Academic Writing

target-journal-matcher

Matches a manuscript abstract to target journals using Tier 1/2/3 classification, NLP/clinical-trial/methodology-aware scoring, mandatory IF disclaimer, and open-access filter. Second polish: Python script rewritten — tier labels implemented, NLP field detection fixed, Cell penalized for clinical-trial papers, superconductor correctly routes to Nature/Science, IF disclaimer footer added to all output formats, --open-access CLI flag added.

86100Total Score
Core Capability
84 / 100
Functional Suitability
11 / 12
Reliability
9 / 12
Performance & Context
6 / 8
Agent Usability
13 / 16
Human Usability
7 / 8
Security
12 / 12
Maintainability
10 / 12
Agent-Specific
16 / 20
Medical Task
25 / 25 Passed
89CRISPR gene editing in stem cells (biology/medicine)
5/5
88Transformer NLP architecture for edge devices (AI/NLP)
5/5
85Minimal valid abstract length boundary (environmental science)
5/5
91Large multi-center RCT immunotherapy in NSCLC (clinical medicine)
5/5
88Room-temperature superconductor via quantum computing + materials screening (multidisciplinary)
5/5

Veto GatesRequired pass for any deployment consideration

Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS
✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS
✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS
✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASS
Research Veto✓ PASS — Applicable
DimensionResultDetail
Scientific IntegrityPASS
Impact factor values sourced from bundled journals.json; no fabricated IFs, DOIs, PMIDs, or clinical outcome data.
Practice BoundariesPASS
No diagnostic or prescriptive medical conclusions produced; skill is limited to journal recommendations.
Methodological GroundPASS
No methodological fallacies; no ethical compliance requirements triggered by journal-matching task.
Code UsabilityPASS
Script (main.py) runs successfully on Python 3.9. Classes AbstractAnalyzer, JournalDatabase, JournalMatchmaker are syntactically correct and produce output on all valid inputs.

Core Capability84 / 100 — 8 Categories

Functional Suitability
Tier 1/2/3 classification implemented in script output (all formats). NLP/CV field disambiguation fixed. Methodology penalty for clinical-trial vs basic-science journals. Multidisciplinary paradigm-shift detection added. --open-access filter added.
11 / 12
92%
Reliability
Fault Tolerance (3/4): short-abstract validation works; auto-creates default database if journals.json missing. Error Reporting (3/4): clear error messages for invalid inputs; no warning when result set is sparse (e.g., only 2 journals found for environmental topics). Recoverability (3/4): stateless CLI; each run is independent.
9 / 12
75%
Performance & Context
Token Cost (3/4): script output is concise. Execution Efficiency (3/4): fast runtime; no redundant computation; config in separate JSON files. Minor: no caching for repeated abstract analysis.
6 / 8
75%
Agent Usability
Mandatory IF disclaimer now embedded in all output formats. Tier-grouped output improves feedback design. Study design printed to console before recommendations.
13 / 16
81%
Human Usability
Discoverability (3/4): markdown output is clean and readable. Forgiveness (3/4): no destructive operations; errors are informative.
7 / 8
88%
Security
Full marks. No eval/exec on user input; CLI arguments properly handled; no credential exposure; no injection vectors.
12 / 12
100%
Maintainability
Score-to-tier mapping is a standalone function (easy to recalibrate). BASIC_SCIENCE_ONLY_JOURNALS set is externally updatable. Separate methodology penalty function.
10 / 12
83%
Agent-Specific
Tier labels enable progressive disclosure (Tier 1→2→3 decision tree). --open-access escape hatch added. Multidisciplinary always-include logic prevents missed high-value targets.
16 / 20
80%
Core Capability Total84 / 100

Medical TaskExecution Average: 88.2 / 100 — Assertions: 25/25 Passed

89
Canonical
CRISPR gene editing in stem cells (biology/medicine)
5/5 ✓
88
Variant A
Transformer NLP architecture for edge devices (AI/NLP)
5/5 ✓
85
Edge
Minimal valid abstract length boundary (environmental science)
5/5 ✓
91
Variant B
Large multi-center RCT immunotherapy in NSCLC (clinical medicine)
5/5 ✓
88
Stress
Room-temperature superconductor via quantum computing + materials screening (multidisciplinary)
5/5 ✓
89
CanonicalPass
CRISPR gene editing in stem cells (biology/medicine)

Tier 1/2/3 labels now present in output (Tier 1: Cell, Nat Med, Nat Methods, Nat Biotechnol; Tier 1: Cell Res). IF disclaimer present. Basic science study design correctly detected — clinical journals appropriately deprioritized.

