academic-research
Lingtai-AI/lingtai · Agent Skill
Cerca e scarica articoli accademici, analizza citazioni e scrive manoscritti LaTeX.
Vai all'installazione ↓Panoramica
| Autore | |
|---|---|
| Repository | Lingtai-AI/lingtai ↗ |
| Cartella dello skill | academic-research |
| Stelle | 698 |
| File inclusi | 25 |
| Licenza | Apache-2.0 |
| Aggiornato | 14 agosto 2026 |
| Link | Sorgente dello skill ↗ · Repository ↗ |
Verifica Mozgi
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Installazione
Uno skill è una cartella con un file SKILL.md. Scegli il tuo agente ed esegui i comandi in un terminale (macOS, Linux o Git Bash su Windows) dalla radice del progetto.
Incolla il prompt in Claude, Codex o un altro assistente: controllerà lo skill e lo installerà.
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
Riavvia Claude Code: lo skill compare nell'elenco; per richiamarlo a mano usa /academic-research. Per tutti i progetti: ~/.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 cerca gli skill in .agents/skills dalla cartella corrente fino alla radice del repo; richiamalo con /skills o $academic-research. Per tutti i progetti: ~/.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 carica lo skill all'avvio; richiamalo con “/” nella chat Agent. Cursor legge anche .claude/skills e .agents/skills. Per tutti i progetti: ~/.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 propone di attivare lo skill quando il compito corrisponde: tu confermi. Per tutti i progetti: ~/.gemini/skills.
Leggi SKILL.md e i file correlati prima di installare. Clicca un file per l'anteprima.
Contenuto di SKILL.md
Testo originale dell'autore · sola letturaAcademic 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 ↗.
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> 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.
Domande frequenti
Che cosa fa lo skill academic-research?
Cerca e scarica articoli accademici, analizza citazioni e scrive manoscritti LaTeX.
Come si installa academic-research?
Apri il blocco “Installazione” in questa pagina e scegli il tuo agente — Claude Code, Codex, Cursor o Gemini CLI — per i comandi pronti che copiano la cartella dello skill nel posto giusto. Oppure copia il prompt dalla scheda “Con l'assistente” e lascia che sia l'assistente a controllare e installare lo skill.
Con quali modelli AI funziona academic-research?
Gli skill sono semplici istruzioni in Markdown (formato Agent Skills), quindi academic-research funziona con qualsiasi agente che legge SKILL.md: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode e altri. Il modello carica le istruzioni solo quando il compito corrisponde alla descrizione dello skill.
academic-research è gratuito?
Lo skill è pubblicato apertamente da Lingtai-AI con licenza Apache-2.0. Lo skill in sé non costa nulla: paghi solo il tuo fornitore AI per i token usati.
È sicuro installare gli skill?
Uno skill è un insieme di istruzioni che il tuo agente seguirà: leggi SKILL.md e i file correlati prima di installarlo. Gli script nella cartella dello skill vengono eseguiti solo se li esegue l'agente: controlla cosa fa e non concedere permessi superflui.
In cosa differisce uno skill da un server MCP o da un plugin?
Un server MCP o un plugin danno al modello nuovi strumenti: codice che viene eseguito e restituisce un risultato. Uno skill fornisce conoscenza e metodo: come affrontare il compito, quali passi seguire e com'è un buon risultato. Non servono server né chiavi API.