PhysRevLett.125.015001
python3 <skill-path>/scripts/fetch_paper.py arXiv:2301.00001
python3 <skill-path>/scripts/fetch_paper.py PMID:12345678
# Batch (one identifier per line in the file)
python3 <skill-path>/scripts/fetch_paper.py --batch dois.txt --out papers/
# Resolve metadata only (no PDF download)
python3 <skill-path>/scripts/fetch_paper.py 10.1038/nature12373 --dry-run
# Skip LibGen (e.g. legal-sensitive environment)
python3 <skill-path>/scripts/fetch_paper.py <id> --no-libgen
Output layout (idempotent — re-runs skip entries with status: ok):
papers/{first-author-year-firstword}/
├── paper.pdf | paper.md # full-text artifact
├── metadata.json # CrossRef-normalized
└── manifest.json # {status, tier, source, ts, doi}
Tier ladder (script stops at first hit):
| Tier | Source | Best for |
|------|--------|----------|
| 1 | arXiv direct | Preprints (physics, CS, math, q-bio, econ) |
| 2 | Unpaywall | Publisher-blessed gold/green OA |
| 3 | Europe PMC | Biomedical full-text + PMC mirror |
| 4 | CORE | Institutional repositories (needs $CORE_API_KEY) |
| 5 | Publisher-page extract | Nature/APS/AIP/IOP/Cambridge → in-house extractor (stdlib + requests, no third-party deps). Fetches the already-accessible official article / DOI landing page and parses citation_* metadata + the article body into structured Markdown. No paywall/CAPTCHA bypass, no cookies/credentials — official pages only. A login/paywall page is a clean miss; the ladder then falls through. Opt out with --no-publisher-extract. |
| 5b | Authorized publisher | Licensed/institutional access only — official DOI landing page → same-host publisher PDF → %PDF- validation, full provenance. Recovers paywalled-but-subscribed papers without shadow libraries. Never bypasses paywalls or handles credentials. Opt out with --no-institutional. See authorized-publisher-access.md ↗. |
| 6 | LibGen | Last resort; opt out with --no-libgen |
Set $LINGTAI_RESEARCH_EMAIL to a real address before first use — Unpaywall
rejects placeholder emails with HTTP 422. The default falls back to
[email protected] with a warning.
Read on only if: the script fails on your paper, you need a custom query shape,
or you're composing a multi-step workflow (search → fetch → cite → write).
Before drafting citation-bearing academic writing
> Verify sources before you write prose. If you are about to draft a paper,
> related-work section, literature review, References list, or any author–year /
> citation-bearing manuscript, pass the evidence-verification gate first:
> reference/evidence-verification-gate.md ↗.
> No verified evidence → no confident prose. Peer-reviewed and preprint sources
> must not share the same evidence layer; search results are leads, not citations.
> If the user asks to "go fast," reduce *scope*, not *verification*. Produce a
> verified literature matrix before any submission-like draft.
Escape hatch — quick paths
I'm about to write a paper / references / related-work section → reference/evidence-verification-gate.md (verify FIRST)
The user wants a "fast" / "readable" paper draft → reference/evidence-verification-gate.md (reduce scope, not verification)
I have a DOI → reference/api-doi-resolver.md → api-crossref.md
I have an arXiv ID → reference/api-arxiv.md (direct PDF link)
I have a PMID → reference/api-europe-pmc.md
I have a bibcode → reference/api-nasa-ads.md (requires free key)
I only have keywords → reference/decision-tree.md → pick API by discipline
I need a citation network → reference/api-semantic-scholar.md (single paper) or api-semantic-scholar-batch.md (large corpus) or api-openalex.md
I need to override the PDF ladder → reference/pipeline-obtain-pdf.md
Tier-5 publisher-extract failed and I want to retry it manually → reference/publisher-page-extraction.md
OA + authorized-publisher failed and I have a batch + the user's Zotero → reference/zotero-institutional-fulltext-handoff.md
All OA chains failed → reference/libgen-fallback.md (last resort)
I need astrophysics → reference/api-nasa-ads.md
I need high-energy physics → reference/api-inspire-hep.md
I need biomedical → reference/api-europe-pmc.md or api-pubmed.md
I need to write/compile a paper → reference/pipeline-latex-writing.md
My empirical draft keeps getting reframed / reviewers "agree" → reference/anti-pattern-text-consistency-vs-data-correspondence.md
I hit an API error → reference/error-handling.md
Reference index
API references (13)
Each card includes endpoint parameters, runnable code, response shape, rate limits, and fallbacks.