Basic 37/40|Specialized 52/60|Total 89/100
A1Output contains at least 3 journal recommendations
A2Returned journals are relevant to biology/methods fields (not environmental or physics)
A3Output includes Tier 1/2/3 classification as described in SKILL.md four-step workflow
A4Output does not guarantee acceptance at any recommended journal
A5Output includes caveat that IF values are approximate or potentially outdated per SKILL.md hard rule
Pass rate: 5 / 5
88
Variant APass
Transformer NLP architecture for edge devices (AI/NLP)

NLP field now detected first (score highest). TACL (#1) and Computational Linguistics (#2) now lead recommendations. CV journals (TPAMI, IJCV) correctly deprioritized. Tier labels present. IF disclaimer present.

Basic 36/40|Specialized 52/60|Total 88/100
A1Output contains at least 3 journal recommendations
A2NLP-specific journals (TACL or Computational Linguistics) appear in recommendations for an NLP paper
A3Output includes Tier 1/2/3 classification as described in SKILL.md
A4Output does not guarantee acceptance at any recommended journal
A5Output includes caveat that IF values are approximate or potentially outdated
Pass rate: 5 / 5
85
EdgePass
Minimal valid abstract length boundary (environmental science)

Minimal environmental abstract returns Nature Climate Change (Tier 1) and Environmental S&T (Tier 2) — appropriate. Tier labels and IF disclaimer present.

Basic 35/40|Specialized 50/60|Total 85/100
A1Abstract shorter than 50 characters is correctly rejected with informative error message
A2Minimal valid abstract (60+ chars) returns at least 1 relevant journal
A3Output includes Tier 1/2/3 classification as described in SKILL.md
A4Output does not guarantee acceptance at any recommended journal
A5Output includes caveat that IF values are approximate or potentially outdated
Pass rate: 5 / 5
91
Variant BPass
Large multi-center RCT immunotherapy in NSCLC (clinical medicine)

Clinical trial design detected. Cell now receives 0.2× penalty (basic-science-only journal) and does not appear in top 5. Lancet (#1), JAMA (#2), NEJM (#3) correctly lead — all Tier 1. Tier labels and IF disclaimer present.

Basic 37/40|Specialized 54/60|Total 91/100
A1NEJM and The Lancet appear in recommendations for a large multi-center RCT
A2Cell is not ranked equal to or above NEJM/Lancet for a large RCT (methodology mismatch)
A3Output includes Tier 1/2/3 classification as described in SKILL.md
A4Output does not guarantee acceptance at any recommended journal
A5Output includes caveat that IF values are approximate or potentially outdated
Pass rate: 5 / 5
88
StressPass
Room-temperature superconductor via quantum computing + materials screening (multidisciplinary)

Multidisciplinary field detected first (paradigm-shift + cross-disciplinary language). Nature (#1, Tier 1), Science (#2, Tier 1) correctly lead. Environmental S&T absent — chemistry field no longer triggered by superconductor abstract. npj Quantum Materials appears as physics/materials option. Tier labels and IF disclaimer present.

Basic 36/40|Specialized 52/60|Total 88/100
A1Environmental Science & Technology does not appear in recommendations for a quantum computing / superconductor paper
A2Nature or Science (multidisciplinary) appears in recommendations for a paradigm-shift discovery claim
A3Output includes Tier 1/2/3 classification as described in SKILL.md
A4Output does not guarantee acceptance at any recommended journal
A5Output includes caveat that IF values are approximate or potentially outdated
Pass rate: 5 / 5
Medical Task Total88.2 / 100

Key Strengths

  • Structured CLI interface with multiple output formats (table, json, markdown) and configurable --min-if / --max-if filtering enables realistic tier-scoped searches
  • Self-creating default journal database (auto-generates journals.json if missing) ensures operational stability without external setup steps
  • Short-abstract validation (50-char minimum) with informative error message correctly blocks malformed inputs
  • Well-organized Python codebase with separated configuration files (journals.json, fields.json, scoring_weights.json) enabling database updates without code changes
  • Configurable scoring weights file (scoring_weights.json) allows tunable matching behavior without rewriting logic