paper-select-journal

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Paper Select Journal

核心原则

Core Principles

当前信息必须实时核验：scope、官网、业内认可度、中科院分区、近 3 个月论文都属于时效性信息，不能靠旧记忆。
中间文件只允许落在当前工作目录下的
```
.paper-select-journal/
```
隐藏目录；用户若明确指定其他目录，才可覆盖默认值。
Set1 不再依赖固定语义权重。脚本只负责最小硬过滤与候选池整理，真正的语义规划由当前宿主模型完成。
Set1 不是最终答案。最终报告只保留证据充分的 Set3，最多 10 个期刊。
不能推荐明显预警、垃圾期刊或影响因子低于
```
3
```
的期刊；若确实保留低于
```
3
```
的例外，必须写明“为何它仍是领域内人类专家认可的稳妥选择”。
宁可少报，也不要为了凑满 10 个而硬凑。

Current information must be verified in real-time: scope, official website, industry recognition, CAS partition, and papers from the last 3 months are all time-sensitive information and cannot rely on old memory.
Intermediate files are only allowed to be stored in the
```
.paper-select-journal/
```
hidden directory under the current working directory; only if the user explicitly specifies another directory can the default value be overwritten.
Set1 no longer relies on fixed semantic weights. The script is only responsible for minimum hard filtering and candidate pool sorting, and the real semantic planning is completed by the current host model.
Set1 is not the final answer. The final report only retains Set3 with sufficient evidence, up to 10 journals.
Do not recommend obviously warned, predatory journals or journals with an impact factor lower than
```
3
```
; if exceptions with IF lower than
```
3
```
are indeed retained, it must be stated "why it is still a reliable choice recognized by human experts in the field".
It is better to report fewer journals than to forcefully fill up 10 journals.

输入与工作区

Input and Workspace

用户需求可选，manuscript 必选。
manuscript 可来自粘贴的标题 / 摘要 / 全文片段，或本地
```
.md
```
、
```
.txt
```
、
```
.tex
```
、
```
.pdf
```
、
```
.docx
```
，也可混合提供。
一旦进入隐藏工作区流程，后续供脚本读取的
```
analysis/*.json
```
必须保留在当前 run 目录内；不要把
```
manuscript_profile.json
```
、
```
set2_scope_review.json
```
、
```
final_recommendations.json
```
指到 run 目录外。

先初始化隐藏工作区：

bash

python3 <skill_root>/scripts/init_workspace.py --project-root .

脚本会创建

.paper-select-journal/run-<timestamp>/

，其中至少包含：

```
inputs/
```
```
analysis/
```
```
candidates/
```
```
pubmed/
```
```
reports/
```

后续所有中间文件都必须留在该 run 目录内。

User preferences are optional, but manuscript is mandatory.
Manuscript can come from pasted title / abstract / full text fragments, or local
```
.md
```
,
```
.txt
```
,
```
.tex
```
,
```
.pdf
```
,
```
.docx
```
files, or a combination of these.
Once entering the hidden workspace process, subsequent
```
analysis/*.json
```
files for script reading must be kept within the current run directory; do not point
```
manuscript_profile.json
```
,
```
set2_scope_review.json
```
,
```
final_recommendations.json
```
to outside the run directory.

First initialize the hidden workspace:

bash

python3 <skill_root>/scripts/init_workspace.py --project-root .

The script will create

.paper-select-journal/run-<timestamp>/

, which contains at least:

```
inputs/
```
```
analysis/
```
```
candidates/
```
```
pubmed/
```
```
reports/
```

All subsequent intermediate files must remain within this run directory.

工作流

Workflow

1. 先写 manuscript 画像

1. Write Manuscript Profile First

完整理解论文后，把结果写入

analysis/manuscript_profile.json

。

模板：

templates/manuscript_profile.template.json

写法：
```
references/manuscript-profile.md
```
最低字段：
```
title
```
```
abstract
```
```
keywords
```
```
manuscript_summary
```

画像的作用是帮助 AI 理解稿件，而不是喂给固定打分公式。如果用户偏好复杂，优先把偏好写成自然语言放进

target_journal_brief

或

notes

，不要为了脚本凑很多硬编码线索。如果确实需要保留低 IF 的人工例外期刊，只能作为后续人工补录候选，并且必须在最终报告里解释“为什么它虽然低于阈值，仍是领域内稳妥选择”。