| API | File | Best for | Key? |
|-----|------|----------|------|
| arXiv | api-arxiv.md ↗ | Preprint retrieval | No |
| CrossRef | api-crossref.md ↗ | DOI metadata, funder queries | No (mailto recommended) |
| DOI Resolver | api-doi-resolver.md ↗ | Batch DOI → structured citation | No |
| OpenAlex | api-openalex.md ↗ | Discovery, institution/concept analysis | No |
| Semantic Scholar | api-semantic-scholar.md ↗ | Citation networks, TLDR | No (tight limits) |
| Semantic Scholar Batch | api-semantic-scholar-batch.md ↗ | Citation-graph construction from large ID lists | No (tight limits) |
| CORE | api-core.md ↗ | OA full-text downloads | Optional (recommended) |
| PubMed | api-pubmed.md ↗ | Biomedical search, PMC full text | No |
| Unpaywall | api-unpaywall.md ↗ | OA versions / PDFs | email (real) |
| Google Scholar | api-google-scholar.md ↗ | Broadest discipline coverage | No (needs stealth) |
| Europe PMC | api-europe-pmc.md ↗ | Biomed, PMID, full-text XML | No |
| NASA ADS | api-nasa-ads.md ↗ | Astrophysics, BibTeX export | Yes (free) |
| INSPIRE-HEP | api-inspire-hep.md ↗ | High-energy physics | No |
Pipeline workflows (6)
| Pipeline | File | Purpose |
|----------|------|---------|
| Paper discovery | pipeline-discovery.md ↗ | Keywords → candidate papers |
| PDF acquisition | pipeline-obtain-pdf.md ↗ | Metadata → full text (manual ladder) |
| Citation tracking | pipeline-citation-tracking.md ↗ | Forward/backward citation networks |
| Scholar analysis | pipeline-scholar-analysis.md ↗ | Impact, trends, h-index |
| LaTeX writing | pipeline-latex-writing.md ↗ | Compile, bibliography, figures, debug |
| Decision tree | decision-tree.md ↗ | "I have X — which API should I use?" |
Standalone references
- evidence-verification-gate.md ↗ — verify-before-drafting gate for citation-bearing academic writing: detection triggers, the verified-literature-matrix schema, A/B/C evidence tiers, "go fast = reduce scope not verification," matrix-before-draft artifact convention, pre-draft lint
- publisher-page-extraction.md ↗ — Tier-5 manual escape hatch (Nature/APS/AIP/IOP/Cambridge → structured Markdown)
- authorized-publisher-access.md ↗ — Tier-5b automated probe: official DOI landing page → same-host publisher PDF, licensed access only, no paywall/credential handling
- zotero-institutional-fulltext-handoff.md ↗ — Tier-6a human-in-the-loop: stage a failed batch into Zotero with tags, the human runs Find Full Text (institutional access), agent harvests PDFs back with provenance. No UI automation / TCC bypass / credential handling.
- libgen-fallback.md ↗ — Last-resort PDF source with legal/safety notes
- error-handling.md ↗ — 429 backoff, 403 publisher blocks, timeout patterns
- anti-pattern-text-consistency-vs-data-correspondence.md ↗ — empirical-writing failure mode: prose drifts from the data while reviewer rounds make it more polished. Trigger pattern, re-anchoring steps, detection checklist.
