academic-research
Lingtai-AI/lingtai · Agent Skill
Akademik makaleleri arar ve indirir, alıntıları analiz eder ve LaTeX el yazmalarını yazar.
Kuruluma geç ↓Genel bakış
| Yayıncı | |
|---|---|
| Depo | Lingtai-AI/lingtai ↗ |
| Skill klasörü | academic-research |
| Yıldız | 698 |
| Dosyalar | 25 |
| Lisans | Apache-2.0 |
| Son güncelleme | 14 Ağustos 2026 |
| Bağlantılar | Skill kaynağı ↗ · Depo ↗ |
Mozgi kontrolü
Skill dosyalarında bulduklarımız. Ayrıntılar abonelikle.
Kurulum
Skill, SKILL.md dosyası içeren bir klasördür. Ajanınızı seçin ve komutları proje kökünde bir terminalde çalıştırın (macOS, Linux veya Windows'ta Git Bash).
Promptu Claude, Codex veya başka bir asistana yapıştırın: skill'i inceleyip sizin için kuracaktır.
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
Claude Code'u yeniden başlatın — skill listede görünür; elle çağırmak için /academic-research. Tüm projeler için ~/.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 skill'leri geçerli klasörden depo köküne kadar .agents/skills içinde arar; /skills veya $academic-research ile çağırın. Tüm projeler için ~/.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 skill'i açılışta yükler; Agent sohbetinde “/” ile çağırın. Cursor .claude/skills ve .agents/skills klasörlerini de okur. Tüm projeler için ~/.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
Gemini CLI, görev uyduğunda skill'i etkinleştirmeyi önerir — siz onaylarsınız. Tüm projeler için ~/.gemini/skills.
Kurulumdan önce SKILL.md ve eşlik eden dosyaları inceleyin. Önizleme için dosyaya tıklayın.
SKILL.md içeriği
Yazarın orijinal metni · salt okunurAcademic 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.
Sık sorulan sorular
academic-research skill'i ne yapar?
Akademik makaleleri arar ve indirir, alıntıları analiz eder ve LaTeX el yazmalarını yazar.
academic-research nasıl kurulur?
Bu sayfadaki “Kurulum” bölümünü açın ve ajanınızı seçin — Claude Code, Codex, Cursor veya Gemini CLI: skill klasörünü doğru yere kopyalayan hazır komutlar orada. Ya da “Asistanla” sekmesindeki promptu kopyalayın; asistan skill'i inceleyip kuracaktır.
academic-research hangi yapay zekâlarla çalışır?
Skill'ler düz Markdown talimatlarıdır (Agent Skills formatı), bu yüzden academic-research SKILL.md okuyan her ajanla çalışır: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode ve diğerleri. Model talimatları yalnızca görev skill açıklamasına uyduğunda yükler.
academic-research ücretsiz mi?
Skill, Lingtai-AI tarafından açık olarak yayımlandı (Apache-2.0 lisansı). Skill'in kendisi ücretsizdir — yalnızca kullandığınız token'lar için yapay zekâ sağlayıcınıza ödeme yaparsınız.
Skill kurmak güvenli mi?
Skill, ajanınızın izleyeceği talimatlardır; kurmadan önce SKILL.md ve eşlik eden dosyaları okuyun. Skill klasöründeki betikler yalnızca ajanınız çalıştırırsa çalışır — ne yaptığını izleyin ve gereksiz izin vermeyin.
Skill, MCP sunucusu veya eklentiden nasıl farklıdır?
MCP sunucusu veya eklenti modele yeni araçlar verir — çalışıp sonuç döndüren kod. Skill ise bilgi ve süreç verir: göreve nasıl yaklaşılacağı, hangi adımların izleneceği ve iyi sonucun neye benzediği. Sunucu veya API anahtarı gerekmez.