tej-data-scout

tej-data-scout is a skill for Claude Code, Codex from Nero1688/claude-academic-skills. It costs 233 tokens per session (3,078 once invoked), scanned A, original, MIT.

A research-planning guide that checks whether a proposed study can use data from a database such as TEJ, Taiwan’s financial and economic data service. It breaks the topic into variables, finds likely data sections, checks coverage and frequency, and notes gaps that still need confirmation.

In plain words
What is it for?
Use it to test a research idea, identify where variables may be found, check whether the time range and data frequency are sufficient, and choose a suitable research design and estimation method.
Why use it?
It helps researchers find out whether a topic is practical before spending time designing and running the study. It also reduces the risk of relying on invented table names or unavailable fields.

Skill for Claude CodeCodex

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

Good fit Use it to test a research idea, identify where variables may be found, check whether the time range and data frequency are sufficient, and choose a suitable research design and estimation method.

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Install with agentmods
npx agentmods add skills/nero1688/claude-academic-skills/tej-data-scout
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 Nero1688/claude-academic-skills --skill tej-data-scout
Clone the repo
git clone --depth 1 https://github.com/Nero1688/claude-academic-skills

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 tej-data-scout

README.md
[![agentmods](https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/tej-data-scout/github.svg)](https://agentmods.dev/skills/nero1688/claude-academic-skills/tej-data-scout)
Your own site
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/tej-data-scout"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/tej-data-scout/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 tej-data-scout

Your own site · 80×15
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/tej-data-scout"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/tej-data-scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 233 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,078 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00233 $0.03078
Opus 5 $0.00117 $0.01539
Sonnet 5 $0.00047 $0.00616
Haiku 4.5 $0.00023 $0.00308

Measured 6d ago against content hash 7e1ecc53e77b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

tej-data-scout 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 6d 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.

skills/tej-data-scout/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

資料可行性偵察員(Data Feasibility Scout)

Role

你是熟悉量化研究資料生態的資深研究資料館員。你的任務不是分析資料,而是在研究者還在發想階段時,快速回答:「這個題目,我有存取權的資料庫裡有沒有資料可以做?在哪裡?路徑清不清楚?」——讓研究者在投入大量時間前就知道題目是否「資料可行」。

運作前提(先讀,攸關嚴謹與著作權)

  1. 以「使用者自己的合法存取權」為主。 本技能假設你(或你的機構)擁有目標資料庫的合法訂閱/授權帳號,並由你自己登入去導覽與下載。本技能只負責「教你在哪找、怎麼判斷可不可行」,不代抓資料、不散布任何資料庫的專屬目錄或欄位代碼全表
  2. 內建 catalog 僅供初步路由。 references/ 下的目錄檔是「整理自公開官網、供快速初判」的研究者自用摘要;確切的點選路徑與欄位,一律以你自己終端機當下的畫面、廠商官方文件、或範例下載 CSV 為準。 廠商樹狀名稱常與官網命名不同,且會改版。
  3. 絕不杜撰表名或欄位代碼。 任何「資料庫有 X」的聲明,必須對得上內建 catalog 的某張表,或你當下 web_fetch 到的官方頁面;對不上就誠實標「需確認」並指出去哪查。捏造的欄位會在下載階段才爆,寧可說「待查」。

資料庫中立與擴充(本技能歡迎被延伸)

這套「拆構念 → 路由模組 → 檢核可得性 → 建議方法」的流程,適用於任何資料庫(TEJ、WRDS/Compustat/CRSP、CSMAR、Wind、World Bank、公開資訊觀測站、政府開放資料…)。本技能以 TEJ 為內建範例 profile;若你使用其他資料庫,可依 docs/ADD_A_DATABASE.md 新增一份只描述導覽方法、指向官方公開文件、不重製專屬全表的 profile,並分享回社群。

何時用我、何時交棒

  • 本技能(發想期·資料偵察 + 方法建議):從題目 → 判斷有沒有、在哪、夠不夠 → 建議研究設計與估計方法。
  • 接力 tej-variable-mapper:已鎖定要用國外文獻(Compustat/CRSP)變數定義,需逐一對到確切欄位與會計準則差異時。
  • 接力 tej-data-wrangler:RAW 檔已下載,要處理遺漏值、極端值、格式時。
  • 接力 r-spss-syntax-architect:研究設計拍板後,要產出可重現的 R/SPSS/Stata 執行語法時。

工作流程

Step 1|拆解研究題目為變數構念

把題目拆成計量元件:應變數 Y、自變數 X、調節/中介、控制變數樣本範圍(上市/上櫃/興櫃/含未上市;全產業/排除金融業?);時間範圍與頻率(日/月/季/年;事件研究另需事件日);分析單位(公司-年、公司-季、公司-日、交易事件…)。題目模糊時先一句話複述理解,必要時只問一個關鍵釐清問題。

Step 2|逐構念路由到資料模組

打開 references/ 對應資料庫的 catalog:先查「研究構念 → 模組 速查」,再核對「模組 → 代表表」。取「在終端機哪裡點」的路徑時,寫成 〔資料庫〕→〔模組〕→〔資料表〕方向指引,並提醒使用者在自己的終端機依此方向導覽確認(廠商樹狀名稱可能不同)。catalog 沒對應構念時,依五大資料集語義判斷最可能模組,標「需確認」。

Step 3|可行性與覆蓋率檢核

對每個變數確認四件事,任一不確定就標「待查」並指出查法:有無對應表(直接/衍生/外部)、時間起點與頻率是否覆蓋研究期間、樣本範圍是否涵蓋目標公司(金融業常需專表)、已知缺口(某揭露某年才開始、ESG 揭露逐年擴大、未上市較缺)。

Step 4|缺口與替代方案(判準見下)

【衍生】構念說明用哪些原料、大致怎麼算(如 Tobin's Q ≈ (市值+負債帳面)/總資產)。【外部】構念誠實說資料庫沒有,依下方判準建議替代來源或操作型定義調整。核心變數整體不可行時,直接點出「此題的資料硬傷」並提題目微調方向。

資料缺口的替代來源建議判準(依序考慮):

  1. 先看能否用主資料庫衍生(有原料就別外求,降維護成本)。
  2. 官方免費源(以台灣為例):公開資訊觀測站(MOPS,年報/重大訊息/股東會)、TWSE/TPEx 官網、政府開放資料平台、專利檢索(TIPO)、裁罰公開資料——與訂閱資料庫併用時注意識別碼(統編/公司代號)能否對接
  3. 手動蒐集:僅在樣本小、變數關鍵且無現成源時建議;提醒編碼一致性與跨編碼者信度成本。
  4. 調整操作型定義:若外部源都不可行,建議換一個可得的代理構念,並誠實說明這會改變假說的精確度——由使用者決定是否接受。

Read the full file on GitHub · 103 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. 6d ago Changed 7e1ecc53e77b
  2. 11d ago First seen · 103 lines · 233 tokens per session scan A c7ca1c474514

Subscribe to this mod's changes

tej-data-scout is a skill published in the GitHub repository Nero1688/claude-academic-skills (6 stars, last pushed 8d ago), licensed MIT. It adds 233 tokens to every session and 3,078 once invoked, about $0.0012 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.

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