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Alterlab Akademik Tesvik

Organization
NVlabs
alterlab-akademik-tesvik

Computes the annual Turkish academic-incentive (akademik teşvik) score for Devlet (state) university öğretim elemanı (academic staff) from a classified activity list, applying the verified Akademik Teşvik Ödeneği Yönetmeliği (2018/11834) Faaliyet Hesaplama Tablosu headline puanları (Proje 20, Araştırma 15, Yayın 30, Patent 30, Atıf 30, Tebliğ 20, Ödül 20) with the k (author-count), p (Q1 1/Q2 0.8/Q3 0.5/Q4 0.25), and r (project-role) coefficients via türü puanı = Σ(faaliyet oranları) × headline (MADDE 8/2), then enforcing the per-type headline ceilings, the 100 cap (MADDE 8/3), and the net-30 payment gate (MADDE 10/3) in a deterministic scripts/tesvik_score.py. Use when the user wants to calculate akademik teşvik puanı, check the en az 30 (net-30) threshold, apply TABLO 4 ceilings, or estimate teşvik eligibility; keeps its coefficients separate from doçentlik scoring. For doçentlik point eligibility prefer alterlab-docentlik-eligibility. Part of the AlterLab Academic Skills suite.

Overview

PublisherNVlabs
RepositorySkill2Env
Skill namealterlab-akademik-tesvik
Stars
189
Forks
18
Bundled files
4
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.

  • 4 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 Akademik Tesvik 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-akademik-tesvik
  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-akademik-tesvik .claude/skills/alterlab-akademik-tesvik
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alterlab Akademik Tesvik 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 Akademik Tesvik 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 Akademik Tesvik 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.

Akademik Teşvik — Annual Incentive Score Calculator

Computes the akademik teşvik (academic-incentive) puanı for a Turkish Devlet (state) university öğretim elemanı (academic staff member) for one calendar year, and decides whether the incentive is payable. It applies the Akademik Teşvik Ödeneği Yönetmeliği (Academic Incentive Allowance Regulation; Bakanlar Kurulu 2018/11834, RG 27/6/2018 No. 30461, amended CK-2043 of 2020) deterministically and offline, so the same activity list always yields the same score.

This is an annual calculation distinct from doçentlik (associate- professorship) eligibility scoring: the two rule systems use different coefficients and thresholds, and this skill keeps them separate on purpose.

Quick Start

Calculate my akademik teşvik score for this year
Akademik teşvik puanımı hesapla — 30 barajını geçiyor muyum?
Does this activity list clear the net-30 incentive threshold?
Apply the TABLO 4 ceilings and the 30% rule to my publications

→ Classify each activity into its regulation type, build a JSON activity list, run scripts/tesvik_score.py, then report the final score, the per-type breakdown, and the net-30 pay/no-pay verdict.

When to Use This Skill

Use it when the request is about the annual teşvik (incentive) score or its payment threshold — calculating the puan, applying the TABLO 4 / Faaliyet Hesaplama Tablosu ceilings, checking the en az 30 (net-30) gate, the per-type headline ceilings (the %30 table weighting), the 100 cap, or estimating whether a year's research output qualifies for the incentive payment.

Does NOT Trigger

Route adjacent requests to the right sibling skill:

The request is really about…Use this skill instead
Doçentlik (associate-prof) point eligibility & ÜAK minimumsalterlab-docentlik-eligibility
TÜBİTAK ARDEB 1001/1002-A proposal draftingalterlab-tubitak-proposal
Whether a journal is in TR Dizin (live index status)alterlab-trdizin
DergiPark journal/article metadata or OAI harvestalterlab-dergipark
Turkish APA-7 / TR Dizin manuscript formatting & özalterlab-tr-academic-style
YÖK Akademik profile / official affiliation lookupalterlab-yok-akademik
Verifying that cited references actually existalterlab-citation-verifier

If the user asks for the TRY payout amount, this skill computes the score and shows the formula, but does not quote money from a hardcoded salary coefficient (it changes every six months) — see "Payout" below.

What the Score Is Built From

The regulation's method (MADDE 8/2) is two steps, not a per-activity base × k × p × r:

türü puanı = (Σ faaliyet oranları) × headline puan (MADDE 8/2-a); then akademik teşvik puanı = Σ türü puanları (MADDE 8/2-b).

Each Faaliyet Hesaplama Tablosu row gives an oran — a percentage-style multiplier like k × p × 60, r × 80, or 15 × ay — which you express as a fraction of that type's headline puan before summing. The verified tables (transcribed from the regulation PDF) live in references/tablo4.md:

  • Per-type headline puan (also the MADDE 8/3 ceiling): Proje 20, Araştırma 15, Yayın 30, Tasarım 15, Sergi 15, Patent 30, Atıf 30, Tebliğ 20, Ödül 20.
  • k (author count): 1→1, 2→0.8, 3→0.6, 4→0.45, 5→1/5, 6→1/6, 7+→1/n.
  • p (journal quartile): Q1 1, Q2 0.8, Q3 0.5, Q4 0.25 (AHCI uses p=0.5, MADDE 8/6).
  • r (project role): yürütücü (PI) 1, araştırmacı/bursiyer 0.5.

The full calculation pipeline, the worked example, and the payout formula are in references/hesaplama.md.