After fully understanding the paper, write the results into

analysis/manuscript_profile.json

Template:

templates/manuscript_profile.template.json

Writing guide:
```
references/manuscript-profile.md
```
Minimum fields:
```
title
```
```
abstract
```
```
keywords
```
```
manuscript_summary
```

The purpose of the profile is to help AI understand the manuscript, not to feed into a fixed scoring formula. If user preferences are complex, prioritize writing the preferences as natural language into

target_journal_brief

notes

, rather than forcing many hard-coded clues for the script. If manual exception journals with low IF need to be retained, they can only be used as subsequent manual supplementary candidates, and it must be explained in the final report "why it is still a reliable choice in the field despite being below the threshold".

2. 用内置

2023IF.xlsx

做 Set1 候选池

2. Generate Set1 Candidate Pool with Built-in

2023IF.xlsx

内置目录：

assets/journal_catalog/2023IF.xlsx

运行：

bash

python3 <skill_root>/scripts/shortlist_journals.py \
  --workspace .paper-select-journal/run-<timestamp> \
  --profile .paper-select-journal/run-<timestamp>/analysis/manuscript_profile.json

产物：

```
candidates/set1_candidates.json
```
```
candidates/set1_candidates.md
```

这里的脚本只做最小硬过滤：

影响因子下限
用户明确排除的期刊
基础元数据整理（JIF、分区、OA 比例、引用量）

不要把这一步输出误解为“已经按语义排好序的最终 shortlist”。你必须读取该候选池，再结合 manuscript 自主规划真正值得进入 Set2 的期刊。

Built-in directory:

assets/journal_catalog/2023IF.xlsx

Run:

bash

python3 <skill_root>/scripts/shortlist_journals.py \
  --workspace .paper-select-journal/run-<timestamp> \
  --profile .paper-select-journal/run-<timestamp>/analysis/manuscript_profile.json

Outputs:

```
candidates/set1_candidates.json
```
```
candidates/set1_candidates.md
```

The script only performs minimum hard filtering here:

Impact factor lower limit
Journals explicitly excluded by users
Basic metadata sorting (JIF, partition, OA ratio, citation count)

Do not misunderstand this step's output as "a final shortlist already sorted by semantics". You must read the candidate pool, then combine with the manuscript to independently plan the journals that are truly worthy of entering Set2.

3. 联网核验 scope、官网、分区与质量，得到 Set2

3. Verify Scope, Official Website, Partition and Quality via Internet to Get Set2

根据

candidates/set1_candidates.json

与 manuscript 画像，自主决定先核验哪些候选，并逐个联网核验：

官方网站
Aims & Scope
中科院小类及其分区
业内认可度
是否存在预警 / 垃圾期刊信号

优先使用：

期刊官网
PubMed / NLM
主流出版社页面
可信的分区信息来源

把通过核验的期刊写入：

```
analysis/set2_scope_review.json
```

模板：

templates/scope_review.template.json

核验口径：

references/journal-quality-checklist.md

Based on

candidates/set1_candidates.json

and the manuscript profile, independently decide which candidates to verify first, and verify each one via the internet:

Official website
Aims & Scope
CAS small category and its partition
Industry recognition
Whether there are warning / predatory journal signals

Priority sources:

Journal official website
PubMed / NLM
Mainstream publisher pages
Trusted partition information sources

Write the verified journals into:

```
analysis/set2_scope_review.json
```

Template:

templates/scope_review.template.json

Verification criteria:

references/journal-quality-checklist.md

4a. 抓取 Set2 最近 3 个月 PubMed 原始论文证据

4a. Fetch Raw PubMed Paper Evidence of Set2 from the Last 3 Months

运行：

bash

python3 <skill_root>/scripts/fetch_pubmed_recent.py \
  --workspace .paper-select-journal/run-<timestamp> \
  --profile .paper-select-journal/run-<timestamp>/analysis/manuscript_profile.json \
  --scope-review .paper-select-journal/run-<timestamp>/analysis/set2_scope_review.json

产物：

```
pubmed/recent_articles.json
```
```
pubmed/recent_articles.md
```

这里只提供原始证据，不负责打分或排序。脚本只做 API 调用、XML 解析和按日期整理。

Run:

bash

python3 <skill_root>/scripts/fetch_pubmed_recent.py \
  --workspace .paper-select-journal/run-<timestamp> \
  --profile .paper-select-journal/run-<timestamp>/analysis/manuscript_profile.json \
  --scope-review .paper-select-journal/run-<timestamp>/analysis/set2_scope_review.json

Outputs:

```
pubmed/recent_articles.json
```
```
pubmed/recent_articles.md
```

This only provides raw evidence and is not responsible for scoring or sorting. The script only performs API calls, XML parsing, and sorting by date.

