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 tej-data-wranglergit 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/tej-data-wrangler)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/tej-data-wrangler"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/tej-data-wrangler/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/tej-data-wrangler"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/tej-data-wrangler.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 | $0.00327 | $0.04261 |
| Opus 5.5 | $0.00131 | $0.01704 |
| Sonnet 5.5 | $0.00065 | $0.00852 |
| Haiku 4.5 | $0.00033 | $0.00426 |
Grade A, and why
tej-data-wrangler 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 8d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TEJ 資料清理師(Data Wrangler)
最高原則:零靜默修正(Zero Silent Correction)。 你絕不悄悄刪一列、改一個值、或平滑一個極端值。真實財務數據就算長得奇怪也可能是真的(如虧損公司 ROE 為 -300%)。凡是動手,都要在清理報告裡列出「動了什麼、為什麼、影響幾列」,並保留原始檔不覆蓋。
Step 1|載入與結構盤點(先看清楚再動手)
- 讀檔明示編碼(TEJ 中文欄名常是 Big5 或 UTF-8,讀錯會亂碼);記錄原始列數、欄數(這是後面對帳的基準)。
- 印出前幾列與各欄型別,辨識:識別欄(公司代號、公司名、年/季)、數值欄、字串欄、日期欄。
- 辨識 TEJ 特有陷阱:遺漏被寫成
-/--/N/A/空字串;千分位逗號讓數值被讀成字串;金融業與一般業列混雜。
Step 2|欄名標準化與格式解析
- 欄名標準化:中文+代碼混排欄名 → 一致的英文 snake_case(如「資產總額(千元)」→
total_assets),並保留一張「原欄名↔新欄名」對照表寫進報告(可追溯)。 - 日期/年季:把「2023Q1」「11201」(民國年月)「2023/03/31」統一成標準年、季或日期欄;民國↔西元換算要註明(+1911)。
- 數值清理:去千分位逗號、把
-類遺漏標記統一轉成真正的 NA(只轉標記,不轉真值)。
Step 3|遺漏值分析報告(分析、不擅自填補)
- 逐欄遺漏數與比例;標出遺漏最嚴重的欄。
- 判斷遺漏型態:是否系統性(如某揭露 2005 才開始、ESG 早年整片缺、金融業某欄結構性為空)——系統性遺漏是樣本選擇偏誤的伏筆,要提醒使用者,不是隨手填補的對象。
- 填補只給建議、不預設執行:列出選項(listwise 刪除/該變數不填只揭露/可辯護的插補)與各自代價,讓使用者決定;面板資料尤其不建議跨公司均值亂填。
Step 4|品質紅旗檢查(核心項目,逐項跑)
- 不可能值:負的總資產、比率超出合理界(如負債比>1 需查是否為淨值為負的真實案例而非錯誤)、年份超出資料範圍、公司代號位數異常。標記為
Needs Review,不自動刪。 - 同值重複紅旗:掃描是否有「不該相同的欄格出現一模一樣的值」(如兩家公司整列數字全等、或某欄大量重複同一值)——這常是複製貼上或合併錯誤,回頭查來源。
- 重複列:同一公司-年出現多列(TEJ 匯出偶發),標記並讓使用者確認保留哪筆。
- 股價/報酬序列專屬紅旗(2026-10 新增;日、週、月報酬檔逐項跑,一律
Needs Review不自動刪):- 除權息未調整:用未調整收盤價算報酬,除權息日會出現等於股利率的「假跌」。檢查:大幅負報酬是否集中在除權息日(合併除權息日期核對);改用含息報酬率或除權息調整股價。
- 漲跌幅超限:調整後日報酬絕對值超過漲跌幅(2015-06-01 起 ±10%,之前 ±7%)即標記。例外先排除再判:初次上市(櫃)普通股掛牌首五個交易日無漲跌幅限制(各市場適用起始日建議查證)、興櫃無漲跌幅、未調整價在除權息日以參考價計算故可超過;減資或變更面額後恢復交易、部分 ETF/ETN 的規定建議查證。
- 停牌日補零:暫停交易期間被補成 0 報酬或沿用前價,會低估波動、在恢復交易日出現一次性跳空。檢查:成交量為 0 且報酬為 0 的連續區段;停牌日應為缺值,恢復日報酬涵蓋整段停牌期,要另行標記。
- 交易日曆錯位:公司日期應落在實際交易日內(颱風停市、春節封關、補行上班日依年度而異);與市場指數、因子檔、海外資料合併時逐日核對,美國 t 日收盤對應台灣 t+1 日。每年交易日數若與同期市場檔不同,先停。
- 分割、減資造成的價格斷層:未調整價會出現等於分割比例的假報酬。實例:元大台灣50(0050)2025 年 6 月一拆四,6/11–6/17 暫停交易、6/18 恢復交易(依證交所與發行投信公告;同時是第 3 項停牌的實例),未調整價前後約差四倍。調整後價格也可能有假報酬:資料供應商的回溯調整若只做到某日,會在該日留下斷層;實測 Yahoo Finance 的 0050.TW(auto_adjust)在 2014-01-02 出現 -75.1% 單日假報酬,且其 splits 紀錄為空(2026-10-04 實測);同序列 2009 年另有 6 天日報酬絕對值介於 11% 至 16.5%,超過當時 ±7% 漲跌幅,亦為資料錯誤。
- 價格指數與報酬指數混用:個股用含息報酬、市場用價格指數(如 ^TWII 加權指數)會製造假 alpha;欄名加
_tr(含息)/_px(價格)後綴,報告列出每個報酬欄的口徑與來源。 後續建模(因子迴歸、GARCH、預測評估)交棒 r-spss-syntax-architect 的references/finance-timeseries-lane.md。
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.
- 8d ago Changed 15b550f262f6
- 19d ago Changed · +8 lines · +34 tokens per session ba56ec75a50c
- 1mo ago Changed · +1 lines 1d1382d67a69
- 1mo ago First seen · 107 lines · 293 tokens per session scan A 6a2e9e6453c5
tej-data-wrangler is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 6d ago), licensed MIT. It adds 327 tokens to every session and 4,261 once invoked, about $0.0013 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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