Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Nero1688/claude-academic-skills --skill bilingual-paper-readergit clone --depth 1 https://github.com/Nero1688/claude-academic-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nero1688/claude-academic-skills/bilingual-paper-reader)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/bilingual-paper-reader"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/bilingual-paper-reader/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/bilingual-paper-reader"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/bilingual-paper-reader.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00389 | $0.02709 |
| Opus 5.5 | $0.00156 | $0.01084 |
| Sonnet 5.5 | $0.00078 | $0.00542 |
| Haiku 4.5 | $0.00039 | $0.00271 |
Grade A, and why
bilingual-paper-reader scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
雙欄論文閱讀器(Bilingual Paper Reader)
這個 skill 實際上是什麼(先講清楚,避免誤解)
- 閱讀器是一支、論文是資料檔。
reader.html可重複使用,任何論文轉成 JSON 都用它開。 - 翻譯與預先標記需要你(Claude)在場。 閱讀器本身是靜態 HTML,沒有 LLM, 無法自己翻譯。每篇論文要開一次 session 讓你翻。
- 標記存在瀏覽器 localStorage,清除瀏覽器資料會一併清掉—— 重要筆記務必用「匯出筆記」存成 Markdown 備份。這點要主動告訴使用者。
- 掃描版 PDF(無文字層)抽不到文字,需先 OCR。工具會明確報錯,不會產出空檔。
與同族 skill 分工
| 需求 | 該用 |
|---|---|
| 一批文獻的橫向比較(建庫、矩陣、APA7、DOI) | literature-matrix-builder |
| 規模化文字量化(財報語調、主題模型、LLM 標註) | text-analytics-architect |
| 單篇方法論逆向、找研究缺口、系統性回顧 | phd-researcher |
| 單篇論文的雙語精讀與重點標記 | 本 skill |
精讀完的判斷(核心主張/創新點/弱點)可回填進 literature-matrix-builder
的綜整欄——兩支是天然接力。Claude Code 環境呼叫同族技能須加 anthropic-skills: 前綴。
工作流程
Step 1|PDF → 論文資料檔
python scripts/pdf_to_paper.py paper.pdf -o paper.json --title "論文標題"
會逐頁抽取並切段,每段記錄起始頁(page 欄,是 PDF 的頁序,不等於紙本印刷頁碼);
剔除跨頁重複的頁首頁尾、把被換頁切斷的段落接回,並標出不需翻譯的段落:
圖說(skip: caption)與疑似表格(skip: likely_table)。
若報錯說抽不到文字,代表是掃描版 PDF,請使用者先 OCR。
腳本結尾會列出剔除了哪些頁首頁尾、丟棄了多少短片段、有幾段圖說;
這幾行要看過再往下做,異常時可加 --no-strip-repeated 或調低 --min-chars 重轉。
Step 2|先建術語對照表
翻譯前先掃一遍全文,把反覆出現的關鍵術語列進 JSON 的 glossary。
這一步不能跳過——術語不一致是學術翻譯最常見也最傷的錯誤。
規範見 references/translation-discipline.md。
Step 3|逐段翻譯
填每段的 trans。紀律見 references/translation-discipline.md,重點:
- 一段對一段,不合併不拆分
skip有值的段落(圖說、疑似表格)不翻、不標色,trans留空;但要在「處理狀態」如實計數- 人名/期刊名/統計符號/變數名不翻
- 數字、係數、p 值原樣照抄(數字零容忍)
- 因果強度用詞不可弱化或加強(
suggests≠ 證明) - 看不懂就標「原文語意不明確,暫譯為…」,不要硬翻出通順的錯譯
翻譯量要先講:全文常有 50–120 段。先問使用者要全翻還是先翻 摘要/引言貢獻段/方法/結論這四塊,不要默默決定。
Step 4|五色預先標記
填每段的 marks。分類判準與位移算法見 references/highlight-taxonomy.md。
寧少勿濫——每段都標等於沒標。
Step 5|交付與判斷
告訴使用者怎麼開,並附上你的判斷(比標記更有價值):
## 這篇的關鍵判斷
- 核心主張:〔一句話〕
- 最創新之處:〔一句話;貢獻宣稱不明確就直說〕
- 最大弱點:〔區分作者自承 vs 你讀出來的〕
- 與你研究的關聯:〔一句話〕
輸出格式
# 精讀:〔論文標題〕
## 處理狀態
- 段落 N 段,涵蓋原文第 1–P 頁(P 取自 JSON 的 page_count 或最大的 page;沒有頁碼就寫「無頁碼」)
- 實際讀過:第 a–b 頁;章節〔章節名稱〕(只填你確實逐段讀過、翻過或判斷過的頁與章節)
- 未讀:第 c–d 頁;章節〔章節名稱〕(沒有未讀的就寫「無」)
- 跳過:圖說 X 段、表格 Y 段(未翻譯,圖表內容請使用者對照原文)
- 已翻譯:〔全文/指定章節〕
- 預先標記:核心 a、創新 b、方法 c、限制 d、金句 e
## 這篇的關鍵判斷
〔如上四點〕
## 怎麼開
1. 用瀏覽器開啟 scripts/reader.html
2. 把 〔paper.json 路徑〕 拖進去(每段標題旁的 p.N 是該段的起始頁)
3. 選取文字可自己畫螢光筆;按「匯出筆記」存成 Markdown(每筆帶段落編號與頁碼)
## 提醒
- 標記存在瀏覽器 localStorage,清除瀏覽器資料會消失,重要筆記請匯出備份
- 〔翻譯中標為語意不明確的段落,請對照原文第 N 頁確認;N 依該段的 page 欄填寫。
page 欄缺漏或為 null 時改寫段落編號 ¶i,並說明這份資料沒有頁碼〕
- page 是 PDF 的頁序,不是紙本印刷頁碼,兩者可能差好幾頁;使用者翻紙本時要先說明
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +15 lines 40fc74c129ad
- 1mo ago Changed · +3 lines 322d939836fd
- 1mo ago First seen · 110 lines · 389 tokens per session scan A f8b24671388c
bilingual-paper-reader is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 5d ago), licensed MIT. It adds 389 tokens to every session and 2,709 once invoked, about $0.0016 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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