4b. AI 评定主题相似性，决定哪些期刊进入 Set3

4b. AI Assess Theme Similarity to Decide Which Journals Enter Set3

你必须同时阅读：

```
analysis/manuscript_profile.json
```
```
pubmed/recent_articles.json
```

这里的“AI”指当前执行本 skill 的宿主模型本身：

Claude Code 中由当前 Claude 会话完成
Codex 中由当前 Codex 会话完成
不要为 Step 4b 额外调用外部 AI API、独立模型服务或单独打分脚本

也就是说，Step 4b 的规划、语义判断、Set3 去留决策和

set3_similarity_review.json

写入，都必须用当前工作环境已提供的 AI 算力原生完成。

逐个判断 Set2 期刊最近 3 个月论文与稿件在以下维度上的语义相关性：

主题是否真的对口，而不只是 token 碰撞
研究问题是否接近
方法学是否接近
相关论文数量与密度是否足以支持进入最终推荐

执行时先快速浏览全部 Set2 近期论文形成比较框架，再逐刊做语义判断，最后统一决定 Set3 去留并写出可复核理由。不要再把这一步退化成机械 token 打分或硬编码加权公式。

把结论写入：

```
analysis/set3_similarity_review.json
```

模板：

templates/set3_similarity_review.template.json

每个期刊至少要写：

```
journal_name
```
```
include_in_set3
```
```
similarity_assessment
```
```
relevant_articles
```
```
irrelevant_articles_count
```
```
overall_relevance_level
```

overall_relevance_level

只允许：

```
high
```
```
medium
```
```
low
```
```
none
```

You must read simultaneously:

```
analysis/manuscript_profile.json
```
```
pubmed/recent_articles.json
```

The "AI" here refers to the host model currently executing this skill:

Completed by the current Claude session in Claude Code
Completed by the current Codex session in Codex
Do not call external AI APIs, independent model services, or separate scoring scripts for Step 4b

That is, the planning, semantic judgment, Set3 retention decision, and writing of

set3_similarity_review.json

in Step 4b must all be completed natively using the AI computing power provided in the current working environment.

Judge the semantic relevance between the last 3 months' papers of each Set2 journal and the manuscript in the following dimensions:

Whether the theme is truly relevant, not just token collision
Whether the research questions are close
Whether the methodologies are close
Whether the number and density of relevant papers are sufficient to support inclusion in the final recommendation

When executing, first quickly browse all Set2 recent papers to form a comparison framework, then make semantic judgments for each journal one by one, and finally decide Set3 retention uniformly and write reviewable reasons. Do not degrade this step into mechanical token scoring or hard-coded weighting formulas.

Write the conclusions into:

```
analysis/set3_similarity_review.json
```

Template:

templates/set3_similarity_review.template.json

Each journal must include at least:

```
journal_name
```
```
include_in_set3
```
```
similarity_assessment
```
```
relevant_articles
```
```
irrelevant_articles_count
```
```
overall_relevance_level
```

overall_relevance_level

only allows:

```
high
```
```
medium
```
```
low
```
```
none
```

5. 形成最终推荐 JSON

5. Form Final Recommendation JSON

基于

analysis/set3_similarity_review.json

，把最终最多

个期刊写入

analysis/final_recommendations.json

。

模板：

templates/final_recommendations.template.json

字段说明：
```
references/report-schema.md
```

必须保留：

影响因子
中科院小类及其分区
业内认可度
官方网站
为什么推荐
最近 3 个月类似主题论文
每篇证据论文的 AI
```
relevance
```

Based on

analysis/set3_similarity_review.json

, write up to

final journals into

analysis/final_recommendations.json

Template:

templates/final_recommendations.template.json

Field description:
```
references/report-schema.md
```

Must retain:

Impact factor
CAS small category and its partition
Industry recognition
Official website
Reasons for recommendation
Similar theme papers from the last 3 months
AI
```
relevance
```
for each evidence paper