Relationship to built-in web search
web-search-manual(returned byweb_search(action="manual")) is the routing layer ("which tier to use for this URL?")- academic-research is the deep-dive layer ("how do I write OpenAlex filter parameters? what email does Unpaywall want?")
- The two are complementary. If you're just scraping one publisher page and don't need the OA ladder, the built-in manual's
scripts/extract_page.pyis lighter.
Known caveats
- Unpaywall's
emailparameter is required and must be real — placeholder addresses get HTTP 422. Set$LINGTAI_RESEARCH_EMAILonce. - CORE without an API key is harshly rate-limited (~100/day vs 10,000/day with a free key from https://core.ac.uk/services/api).
- Semantic Scholar free tier is very tight (~100 req/day/IP, ~1 req/s practical). For large-corpus citation graphs use the batch endpoint — api-semantic-scholar-batch.md ↗. Request a key for any serious citation-network work.
- Google Scholar requires a stealth browser (camoufox or playwright-stealth v2); legacy
playwright_stealthAPI does not work. - arXiv enforces HTTPS — HTTP requests are 301-redirected automatically.
- Library Genesis legality varies by jurisdiction — use is the user's responsibility. Pass
--no-libgento opt out. - Publisher-page extraction (Tier 5) — in-house stdlib+
requestsextractor (nothing to install; replaced the brokenzhiping0913/Download_paper, issue #136). Fetches the already-accessible official landing page → structured Markdown. No paywall/CAPTCHA bypass, no cookie/credential handling — official pages only; login/paywall/unsupported-prefix/too-short is a clean miss. Heuristic (equations/figures may be lost).--no-publisher-extractto skip. Full contract: reference/publisher-page-extraction.md ↗. - Authorized-publisher tier (5b) — uses only access you already have: official DOI landing → same-host PDF,
%PDF-+Content-Type validated. Never bypasses paywalls/CAPTCHAs, never reads/stores/replays cookies/credentials (cookies/auth never written to provenance). Most institutional access is IP-based, so a plain GET works on a licensed network and harmlessly misses off it.--no-institutionalto disable. Full contract: reference/authorized-publisher-access.md ↗. - Zotero institutional full-text (Tier 6a) is a human-in-the-loop handoff, not an agent tool — for a batch where OA+5b both missed and the user runs Zotero Desktop on an institutional network: the agent stages rows via
/connector/saveItemswith a dated tag, the human clicks Find Full Text, the agent harvests PDFs back with provenance (copy-not-move,resolved_by: human Find Full Text). Never drive the UI via AppleScript/Accessibility (macOS TCC-1719/-1743, out of policy); no paywall bypass, no credential handling, no auto-LibGen. Full contract: reference/zotero-institutional-fulltext-handoff.md ↗. - Drafting citation-bearing academic writing without verifying sources first — under "fast"/"readable draft" pressure the model flattens DOI-verified papers, preprints, and search leads into one confident evidence layer, which reads as submission-ready and invites reviewer rejection. Pass the evidence-verification gate ↗ before drafting: verify a literature matrix first, keep A/B/C reliability tiers separate in the prose, and reduce scope (not verification) when speed is requested.
- Writing an empirical paper iteratively can drift from the data — reviewer rounds make prose more polished and internally consistent without verifying it still matches the experiments on disk. Reviewer agreement is text-consistency evidence, not data-correspondence evidence. Anchor every claim to data files/runner code *before* writing, and re-derive (don't just rewrite) when feedback flags confusion. See reference/anti-pattern-text-consistency-vs-data-correspondence.md ↗.
---
> Found a bug or issue? If you encounter any problems with this skill, load the lingtai-issue-report skill and follow its instructions to report it.
常見問題
academic-research 技能有什麼用途?
搜尋和下載學術論文,分析引用並撰寫 LaTeX 手稿。
如何安裝 academic-research?
打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。
academic-research 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 academic-research 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。
academic-research 是免費的嗎?
此技能由 Lingtai-AI 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。
安裝技能安全嗎?
技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。
技能和 MCP 伺服器或外掛有何不同?
MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。