Alterlab Dergipark logo

Alterlab Dergipark

Organization
NVlabs
alterlab-dergipark

Harvests article metadata, abstracts, and full-text PDFs from DergiPark (TÜBİTAK ULAKBİM's national journal-hosting platform, ~2,537 journals) via its verified platform-wide OAI-PMH endpoint (https://dergipark.org.tr/api/public/oai/; verbs Identify/ListSets/ListRecords/GetRecord; prefixes oai_dc/oai_mods/oai_marc/oai_etdms; setSpec=journal-slug), parses Highwire citation_* and DC.* meta tags on /pub/{slug}/article/{id} pages, pulls PDFs from the citation_pdf_url path, and emits BibTeX/RIS locally. Use when the user wants to harvest a Turkish journal, fetch DergiPark articles, list a journal archive, get BibTeX/RIS for a DergiPark paper, or read a journal aim-and-scope (öz/kapsam). For TR Dizin indexing status use alterlab-trdizin; for YÖK theses use alterlab-yok-tez; for academic profiles use alterlab-yok-akademik. Part of the AlterLab Academic Skills suite.

Overview

PublisherNVlabs
RepositorySkill2Env
Skill namealterlab-dergipark
Stars
189
Forks
18
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVlabs on GitHub. Read the source before you install it.

Installation

Install the Alterlab Dergipark AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVlabs/Skill2Env/tree/main/SkillHub/skills/alterlab-dergipark
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVlabs/Skill2Env.git /tmp/Skill2Env
mkdir -p .claude/skills
cp -r /tmp/Skill2Env/SkillHub/skills/alterlab-dergipark .claude/skills/alterlab-dergipark
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alterlab Dergipark in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alterlab Dergipark on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alterlab Dergipark is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DergiPark — National Journal Harvester (TÜBİTAK ULAKBİM)

DergiPark (https://dergipark.org.tr) is TÜBİTAK ULAKBİM's national journal-hosting platform — ~2,537 Turkish scholarly journals on OJS-based infrastructure. This skill harvests its metadata, abstracts, and full-text PDFs reproducibly, by going through the one stable machine surface: a platform-wide OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) endpoint, plus open article pages. It never guesses bibliographic data — every field comes from the live endpoint, and it emits BibTeX/RIS locally from parsed meta tags.

The interactive search page is gated behind a human-verification challenge, so this skill does not scrape search over plain HTTP. OAI set-harvest and direct article-page fetches are the robust paths (see references/oai-cookbook.md).

When to Use This Skill

Use it when the request is about getting data out of DergiPark:

  • harvest / fetch all articles of a Turkish (DergiPark) journal
  • list a journal's archive or find its slug
  • get BibTeX or RIS (an export format) for a DergiPark article
  • download a DergiPark full-text PDF
  • read a journal's aim-and-scope (öz/kapsam — abstract/scope) or its self-declared index list

Canonical Turkish terms you may see: öz (abstract), kapsam (scope), dergi (journal), künye (citation/masthead). Preserve Turkish spelling with diacritics in all user-facing strings (e.g. Mülkiye Dergisi).

Does NOT Trigger — route adjacent requests to the right sibling

The request is really about…Route to
Whether a journal is indexed in TR Dizin (national citation index) / its TR Dizin statusalterlab-trdizin
Finding/searching a graduate thesis (YÖK Ulusal Tez Merkezi)alterlab-yok-tez
An academic's profile / affiliation (YÖK Akademik)alterlab-yok-akademik
University program admission statistics (YÖK Atlas)alterlab-yokatlas
Computing doçentlik (associate-professorship) eligibility pointsalterlab-docentlik-eligibility
Computing akademik teşvik (academic-incentive) scorealterlab-akademik-tesvik
Depositing a manuscript/data in Aperta (TÜBİTAK open archive)alterlab-aperta
Verifying that cited references exist / hallucination auditalterlab-citation-verifier
Turkish APA / TR Dizin writing-style conventionsalterlab-tr-academic-style

How It Works — Three Scripts

All run with uv run python from the skill directory. No API key. Each prefers requests if installed, else falls back to stdlib urllib. Full recipes and gotchas are in references/oai-cookbook.md; the verified endpoint table is in references/endpoints.md.

