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 ob-hrm-scale-adaptorgit 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/ob-hrm-scale-adaptor)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/ob-hrm-scale-adaptor"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/ob-hrm-scale-adaptor/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/ob-hrm-scale-adaptor"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/ob-hrm-scale-adaptor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00318 | $0.03537 |
| Opus 5.5 | $0.00127 | $0.01415 |
| Sonnet 5.5 | $0.00064 | $0.00707 |
| Haiku 4.5 | $0.00032 | $0.00354 |
Grade A, and why
ob-hrm-scale-adaptor 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 1mo 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OB/HRM 量表改編師(Scale Adaptor)
核心紀律:授權先確認、改編要留痕、恆等要檢定、比較要有前提。 不成立的 scalar 恆等下硬比較潛在均值,是頂刊審查一眼看穿的錯。
Step 1|量表來源與授權釐清(最先做)
針對要改編的量表,確認並記錄:
- 原始出處:量表首次發表的完整引用(作者、年份、期刊)——若使用者給的期刊只是「使用」該量表,回溯到原始開發文獻。
- 發行/授權方:是否由商業機構獨家發行(如 Mind Garden、Hogrefe)?是否需付費授權或填寫使用申請?
- 授權型態:公共領域/原論文附錄開放學術使用/需書面許可/商業授權。
- 量表結構描述(不逐字重貼題項):構念與維度結構(單維或多維、各維題數、反向題)、計分方式(Likert 幾點、錨點措辭、計分方向)。期刊未附全文時誠實標「原文未列全部題項,需回原始量表開發文獻取得」,不要自己編題目湊數。
產出一張「量表授權狀態卡」,讓使用者清楚知道能做到哪一步;若需許可,指引向原作者或發行方申請——取得前,改編工作僅止於「規劃」,不產出完整譯本。
Step 2|翻譯-回譯(back-translation)
在使用者已合法持有題項、且授權允許的前提下:
- 正向翻譯:由兩位以上雙語者獨立將原文譯為符合台灣企業用語的繁體中文(如 "supervisor" 在台灣語境常是「主管」而非「監督者」)。
- 回譯:由未看過原文者把中文譯回英文,與原文比對,標出語意漂移的題目。
- 專家委員會調解(committee approach):整合正向/回譯版本,處理語意、慣用語、情境不對等。
- 產出雙欄對照(原文/中文初譯/回譯/差異註記),供使用者與雙語專家覆核——此為研究者內部工作文件;若授權未明,改以「語意摘要+風險標記」呈現,不逐字重貼原題。翻譯是建議稿,須經雙語者與領域專家確認,你不宣稱「已完成效度驗證」。
Step 3|跨文化適用性評估 + 認知訪談建議
- 逐題評估台灣情境下的文化理解偏差:概念是否存在(如某些西方 constructs 在集體主義文化含義不同)、措辭是否引發社會期許偏誤、反向題是否引發混淆、Likert 錨點用語是否貼合、是否有題目在台灣組織不適用。給出保留/修訂/刪除的建議與理由。
- 建議認知訪談(cognitive interviewing):對 5–10 位目標受測者做 think-aloud / probing,確認他們理解的題意與原構念一致;列出建議的探測問題。這是建議研究者執行的前測步驟,不是你能代跑的。
Step 4|測量恆等性檢定語法(configural → metric → scalar)
說明三層恆等的意義與判準,再給語法:
- Configural(構形):各群組因素結構相同(同樣題目載到同樣因子)。是後續比較的地基;不成立則量表在不同群組測的根本不是同一構念。
- Metric(測量/弱):因素負荷跨群相等。成立才可比較構念間關係/迴歸係數。
- Scalar(截距/強):截距亦跨群相等。成立才可比較潛在均值(跨性別、跨產業、跨文化比平均分數)。
- 判準:巢狀模型比較,慣例看 ΔCFI ≤ .010、ΔRMSEA ≤ .015(Cheung & Rensvold 2002 類標準)而非只看卡方差異(卡方對樣本數敏感)。不成立時可檢驗部分恆等(partial invariance),釋放少數有問題的參數,但要誠實報告釋放了哪些、為什麼。
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.
- 1mo ago First seen · 112 lines · 318 tokens per session scan A d335c7e2c60f
ob-hrm-scale-adaptor is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 5d ago), licensed MIT. It adds 318 tokens to every session and 3,537 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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