/find-journal

name: find-journal
description: Use when choosing where to submit a manuscript. Matches the abstract against curated journal profiles and returns ranked picks with scope fit, AI-disclosure policy, an acceptance-readiness pre-flight and a reject-fallback cascade. Impact and APC figures may be stale.
metadata:
  triggers: "find journal, recommend journal, where to submit, which journal, journal selection, target journal, journal match"

Find Journal Skill

Phase 1: Input Collection

Required Inputs

1. **Abstract text** or key findings summary 2. **Study type**: original research, meta-analysis, case report, technical note, review, letter, AI validation, diagnostic accuracy, etc.

Optional Inputs

3. **Preferred tier**: Q1 / Q1-Q2 / any (default: any) 4. **OA preference**: Full OA / Hybrid OK / No preference (default: no preference) 5. **Field focus**: radiology, medical AI, clinical specialty, methodology, education, general medicine 6. **Journals to exclude**: journals that have already rejected this manuscript

If only an abstract is given, infer the study type from it. When called from `/write-paper` or another skill, take the abstract and study type from the calling context and skip these questions.

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Phase 2: Theme Extraction

From the abstract or key findings, extract: disease/condition; modality/technique; methodology (e.g., retrospective cohort, diagnostic accuracy, systematic review, RCT); population; and innovation type (new algorithm, clinical validation, workflow improvement, educational tool).

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Phase 2.5: Acceptance-Readiness & Design-Ceiling Pre-flight

Editors screen importance/novelty and the design ceiling before scope fit (lack of novelty/importance is the top desk-rejection cause). This phase estimates that first filter, so the skill does not recommend a venue whose bar the design cannot clear. It is advisory, and its flags are **not auto-fixable**: the author decides.

2.5.1 Run the deterministic pre-flight (preferred)

If a manuscript or abstract file is available, run:

python3 ${CLAUDE_SKILL_DIR}/scripts/assess_acceptance_readiness.py <manuscript_or_abstract.md>
# add --json for a machine-readable report

It returns flags in four categories — DESIGN_CEILING, UNFIXABLE_DEFECT, IMPORTANCE_RISK, CLAIM_MISMATCH — and a ceiling verdict (`NO LISTED SIGNAL MATCHED - NOT A DESIGN CLEARANCE …` / `IMPORTANCE-FRAMING REVIEW …` / `SPECIALTY / TOLERANT-VENUE OR DESIGN FIX …` / `HIGH-IMPACT VENUE UNLIKELY …`).

The script reads UTF-8 markdown or plain text only. A .docx, .pdf, .doc or other non-text file exits 2 with no report ("No scan was run"): convert it (e.g. `pandoc -t markdown`) and rerun, or fall back to 2.5.2. It skips the References/Bibliography section.

**Known limits.** The scan matches listed phrases, line by line. It does not understand negation or context ("Unlike prior single-center studies…" still flags single-center), and it misses paraphrases ("one institution", "has not been externally validated", the same patients in training and test sets). So an empty result is not a clearance: apply the §3 taxonomy with judgement (as in 2.5.2) before treating feasibility as uncapped, and adjudicate each flag before it demotes a venue.

2.5.2 If only pasted text is available

Read `${CLAUDE_SKILL_DIR}/references/acceptance_signals_schema.md` §3 (taxonomy and verdict bands), apply the same taxonomy to the pasted text with judgement, and assign the same ceiling verdict.

2.5.3 Carry the verdict forward

Record the ceiling verdict and its top flags for Phase 3.2 Axis 2 and for the Phase 4 Acceptance-Readiness Summary and Cascade plan. Do not block the recommendation on a ceiling: route to a venue the design can clear, or recommend a design change or presubmission inquiry.

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Phase 3: Profile Loading and Matching (2-Pass)

3.1 Pass 1: Load Compact Profiles

Read both tiers and merge them into one profile set:

# Public (shipped with the skill)
${CLAUDE_SKILL_DIR}/references/journal_profiles/*.md

# User-local private (optional, may be empty or absent)
$HOME/.claude/private-journal-profiles/find-journal/*.md

On a filename collision the private copy wins (user override). If either directory is missing or empty, continue with the other and say which tier was unavailable. Count the profiles actually loaded after the merge and state the total in the output — never hard-code the count.

From each profile parse Scope, Scope Keywords, Article Types Accepted, Classification (Tier, OA, Field), Special Notes (includes a 1-line AI policy summary), and the optional **Acceptance Signals** block (format: `${CLAUDE_SKILL_DIR}/references/acceptance_signals_schema.md` §1).

Do NOT read write-paper profiles in this pass — they are 4-5x larger and carry formatting detail irrelevant to matching.

3.2 Two-Axis Scoring (scope fit × acceptance feasibility)

Score each journal on two independent axes: scope fit ("does this journal cover my topic?") and acceptance feasibility ("can this manuscript's design and importance clear this journal's bar?"). Keep them separate — a perfect scope match can still desk-reject the design.

**Axis 1 — Scope fit.** Compute a composite scope-fit score:

| Factor | Weight | Description | |--------|--------|-------------| | Scope alignment | 40% | How well the manuscript's themes match the journal's scope and keywords | | Study type fit | 25% | Whether the journal accepts this article type and values this methodology | | Tier match | 20% | Alignment with user's preferred tier (if specified) | | OA match | 10% | Alignment with user's OA preference (if specified) | | Special fit | 5% | Bonus for unique alignment with journal's Special Notes |

**Axis 2 — Acceptance feasibility.** Weigh the Phase 2.5 ceiling verdict against each journal's Acceptance Signals (selectivity band, desk-reject triggers, design expectations); for a profile without that block, use its Special Notes plus the Phase 2.5 taxon