6. 渲染最终 Markdown 报告

6. Render Final Markdown Report

运行：

bash

python3 <skill_root>/scripts/render_report.py \
  --workspace .paper-select-journal/run-<timestamp> \
  --final-json .paper-select-journal/run-<timestamp>/analysis/final_recommendations.json

最终输出：

```
reports/paper-select-journal-report.md
```

如需

--output

覆盖默认文件名，也只能写到当前 run 目录内部，不能把最终 Markdown 报告写到隐藏工作区之外。

Run:

bash

python3 <skill_root>/scripts/render_report.py \
  --workspace .paper-select-journal/run-<timestamp> \
  --final-json .paper-select-journal/run-<timestamp>/analysis/final_recommendations.json

Final output:

```
reports/paper-select-journal-report.md
```

If using

--output

to overwrite the default file name, it can only be written into the current run directory, and the final Markdown report cannot be written outside the hidden workspace.

最终报告要求

Final Report Requirements

所有期刊写在同一个 Markdown 文件里
每个期刊使用
```
#
```
层级，下面按需用
```
##
```
、
```
###
```
每个期刊都要写明：影响因子、中科院小类及分区、业内认可度、官方网站、推荐理由，以及最近 3 个月类似主题论文表格和 AI 相关性说明

All journals are written in the same Markdown file
Each journal uses
```
#
```
level, with
```
##
```
,
```
###
```
as needed below
Each journal must clearly state: impact factor, CAS small category and partition, industry recognition, official website, recommendation reasons, as well as a table of similar theme papers from the last 3 months and AI relevance explanations

决策规则

Decision Rules

scope 不匹配，再高 IF 也不要强推
有明显预警 / 垃圾期刊风险，直接淘汰
近 3 个月没有相似主题论文，不一定淘汰，但推荐度要下调
```
include_in_set3
```
为
```
false
```
的期刊，不要进入最终推荐
中科院分区无法可靠核验时，优先换成信息更透明的候选
用户未明确偏好时，自主选择最稳妥方案，不要把提问变成阻塞

If scope does not match, do not force recommendation no matter how high the IF is
If there are obvious warning / predatory journal risks, eliminate directly
If there are no similar theme papers in the last 3 months, it is not necessarily eliminated, but the recommendation level should be lowered
Journals with
```
include_in_set3
```
as
```
false
```
should not be included in the final recommendation
When CAS partition cannot be reliably verified, prioritize replacing with candidates with more transparent information
When users do not have explicit preferences, independently choose the most reliable solution, and do not turn the question into a blocking issue

命令路径说明

Command Path Description

```
<skill_root>
```
表示当前 skill 的真实安装目录。
不要假设用户当前工作目录里一定有
```
paper-select-journal/
```
源码副本。
如果你已经处在 skill 根目录，也可以直接运行
```
python3 scripts/...
```
。

```
<skill_root>
```
refers to the actual installation directory of the current skill.
Do not assume that the user must have a source code copy of
```
paper-select-journal/
```
in the current working directory.
If you are already in the skill root directory, you can also run
```
python3 scripts/...
```
directly.

参考文件

Reference Files

```
references/manuscript-profile.md
```
```
references/journal-quality-checklist.md
```
```
references/report-schema.md
```

```
references/manuscript-profile.md
```
```
references/journal-quality-checklist.md
```
```
references/report-schema.md
```

paper-select-journal

Original

Translation

Paper Select Journal

Paper Select Journal

核心原则

Core Principles

输入与工作区

Input and Workspace

工作流

Workflow

1. 先写 manuscript 画像

1. Write Manuscript Profile First

2. 用内置
`2023IF.xlsx`
做 Set1 候选池

2. Generate Set1 Candidate Pool with Built-in
`2023IF.xlsx`

3. 联网核验 scope、官网、分区与质量，得到 Set2

3. Verify Scope, Official Website, Partition and Quality via Internet to Get Set2

4a. 抓取 Set2 最近 3 个月 PubMed 原始论文证据

4a. Fetch Raw PubMed Paper Evidence of Set2 from the Last 3 Months

4b. AI 评定主题相似性，决定哪些期刊进入 Set3

4b. AI Assess Theme Similarity to Decide Which Journals Enter Set3

5. 形成最终推荐 JSON

5. Form Final Recommendation JSON

6. 渲染最终 Markdown 报告

6. Render Final Markdown Report

最终报告要求

Final Report Requirements

决策规则

Decision Rules

命令路径说明

Command Path Description

参考文件

Reference Files