journal-check

name	journal-check
description	Look up ONE named journal/venue (by name or ISSN) and report its index status: WoS/Scopus/TR Dizin/DOAJ, quartile, SJR, predatory flag. Use when a SPECIFIC journal is named ('is X a good journal', 'check ISSN Y'). Called with --fit and NO venue name, instead derives a candidate target-venue shortlist from the project's own reference list ('where should I submit', 'hangi dergiye gönderelim') and flags cited sources sitting in predatory or unindexed venues. For a CONCEPTUAL question about what these index classes mean (e.g. 'what is the difference between SCIE and ESCI'), use the literature-review skill instead. No gate.
disable-model-invocation	true
argument-hint	<name|issn> | --fit

Journal/Venue Quality Check

This skill looks up journal/conference venue metadata to help you screen sources and choose submission targets.

Language: every user-facing string below (gates, aborts, summaries) is written in Turkish, the kit's conversational default (CLAUDE.md). If the active project's PROJECT.md sets Conversation language: en, present the equivalent content in natural English — translate meaning and structure (headings, bullets, ✅/❌ markers), don't reproduce the Turkish text verbatim.

PROCEDURE

1. Parse Input

User provides either:

• Journal/venue name: "IEEE Transactions on Pattern Analysis and Machine Intelligence"
• ISSN: "0162-8828"
• Conference name: "NeurIPS 2024"

2. Lookup via Script

Run:

python scripts/venue_check.py --title "<name>" [--json] [--offline]

Or if ISSN provided:

python scripts/venue_check.py --issn "<issn>" [--json]

Expected output:

{
  "issn": "0162-8828",
  "title": "IEEE Transactions on Pattern Analysis and Machine Intelligence",
  "abbrev": "IEEE TPAMI",
  "venue_meta": {
    "wos_index": "SCIE",
    "wos_class": ["Computer Science, Artificial Intelligence"],
    "scopus": true,
    "scopus_class": "Computer Science",
    "quartile": "Q1",
    "sjr": "13.2",
    "tr_dizin": false,
    "doaj": false,
    "predatory_flag": false,
     
     
  

3. If WoS Index Unknown

If wos_index: "unknown", the script will have tried Clarivate Master Journal List via cache. If still unknown:

• WebFetch the MJL search page: mjl.clarivate.com with the venue name
• Check if the venue appears in SCIE, SSCI, AHCI, or ESCI collections
• Update the local sources/venues.jsonl cache:

  python scripts/venue_check.py --title "..." --set-wos "SCIE|SSCI|AHCI|ESCI|none"
  

4. Display Venue Profile (Human-Readable)

Print to user:

══════════════════════════════════════════════════════════════
VENUE PROFILE: IEEE Transactions on Pattern Analysis & Machine Intelligence
──────────────────────────────────────────────────────────────

Indexing Status:
  ✓ Web of Science (SCIE)        Tier: Q1
  ✓ Scopus                        Class: Computer Science
  ✗ TR Dizin (Turkish Index)
  ✗ DOAJ (Open Access)

Quality Metrics:
  SJR: 13.2  (Impact measure)
  H-Index: 500+
  Predatory Flag: ✓ SAFE

Last Checked: June 15, 2024
Source: SCImago data

Recommendation:
  ✅ EXCELLENT TARGET
  Önerim: Yüksek kaliteli bir dergidir. Makale gönderimi için ideal.
  En yüksek titizlik standardı (Impact Factor ~13).

Last 5 Years' CiteScore: [if available]
  2023: X.X  |  2022: Y.Y  |  2021: Z.Z

Impact Factor (JCR): [if available]
  2023: A.A

5. Recommendation Logic

Based on venue_meta fields, return one of:

Condition	Recommendation
WoS Q1 or Q2 + not predatory	✅ EXCELLENT TARGET — This is a top-tier publication venue. High selectivity expected.
WoS Q3 or Q4 + not predatory	✅ GOOD TARGET — Solid venue with peer review. Suitable for most research.
Scopus-only (not WoS) + not predatory	✅ ACCEPTABLE TARGET — Peer-reviewed via Scopus. Check scope fit carefully.
TR Dizin (Turkey-indexed)	✅ GOOD FOR TR AUDIENCE — Türkçe akademik camiaya göre saygın.
DOAJ open-access	⚠️ CAUTION — Open-access only; verify actual review standards. Many low-quality open-access journals exist.
Predatory flag = true	❌ AVOID — This journal shows signs of predatory practices (fabricated IF, rapid publication, email spam). Do NOT submit.
WoS index = "none" + checked_at recent	⚠️ VERIFY QUALITY — Not indexed in major indices. Verify peer review directly on journal website.
No metadata found anywhere	⚠️ UNKNOWN — Venue may be new, niche, or not tracked by major databases. Request user to verify on journal website.

6. Additional Context

If user is sourcing literature, add:

USE IN LITERATURE REVIEW:
Evidence from [VENUE] venues is weighted as:
  • SCIE Q1–Q2: High credibility (weight: 1.0)
  • SCIE Q3–Q4: Moderate credibility (weight: 0.8)
  • Scopus-only: Lower weight (0.6) — verify peer review
  • TR Dizin: Credible for Turkish context (0.7–0.9)
  • Predatory: DO NOT CITE

This informs evidence card strength scoring.

