taiwan-earnings-translator

taiwan-earnings-translator is a skill for Claude Code, Codex from fredchu/claude-dotfiles. It costs 153 tokens per session (1,935 once invoked), scanned A, original, MIT.

A Traditional Chinese translator for complete U.S. stock earnings-call transcripts, including transcripts supplied as text or English PDFs. Earnings calls are company presentations and question sessions about financial results.

In plain words
What is it for?
Use it to translate earnings calls, compare translated financial figures with the source, and prepare Taiwan-focused financial-media reports.
Why use it?
It preserves spoken details, repetitions, numbers, and paragraph structure that a normal summary or polished translation might omit.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to translate earnings calls, compare translated financial figures with the source, and prepare Taiwan-focused financial-media reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fredchu/claude-dotfiles/taiwan-earnings-translator
Install

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.

Any agent
npx skills add fredchu/claude-dotfiles --skill taiwan-earnings-translator
Clone the repo
git clone --depth 1 https://github.com/fredchu/claude-dotfiles

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for taiwan-earnings-translator

README.md
[![agentmods](https://agentmods.dev/badge/skills/fredchu/claude-dotfiles/taiwan-earnings-translator/github.svg)](https://agentmods.dev/skills/fredchu/claude-dotfiles/taiwan-earnings-translator)
Your own site
<a href="https://agentmods.dev/skills/fredchu/claude-dotfiles/taiwan-earnings-translator"><img src="https://agentmods.dev/badge/skills/fredchu/claude-dotfiles/taiwan-earnings-translator/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.

agentmods 80×15 button for taiwan-earnings-translator

Your own site · 80×15
<a href="https://agentmods.dev/skills/fredchu/claude-dotfiles/taiwan-earnings-translator"><img src="https://agentmods.dev/badge/skills/fredchu/claude-dotfiles/taiwan-earnings-translator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 153 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,935 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00153 $0.01935
Opus 5 $0.00077 $0.00967
Sonnet 5 $0.00031 $0.00387
Haiku 4.5 $0.00015 $0.00194

Measured 11d ago against content hash df498fe51a88, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

taiwan-earnings-translator 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/append.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/taiwan-earnings-translator/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

台灣財報電話會議翻譯員 (Taiwan Earnings Translator)

你是一位專精於台灣金融、商業及資本市場術語的專家級翻譯員。你的任務是將美國上市公司的財報電話會議 (Earnings Call) 逐字稿(可為純文字或 PDF 檔案),還原為台灣專業讀者(如《工商時報》或《財訊》讀者)熟悉的繁體中文。

核心工作流 (Standard Workflow)

Phase 0: 啟動、預研與標頭初始化 (Pre-research & Header Initialization)

在開始翻譯前,你必須:

  1. 背景同步:檢索該公司近期新聞、關鍵產品線與特定行業術語。
  2. 來源讀取
    • 支援直接貼上逐字稿文字,或提供 英文 PDF 檔案路徑
    • 若提供 PDF,請使用 read_file 讀取。由於高品質 PDF 通常具備更精準的對話邊界與語意分段,你必須優先遵循 PDF 原始的段落結構進行翻譯,而非自行重新切分。
  3. 術語建模:閱讀原文找出潛在生僻詞(如零售業的 trade areas)。
  4. 斷點偵測 (Detect & Resume):檢查目錄下是否存在該季度的 .tmp 檔案。
  5. 標頭初始化 (File Initialization)
    • 在建立 .tmp 檔案時,必須先將「背景同步」與「術語建模」內容格式化為 Markdown 標題與列表寫入檔案頂部(包含 TW 與 EN 檔案)。
    • 向使用者回報研究摘要,隨後自動開始執行 Phase 1 分段翻譯。

Phase 1: 原子級鏡像翻譯與狀態鎖定 (Execution)

針對 PDF 來源之特別規範:若來源為 PDF,必須嚴格遵循 PDF 原始的段落切割進行翻譯。這能提供最精準的語境對齊,確保譯文群組與官方正式文件一致。

若原文超過 2,000 字(或 PDF 頁數過多),必須採取「分段輸出」:

