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 global-opendata-scoutgit 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/global-opendata-scout)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/global-opendata-scout"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/global-opendata-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.
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/global-opendata-scout"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/global-opendata-scout.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 | $0.00433 | $0.02240 |
| Opus 5 | $0.00217 | $0.01120 |
| Sonnet 5 | $0.00087 | $0.00448 |
| Haiku 4.5 | $0.00043 | $0.00224 |
Grade A, and why
global-opendata-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 7d 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.
跨國公開資料偵察員（Global OpenData Scout）
⚠️ 台灣使用者最該先知道的一件事
World Bank 沒有台灣資料（2026-07-26 實測確認：TW／TWN 皆查無，
295 個國家／地區清單中無 Taiwan）。OECD 亦然（非會員）。
所以做「台灣 vs 其他國家」的比較時，必然要混用兩個來源：
台灣走官方來源（主計總處／央行／勞動部），
其餘國家走本 skill。合併時的定義一致性與來源譜系，交棒
anthropic-skills:multi-source-data-integrator，並在論文方法節誠實揭露。
細節見 references/cross-country-cautions.md 第零節。
與同族 skill 分工
| 需求 | 該用 |
|---|---|
| 台灣官方統計、不動產、勞動、調查資料庫 | 台灣官方統計來源 |
| 台灣公司揭露、事件研究事件源 | public-disclosure-scout |
| 多國多源撈回來要合併對接 | multi-source-data-integrator |
| 台灣以外的國家、跨國比較資料 | 本 skill |
核心原則
- 端點必須標實測狀態。 本 skill 的 references 對每個端點都標 ✅已實測／⚠️待確認。待確認的絕不寫進腳本。 網路上流傳的國際組織端點大量過時——本次建置實測就推翻了三個 （ILOSTAT 舊 base 已失效、IMF 的 dataservices 已死、OECD v2 路徑 404）。
- 金鑰一律走環境變數。 FRED 需要 key，因此刻意不內建； 要用請自行以環境變數提供，絕不寫進程式碼或產出檔。
- 可比性優先於可得性。 找到數字不等於能用。給資料源的同時， 必須主動點出該比較的陷阱（幣別、基期、會計年度、產業分類、涵蓋率）。
- 不編造國家統計機構。 不確定某國有沒有某項統計，就說「需查證」
並給查證方法（
references/method-find-country-data.md的五步法）， 不要生一個看似合理的機構名或網址。 - 記錄抓取日期。 總體統計會被回溯修訂，同一年的 GDP 隔幾年抓會不同。
工作流程
Step 1｜確認研究涵蓋哪些國家、哪些變數
特別確認：有沒有台灣（有的話就要混源）、時間範圍、分析單位（國家-年？國家-產業-年？）。
Step 2｜路由到資料源
先判斷你要的是「官方統計」還是「事件／風險資料」，兩者查不同的 catalog。
A. 國家層級官方統計 → references/catalog-international-orgs.md
| 你要的 | 建議源 |
|---|---|
| 跨國總體（GDP、人口、貿易） | World Bank ✅ |
| 跨國勞動／薪資／就業 | ILOSTAT ✅ |
| 歐盟細部區域統計 | Eurostat ✅ |
| 國際收支、政府財政 | IMF ✅ |
| 美國深度時間序列 | FRED（需 key，未內建） |
| 某特定國家的細項統計 | 走 references/method-find-country-data.md 五步法 |
💡 做「台灣 vs 他國」的公司層級比較：台灣走
public-disclosure-scout（MOPS）， 美國走 SEC EDGAR，兩邊合併交棒multi-source-data-integrator。 國際組織的統計顆粒度太粗，做不了公司層級比較。
🚨 用貿易資料前必讀：台灣在 UN Comtrade／WITS 沒有獨立國碼， 被併入
490「Other Asia, nes」。實測：查 490 有 218 筆、查 158 是 0 筆但不報錯—— 極易誤判「沒有台灣資料」。
Step 3｜撈取
python scripts/intl_fetch.py wb --search "gdp per capita" # 先找指標代碼
python scripts/intl_fetch.py wb --indicator NY.GDP.MKTP.CD --countries US,JP,DE --start 2015 --end 2022 -o gdp.csv
python scripts/intl_fetch.py sdmx --list-providers
python scripts/intl_fetch.py sdmx --provider ilostat --resource dataflow -o flows.xml
What ships with it
6 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.
- 7d ago Changed · +12 lines · -12 tokens per session 7c8b200a9640
- 12d ago First seen · 116 lines · 445 tokens per session scan A 6d84247448f8
global-opendata-scout is a skill published in the GitHub repository Nero1688/claude-academic-skills (6 stars, last pushed 9d ago), licensed MIT. It adds 433 tokens to every session and 2,240 once invoked, about $0.0022 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.
Other skills, from other repositories
alterlab-deep-research
Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology…
alterlab-paper-writer
Drafts and revises academic papers through a 12-agent pipeline with hardened LaTeX output (apa7 document class, justified text, table column-width formula, centered bilingual abstracts, standardized font stack, PDF compiled from LaTeX), supporting IMRaD, literature review, theoretical, case study, policy brief, and…
alterlab-research-pipeline
Orchestrates the full academic research pipeline (research, write, integrity check, review, revise, re-review, re-revise, final integrity check, finalize), coordinating alterlab-deep-research, alterlab-paper-writer, and alterlab-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-paper-reviewer
Simulates a full multi-reviewer journal review PANEL — 5 personas (Editor-in-Chief + 3 peer reviewers + a Devil's Advocate) debate a manuscript and produce a consensus Editorial Decision (accept/minor/major/reject) plus a prioritized Revision Roadmap. Modes: full, re-review (verify revisions addressed prior comments)…