The Three Gates (do not skip any)

  1. Per-type headline ceiling + the %30 weighting — each type's puanı cannot exceed its headline puan (MADDE 8/3). Those headlines (all ≤ 30) are how the regulation encodes the MADDE 1 rule that each type's weight stays within 30% of the 100-point total — the %30 is a fixed table-design constraint on the ceilings, not a per-run "rescale to 30% of this year's gross" step.
  2. 100 cap — the total score is capped at 100 (MADDE 8/3; the payout formula uses puan/100).
  3. Net-30 gate — the incentive is payable only if the final score ≥ 30: "Akademik teşvik ödeneğinin ödenebilmesi için akademik teşvik puanının en az otuz olması gerekir" (MADDE 10/3, "Diğer hükümler"). Below 30, no payment regardless of output volume.

How to Run It

1. Classify activities into a JSON list

Each item needs a type (one of the nine canonical türler) and an oran — the regulation row's value (k × p × 60, r × 80, 15 × ay, the bare integer for citations/awards, …) expressed as a fraction of that type's headline puan. Take the table cell, treat its trailing integer as a percentage, and apply k/p/r/months:

json
[
  {"type": "Yayın",  "oran": 0.48, "label": "Q1 SSCI, 2 authors → k0.8·p1.0·0.60"},
  {"type": "Proje",  "oran": 0.80, "label": "TÜBİTAK 1001 yürütücü (A1) → r1.0·0.80"},
  {"type": "Atıf",   "oran": 0.08, "count": 12, "label": "12 SCI citations → 0.08 each"}
]

Type names accept Turkish spelling with diacritics (Yayın, Atıf, Tebliğ); the script ASCII-folds them internally. count (default 1) multiplies the oran for identical repeated activities. See references/hesaplama.md for how to derive each oran from the table, with worked rows.

2. Run the calculator

bash
uv run python skills/turkish-academia/alterlab-akademik-tesvik/scripts/tesvik_score.py \
    activities.json --json

Pass - to read the JSON list from stdin. Omit --json for a readable table.

3. Report the result

Present, in this order:

  1. The final score and whether it was capped at 100.
  2. The net-30 verdict: payable (≥30) or not payable (<30).
  3. The per-type breakdown (Σoran → raw türü puanı → final after the headline ceiling), surfacing any type the MADDE 8/3 ceiling reduced.
  4. Any warnings (unknown type, missing oran, ceiling reduction, gate failure).

Payout (score → TRY)

The TRY amount is (kadro oranı) × (score/100) × [(1500 + 8000) × memur aylık katsayısı] (MADDE 8/1), with kadro oranı = Profesör %100, Doçent %90, Dr. Öğr. Üyesi %80, araştırma görevlisi / öğretim görevlisi %70. The memur aylık katsayısı is revised ~twice a year and is not hardcoded; report the formula and ask the user to apply the current period's coefficient before quoting money.

Self-Check Before Reporting

  • Did you classify each activity into a real regulation type, or did the script warn about an unknown type (then it was dropped)?
  • Did you apply the headline ceilings (MADDE 8/3), the 100 cap, and the net-30 gate (MADDE 10/3) — and derive each oran from the table, not invent a base?
  • Are you reporting a score, not a fabricated TRY amount from a guessed salary coefficient?
  • Is this an annual teşvik question — or a doçentlik one that belongs to alterlab-docentlik-eligibility?
  • Is the subject a Devlet (state) university staff member? Foundation/private universities are out of scope; yabancı uyruklu (foreign-national) staff cannot benefit at all (MADDE 6/9), and faaliyetler done while seconded to a vakıf university are not counted (MADDE 6/7).

References

  • references/tablo4.md — verified ceilings and k/p/r coefficient tables, with the regulation source and last-verified date.
  • references/hesaplama.md — full calculation pipeline, worked example, the payout formula, and common pitfalls.
  • Regulation PDF: https://www.mevzuat.gov.tr/MevzuatMetin/21.5.201811834.pdf (Akademik Teşvik Ödeneği Yönetmeliği). Re-verify before a real başvuru.

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 Akademik Tesvik AI skill do?

Computes the annual Turkish academic-incentive (akademik teşvik) score for Devlet (state) university öğretim elemanı (academic staff) from a classified activity list, applying the verified Akademik Teşvik Ödeneği Yönetmeliği (2018/11834) Faaliyet Hesaplama Tablosu headline puanları (Proje 20, Araştırma 15, Yayın 30, Patent 30, Atıf 30, Tebliğ 20, Ödül 20) with the k (author-count), p (Q1 1/Q2 0.8/Q3 0.5/Q4 0.25), and r (project-role) coefficients via türü puanı = Σ(faaliyet oranları) × headline (MADDE 8/2), then enforcing the per-type headline ceilings, the 100 cap (MADDE 8/3), and the net-...

Why use Alterlab Akademik Tesvik on TypingMind?

Because you install it once and use it with any model. Alterlab Akademik Tesvik 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 Akademik Tesvik in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVlabs/Skill2Env/tree/main/SkillHub/skills/alterlab-akademik-tesvik. 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 Akademik Tesvik?

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 Akademik Tesvik?

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

Is the Alterlab Akademik Tesvik 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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