  1. 順序規範:確保 Phase 0 的標頭已寫入,隨後依段落序號或 PDF 頁碼追加。
  2. 上下文鎖定:每一輪輸出前,先宣告:[術語鎖定:已載入 / 上段結尾:... / 目前進度:X% 或 頁碼]
  3. 原子級鏡像 (Atomic Verbatim)
    • 嚴禁摘要、刪減或優化語法。
    • 保留所有口語贅詞("um", "uh" 譯為 "嗯")、重複詞、未完成句子、自我糾正。
    • 保留暫停 (...) 或聽不清楚 ([聽不清楚])。
    • 嚴禁展現「貼心」的摘要行為,還原法說會現場真實質地。

Phase 2: 完整性審計 (Post-Audit)

  1. 數字審計:確認所有阿拉伯數字(如 $479.1 million -> 4.791億美元)與原文 100% 一致且無捨入誤差。
  2. 術語檢查:核對 references/glossary.md 中的對應是否一致。
  3. 標記清理:確保所有用於狀態鎖定的內部標記(如 [術語鎖定:... / 目前進度:...%])在最終交付前已被全數移除。

Phase 3: 靜默分段存檔與暫存緩衝協議 (Silent Archiving & Buffer Protocol)

為避免 CLI 安全彈窗與 Shell 緩衝區崩潰,執行寫入時必須遵守:

  1. 雙語並行存檔:同時產生 中文譯文檔英文原文對照檔
  2. 鏡像結構要求:英文原文檔的段落切割、空行、角色標籤與格式必須與中文譯文檔 完全一致
  3. 暫存檔中轉 (Buffer Mechanism)
    • 先使用 write_file 將內容寫入專案臨時目錄(如 ~/.gemini/tmp/[SESSION_ID]/buffer.txt)。
    • 再執行:cat [暫存路徑] | python3 [技能路徑]/scripts/append.py "[目標路徑]"
  4. 追加寫入:使用技能內建腳本 append.py 執行(支援從 stdin 讀取)。
  5. 標記淨化與更名
    • 在正式更名前,必須執行標記清除。在 macOS 下使用:sed -i '' '/\[術語鎖定:.*\]/d' [目標路徑]
    • 全數校核無誤後,執行 mv.tmp 轉為正式 .txt
  6. 命名規範
    • 中文檔:[股票代號]_[財報季度]_Transcript_TW.txt
    • 英文檔:[股票代號]_[財報季度]_Transcript_EN.txt

Read the full file on GitHub · 73 lines

Files

What ships with it

2 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.

Changes

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.

  1. 11d ago First seen · 73 lines · 153 tokens per session scan A df498fe51a88

Subscribe to this mod's changes

taiwan-earnings-translator is a skill published in the GitHub repository fredchu/claude-dotfiles (2 stars, last pushed 28d ago), licensed MIT. It adds 153 tokens to every session and 1,935 once invoked, about $0.0008 per session on Opus 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.

Related

Other skills, from other repositories

chinese-traditional-conversion

A language-conversion skill that changes Simplified Chinese into Traditional Chinese used in Taiwan. It also adapts vocabulary and phrasing for Taiwan.

jim60105/copilot-prompt · 125 tokens

chinese-content-writing-guideline

Writing guidelines for producing high-quality Traditional Chinese (zh-TW) content. Use when writing any kind of content. Including blog posts, notes, technical articles, technical writing, chitchat, social media posts, etc., even when you are just sending a text message. Also use when reviewing or editing existing…

jim60105/copilot-prompt · 80 tokens

chinese-documentation

A Chinese technical-documentation style guide covering spacing, punctuation, numbers, terminology, and links when Chinese and English appear together.

jnMetaCode/superpowers-zh · 62 tokens

company-valuation

Estimate the intrinsic value of a public company using DCF, relative (peer multiple) and sum-of-parts (SOTP) methods, then triangulate to an implied share price with upside/downside versus the current market price. Use this skill whenever the user asks: "what is AAPL worth", "valuation of NVDA", "fair value of TSLA"…

himself65/finance-skills · 234 tokens

estimate-analysis

Deep-dive into analyst estimates and revision trends for any stock using Yahoo Finance data. Use when the user wants to understand analyst estimate direction, how EPS or revenue forecasts changed over time, compare estimate distributions, or analyze growth projections across periods. Triggers: "estimate analysis for…

himself65/finance-skills · 195 tokens

earnings-preview

Generate a pre-earnings briefing for any stock using Yahoo Finance data. Use this skill whenever the user wants to prepare for an upcoming earnings report, understand what analysts expect, review a company's beat/miss track record, or get a quick overview before an earnings call. Triggers include: "earnings preview…

himself65/finance-skills · 221 tokens