If user is choosing submission venue, add:

SUBMISSION STRATEGY:
1. Check scope: Does your paper fit this journal's focus?
2. Check recent articles: Are they similar to yours?
3. Read the journal's OWN published figures (acceptance rate, time-to-first-
   decision) from its About/Statistics page. Do NOT state a rate from memory or
   infer one from the quartile — quartile measures citation impact, not
   selectivity, and a fabricated percentage is exactly the defect class this kit
   exists to prevent. If the journal publishes no figure, say it publishes none.
4. Run `/journal-check --fit` to see how many of this project's own sources this
   venue published — measured scope proximity, from the registry.

7. Cache Result

Always cache lookup to sources/venues.jsonl (append/update):

{
  "issn": "...",
  "title": "...",
  "venue_meta": {...},
  "user_query": "<user-input>",
  "checked_at": "<ISO timestamp>"
}

8. Final Output

Print summary:

✅ Dergi bilgisi güncellendi: sources/venues.jsonl

Tıklayın / Kopyalayın:
  - Dergi web sitesi: [if available]
  - SCImago profil: [if available]
  - Journal Citation Reports: [if JCR link available]

---

MODE B — /journal-check --fit (no venue named)

Answers "where does this work belong?" from the project's own register instead of from a named journal. Runs when the user passes --fit, or asks where to submit without naming a venue.

B1. Derive the distribution

python scripts/venue_fit.py --json

Reads sources/registry.jsonl and the venue_check.py cache. Fetches nothing. Counts included sources by default; with none yet, it falls back to screened and says so in status_note — repeat that caveat to the user, do not drop it.

B2. Resolve what is unknown

Every shortlist entry with index_provenance: "unknown" carries a resolve_with command. Run it for the top entries the user cares about, then re-run B1. Never present an unresolved venue as un-flagged: unknown means the lookup never happened, not that nothing was found.

B3. Present the shortlist

══════════════════════════════════════════════════════════════
HEDEF DERGİ ADAYLARI — kendi kaynak listenizden türetildi
──────────────────────────────────────────────────────────────
Sayılan kaynak: 41 / 47   ·   Farklı mekân: 22

  1. IEEE Trans. Medical Imaging      6 kaynak (%14.6)  SCIE · Q1
     en yeni 2025 · medyan 2022
  2. Medical Image Analysis           5 kaynak (%12.2)  SCIE · Q1
     en yeni 2024 · medyan 2021
  3. Computers in Biology & Medicine  4 kaynak (%9.8)   SCIE · Q2
     ...

Bu sayı NEYİ ölçer: derginin, sizin okuduğunuz literatürle konu yakınlığını.
Bu sayı NEYİ ölçmez: kabul olasılığını, dergi kalitesini, gönderim tavsiyesini.

Reproduce the measures field's both halves every time. A ranked list of journals is read as a recommendation unless it explicitly says it is not one.

B4. Report the concerns — the list read backwards

concerns holds venues you cite that are predatory-flagged or in no index. This is the half of the output nobody asks for and the more actionable one:

⚠️ Kaynak listenizde dikkat gerektiren mekânlar:
  • Fast Publish Journal — 2 kaynak — predatory_flag
    Gönderim öncesi bu kaynakları yeniden değerlendirin.
  • Local Bulletin — 1 kaynak — hiçbir indekste görünmüyor
    Sorun kanıtı değil; bilinçli bir karar olması gerekiyor.

Never silently drop a flagged venue from the shortlist — that hides the fact that the project's own literature leans on it.

B5. What NOT to do

• Do not turn the counts into a composite "fit score". The count is auditable; a weighted score is not, and POSITIONING.md refuses it.
• Do not add journals the user's registry does not support "because they seem relevant". This mode reports a measured distribution; a remembered journal list is a different (and unverifiable) thing. If you want to suggest one, say plainly that it is a suggestion from general knowledge, outside the measurement.
• Do not merge or split venues by hand. Check merged_variants: if a reported merge is wrong, tell the user, do not correct the number silently.

---

GATES

None. This skill runs without preconditions.

---

ARGUMENT EXAMPLES

/journal-check "IEEE Transactions on Pattern Analysis"
/journal-check 0162-8828
/journal-check "Computer Vision and Image Understanding"
/journal-check "Mühendislik Fakültesi Dergisi"  [Turkish journal name]
/journal-check NeurIPS  [conference shorthand]

---

SCRIPT USAGE

python scripts/venue_check.py --title "..." [--json] [--offline]
python scripts/venue_check.py --issn "..." [--json]
python scripts/venue_check.py --title "..." --set-wos "SCIE"  # update cache
python scripts/venue_fit.py --json [--status included] [--top 20]  # Mode B

---

ERROR MESSAGES (Turkish)

❌ Dergi bulunamadı: "[input]". Adı veya ISSN'yi kontrol edin.
⚠️ Dergi kalitesi belirsiz. Dergi web sitesinde hakem incelemesi politikasını doğrulayın.
✅ Şüpheli dergi algılandı: "[venue]". Makale gönderimi önerilmez.

---

FOLLOWING STEPS

After /journal-check:

• If choosing a submission target: note the venue for PROJECT.md or decision log.
• If screening sources: use the confidence score (Q-tier, Scopus/WoS status) to weight evidence cards.
• If unsure: show user the journal's website for manual verification.