1. scripts/dergipark_oai.py — OAI harvest (primary path)

bash
# Find a journal's slug (slug == OAI setSpec)
uv run python scripts/dergipark_oai.py list-journals --tsv | rg -i "mülkiye"

# Harvest a whole journal (follows resumptionToken paging automatically)
uv run python scripts/dergipark_oai.py harvest mulkiye --prefix oai_dc --out mulkiye.json

# One article's structured oai_dc record
uv run python scripts/dergipark_oai.py get 10 --prefix oai_dc

Verbs: Identify / ListSets / ListRecords / GetRecord. Prefixes: oai_dc (parsed into clean fields) and oai_mods / oai_marc / oai_etdms (returned as raw record XML for callers that want the richer schema).

2. scripts/article_meta.py — one article → BibTeX / RIS

bash
uv run python scripts/article_meta.py /en/pub/mulkiye/article/10 --format bibtex
uv run python scripts/article_meta.py /en/pub/mulkiye/article/10 --format ris
uv run python scripts/article_meta.py /en/pub/mulkiye/article/10 --format json

Parses the page's citation_* + DC.* meta tags and formats BibTeX/RIS locally — it does not call any on-site export backend (that route is unverified). The JSON output includes a resolved pdf_url.

3. scripts/journal_info.py — aim-and-scope + self-declared indexes

bash
uv run python scripts/journal_info.py mulkiye --lang en

Returns the aim-and-scope text and the /indexes page, the latter stamped with a DISCLAIMER and cross-check pointers to TR Dizin and DOAJ.

Three Rules That Prevent Wrong Answers

  1. Publication date ≠ OAI datestamp. The OAI <datestamp> is the platform re-index time, not the publication date. To slice by publication year, harvest then filter on dc:date locally — do not rely on from/until.
  2. PDF id ≠ article id. Take the PDF link from the page's citation_pdf_url meta tag (e.g. /en/download/article-file/9 for article /10); never build it from the article id.
  3. Hosting ≠ indexing. Being on DergiPark says nothing about quality or TR Dizin coverage. The /indexes page is self-declared and unverified by DergiPark. For an authoritative TR Dizin status verdict, route to alterlab-trdizin. See references/indexing.md.

Graceful Degradation

Network failures surface as a network_unavailable / oai_error JSON object on stderr (exit 1) — the scripts never fabricate a record or a verdict. If the endpoint is unreachable, say so and stop; do not invent metadata from memory.

References

  • references/endpoints.md — every verified endpoint, prefix, and identifier scheme (with what is explicitly UNVERIFIED), observed live 2026-06-06.
  • references/oai-cookbook.md — copy-paste recipes: slug lookup, full harvest, year filtering, PDF download, and the gated-search workaround via Playwright.
  • references/indexing.md — the DergiPark-vs-TR Dizin distinction and how to cross-check real indexing against TR Dizin and DOAJ.

Part of the AlterLab Academic Skills suite.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Alterlab Dergipark AI skill do?

Harvests article metadata, abstracts, and full-text PDFs from DergiPark (TÜBİTAK ULAKBİM's national journal-hosting platform, ~2,537 journals) via its verified platform-wide OAI-PMH endpoint (https://dergipark.org.tr/api/public/oai/; verbs Identify/ListSets/ListRecords/GetRecord; prefixes oai_dc/oai_mods/oai_marc/oai_etdms; setSpec=journal-slug), parses Highwire citation_* and DC.* meta tags on /pub/{slug}/article/{id} pages, pulls PDFs from the citation_pdf_url path, and emits BibTeX/RIS locally. Use when the user wants to harvest a Turkish journal, fetch DergiPark articles, list a journal...

Why use Alterlab Dergipark on TypingMind?

Because you install it once and use it with any model. Alterlab Dergipark is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alterlab Dergipark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVlabs/Skill2Env/tree/main/SkillHub/skills/alterlab-dergipark. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Alterlab Dergipark?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alterlab Dergipark?

As many as you like. As long as a model supports skills, you can use Alterlab Dergipark with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alterlab Dergipark AI skill free?

Yes. It is published on GitHub by NVlabs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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