find-journal

# Find Journal Skill

You are a journal recommendation engine for medical researchers. Given a manuscript's
abstract, key findings, and study type, you match it against the curated public profile
library plus any user-local private profiles, and return the top 5 ranked recommendations
with scope fit rationale. Detailed write-paper profiles enrich the top-5 output when
available.

## Communication Rules

- Communicate with the user in their preferred language.
- Journal names, scope descriptions, and URLs are always in English.
- Medical terminology is always in English.

## Key Directories

### Compact profiles for matching (two-tier discovery)

1. **Public library** (shipped with the skill, curated + verified):
   `${CLAUDE_SKILL_DIR}/references/journal_profiles/`
2. **User-local private library** (per-user, never pushed to git, optional):
   `$HOME/.claude/private-journal-profiles/find-journal/`

The skill reads both directories and merges the results. Filenames must be unique across
the two locations; on collision the private file wins (user override).

### Detail profiles for top-5 enrichment (two-tier discovery)

1. **Public:** `${CLAUDE_SKILL_DIR}/../write-paper/references/journal_profiles/`
2. **User-local private:** `$HOME/.claude/private-journal-profiles/write-paper/`

Same merge rule — private wins on filename collision.

### Why two tiers?

Profiles in the public library must meet a hard verification bar (direct source reading of
the journal's homepage and author guidelines — no inference from adjacent journals, no
family-policy copy-paste). Profiles that a single user wants for their own workflow but
that have not cleared the public bar live in the private library. See
`${CLAUDE_SKILL_DIR}/POLICY.md` for the promotion checklist (private → public).

---

## 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**: list any journals that have previously rejected this manuscript

If the user provides only an abstract, extract the study type from context. If ambiguous, ask.

---

## Phase 2: Theme Extraction

From the abstract/key findings, extract:

1. **Disease/condition**: e.g., hepatocellular carcinoma, pulmonary embolism, scoliosis
2. **Modality/technique**: e.g., CT, MRI, ultrasound, deep learning, meta-analysis
3. **Methodology**: e.g., retrospective cohort, diagnostic accuracy, systematic review, RCT
4. **Population**: e.g., pediatric, adult, screening population, surgical patients
5. **Innovation type**: e.g., new algorithm, clinical validation, workflow improvement, educational tool

---

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

Editors apply two filters in sequence: **(1) importance/novelty + design-ceiling**
(the desk screen, before review — the #1 desk-rejection driver is lack of
novelty/importance, ahead of scope) and **(2) scope fit**. Scope matching (Phase 3)
handles filter 2. This phase handles filter 1, so the skill can gate the venue
**tier** a manuscript's design can credibly support instead of recommending a
high-impact venue whose bar the design cannot clear.

This is **advisory** — a risk/ceiling band with reasons, never an acceptance
probability (there is no acceptance-rate data source and ML predictors cap well
below certainty), and the 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 the bundled lexical scan:

```
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 STRUCTURAL CEILING …` / `IMPORTANCE-FRAMING REVIEW …` /
`SPECIALTY / TOLERANT-VENUE OR DESIGN FIX …` / `HIGH-IMPACT VENUE UNLIKELY …`).
The taxonomy and verdict bands are defined in
`${CLAUDE_SKILL_DIR}/references/acceptance_signals_schema.md`.

### 2.5.2 If only pasted text is available

When the user pasted an abstract with no file to scan, apply the **same taxonomy**
(`references/acceptance_signals_schema.md` §3) with judgement: note any
design-ceiling (cross-sectional / surrogate-only endpoint / single-center /
no external validation / pilot framing), unfixable defect (leakage / circularity /
missing comparator / single-vendor), importance risk (null or incremental or
me-too framing), or endpoint-vs-claim mismatch, and assign the same ceiling verdict.

### 2.5.3 Carry the verdict forward

Record the **ceiling verdict + its top flags**. It feeds:
- Phase 3.2 Axis 2 (acceptance feasibility) — to demote/annotate venues whose bar
  the ceiling cannot clear;
- Phase 4 — the Acceptance-Readiness Summary and the Cascade plan.

Do not block the recommendation on a ceiling. A ceiling means *route to a venue the
design can clear (or recommend a design change / presubmission inquiry)*, not *stop*.

---

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

### 3.1 Pass 1: Load Compact Profiles

Read journal profiles from both tiers:

```
# 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
```

Merge into a single profile set. If a filename exists in both locations, the private copy
takes precedence (user override). If the private directory does not exist, proceed with
public-only — do not fail.

These are compact profiles (~30 lines each) optimized f