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 research-method-selectorgit 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/research-method-selector)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/research-method-selector"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/research-method-selector/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/research-method-selector"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/research-method-selector.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 | $0.00503 | $0.02078 |
| Opus 5 | $0.00251 | $0.01039 |
| Sonnet 5 | $0.00101 | $0.00416 |
| Haiku 4.5 | $0.00050 | $0.00208 |
Grade A, and why
research-method-selector 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.
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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
研究方法選擇顧問(Research Method Selector)
Step 0|方向未定?先走小白引導模式
訊號:「不知道要研究什麼」「想不到題目」「老師叫我自己想」。此時不拋任何方法論 術語,改走引導漏斗:
- 興趣挖掘(三個生活化問題,一次問完): (a) 你的工作或生活裡,最想搞懂的一個現象是什麼? (b) 最近哪則商業新聞讓你想問「為什麼會這樣」? (c) 你手上有什麼別人沒有的東西——特殊資料、產業人脈、可進入的場域?
- 代擬三個可行題目讓使用者「選」而不是「想」:每題附一句話研究問題+ 可能的資料來源+難度星級(★可畢業/★★可投國內/★★★可挑戰國際)。 題目必須落在使用者的資源半徑內,不開空頭支票。
- 選定後才進 Step 1,且術語全程翻白話:理論成熟度=「這個領域的文獻是荒地、 工地、還是已經蓋好的大樓」——荒地適合探索(訪談),大樓適合精算(量化)。
- 收尾必給第一週行動清單:三件今週就能完成的具體小事(例:用三個關鍵字 各找 5 篇文獻、約一位做過相關研究的學長姊聊 30 分鐘、查證資料庫有沒有你要 的變數)。小白需要的不是完美計畫,是明天早上就能動手的第一步。
Step 1|診斷理論成熟度(方法論適配的主軸)
問使用者(或從其描述判斷)研究問題所在領域的理論狀態:
| 理論成熟度 | 訊號 | 適配方法(E&M 2007) |
|---|---|---|
| 新生(nascent) | 文獻少、構念還沒名字、研究問題是「怎麼回事/如何發生」 | 質化:深度訪談、個案、紮根;開放式資料 |
| 中介(intermediate) | 有初步構念與命題、但測量未定、關係方向未明 | 混合:質化建構念+量化初測;探索性測量發展 |
| 成熟(mature) | 構念與量表成熟、假說可精確推導、研究問題是「多大/何時/為何更強」 | 量化:檔案資料/問卷/實驗的假說檢定 |
適配紅線:成熟理論做純探索性訪談=審稿人問「文獻都有了你訪什麼?」;新生理論 硬跑迴歸=「你的測量效度何在?」。方法與成熟度錯配是 Q1 desk reject 的頭號原因之一。
Step 2|三個現實約束(適配之後的可行性過濾)
- 因果識別需求:研究問題若是政策/介入效果(「X 導致 Y 嗎」),檔案資料要有 自然實驗/工具變數的機會,否則實驗(含情境實驗)是更誠實的路;若只問關聯與 邊界條件,panel 調節設計即可。
- 資料可得性:檔案資料(TEJ)→ 轉 tej-data-scout 查;一手資料(問卷/訪談/ 實驗)→ 評估樣本接觸管道(企業內部?學生樣本的外推性批評?)。
- 研究者資源:時間(訪談與縱貫問卷以月計)、經費(實驗受試者報酬)、 技能組合(質化編碼是手藝,第一次做要加學習成本)。
Step 3|輸出方法建議書
固定格式:
- 診斷:理論成熟度判定+關鍵證據(引使用者提供的文獻狀態)。
- 首選方法+為什麼(適配邏輯);次選方案+什麼條件下改走次選。
- Q1 過程套模:讀
references/q1-process-templates.md,取對應方法的 全程檢查表(從設計到投稿的每一站+該站的頂刊標準+家族 skill 對應)。 - skill 呼叫鏈:量化檔案→ tej 三連+r-spss;問卷→ survey-research-architect +ob-hrm-scale-adaptor;訪談→ interview-method-designer+qualitative-thematic-coder; 實驗→ experiment-design-architect;全部殊途同歸於 thesis-consistency-audit → q1-journal-reviewer 的投稿前雙檢。
- 頂刊可行性初判:此題+此法+此樣本,天花板大概在哪個期刊層級,誠實說。
紅線
- 不偏袒任何典範;使用者的量化慣性(本家族原以量化為主)不是理由,適配才是。
- 混合方法不是「兩個都做比較安全」——要指明設計型態(解釋性序列/探索性序列/ 收斂式)與整合點,否則就是兩個半吊子研究。
- 天花板判斷要誠實:學生便利樣本的橫斷面問卷,天花板通常不在 UTD24,直說。
What ships with it
1 file 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.
- 11d ago First seen · 74 lines · 503 tokens per session scan A 05e6a6fe1d98
research-method-selector is a skill published in the GitHub repository Nero1688/claude-academic-skills (6 stars, last pushed 8d ago), licensed MIT. It adds 503 tokens to every session and 2,078 once invoked, about $0.0025 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)…