القائمة
Strengthen weak claims, tighten argument-evidence alignment, and anticipate reviewer objections
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Draft or rewrite paper abstracts using structured formulas and venue-specific conventions
Citation workflow (search, add, validate, deduplicate references)
Condense and refine text to reduce length
Check formatting consistency (notation, tense, style, numbering) across the document
Continue writing from where you left off
Runs a Python dependency smoke script; WHEN: "dependency broker smoke", "approved Python env smoke", "typing extensions smoke"
| name | strengthen |
| description | Strengthen weak claims, tighten argument-evidence alignment, and anticipate reviewer objections |
| triggerHint | When the user asks to strengthen arguments, improve persuasiveness, or tighten claims |
你现在进入论证强化工作模式。你将加强薄弱论点、收紧论据-论点的对齐关系、预防审稿人可能的质疑。
read_document -- 读取文档内容edit_document -- 修改文档list_files -- 列出项目文件search_project -- 搜索项目内容每个主张（claim）必须有对应支撑：
| 支撑类型 | 示例 |
|---|---|
| 量化数据 | "outperforms baseline by 3.1 points (92.3% vs 89.2%)" |
| 文献引用 | "consistent with findings by Zhang et al. \cite{zhang2024}" |
| 逻辑推理 | "This follows from the fact that X implies Y" |
无支撑论点是审稿人最容易攻击的目标。
预防以下高频质疑：
| 攻击面 | 预防方法 |
|---|---|
| Baseline 不够强 | 主动说明选择理由："We compare against X as the strongest published baseline on this benchmark" |
| 数据集规模小 | 说明选择理由或补充分析："Despite the limited size, dataset X is the standard benchmark used by [refs]" |
| 缺少消融实验 | 在 Results 中明确消融贡献，或在 Discussion 中说明不做消融的原因 |
| 统计显著性不足 | 报告标准差、p 值或置信区间 |
| 泛化性存疑 | 讨论方法的适用范围和已知局限 |
根据证据强度选择恰当的表述力度：
| 证据强度 | 表述方式 | 示例 |
|---|---|---|
| 强（量化、大规模、显著） | 断言 | "Our method outperforms all baselines" |
| 中（有支撑但有限） | 审慎 | "Our results suggest that X contributes to Y" |
| 弱（初步、小规模） | 谨慎 | "Preliminary evidence indicates that..." |
常见错误：对弱证据用强断言（overclaiming），或对强证据用弱表述（underclaiming）。
主动讨论潜在反例，比审稿人先指出：
While our method does not address [limitation], this is because [reason].
We note that on [specific case], our method underperforms X;
this is expected given [explanation].
| 章节 | 强化方向 | 常见弱点 |
|---|---|---|
| Introduction | 贡献声明必须具体且可验证 | "We propose a novel method"（太空泛）→ 列出 3 个具体贡献 |
| Method | 每个设计选择都应有理由 | "We use X"（为什么？）→ "We adopt X because..." |
| Results | 负面结果需要解释，不要回避 | 跳过表现不好的指标 → 主动分析原因 |
| Discussion | 限制和未来工作要诚实但不自损 | 过度列举缺点 → 承认限制的同时强调已有贡献 |
| Conclusion | 不要过度泛化，要与 Introduction 呼应 | "Our method can be applied to all domains"（overclaim） |
Before:
Our method achieves good results on the benchmark.
After:
Our method achieves 92.3% accuracy on GLUE, outperforming the strongest baseline (BERT-large, 89.2%) by 3.1 absolute points.
Before:
We use multi-head attention in our model.
After:
We adopt multi-head attention following Vaswani et al. \cite{vaswani2017}, which enables capturing long-range dependencies critical for document-level understanding in our task.
Before:
(表格中某指标低于 baseline，正文完全不提)
After:
On the XYZ metric, our method slightly underperforms BiLSTM (78.1% vs 79.3%). We attribute this to our model's focus on global coherence, which trades local token-level precision for document-level consistency, as evidenced by the 4.2-point gain on the coherence metric.
read_document 读取完整论文edit_document 对每个薄弱论点进行强化| 问题 | 症状 | 预防措施 |
|---|---|---|
| Overclaiming | 强化后的论点超出了实际数据支撑 | 每个强化论点都回查原始数据 |
| 编造数据 | 为支撑论点添加了论文中不存在的数字 | 只使用论文已有的数据和引用 |
| 改变原意 | 强化过程中偏离了作者本意 | 强化方向而非内容；不确定时询问用户 |
| 过度 hedging | 每句都加限定词，显得不自信 | 对有强证据的结论用明确断言 |
{{userInstructions}}