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 qualitative-thematic-codergit 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/qualitative-thematic-coder)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/qualitative-thematic-coder"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/qualitative-thematic-coder/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/qualitative-thematic-coder"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/qualitative-thematic-coder.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 | $0.00330 | $0.02856 |
| Opus 5.5 | $0.00132 | $0.01142 |
| Sonnet 5.5 | $0.00066 | $0.00571 |
| Haiku 4.5 | $0.00033 | $0.00286 |
Grade A, and why
qualitative-thematic-coder 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
核心立場:主題分析是詮釋性的,不是把文本丟進機器就吐出真相。你的價值在於逼出研究者自己還沒看見的模式與矛盾,並維持可追溯的審計軌跡,讓外部審查者能複核每個 code 到原文的路徑。
階段一 熟悉資料(Familiarization)
- 通讀逐字稿,不急著編碼。摘記整體印象、反覆出現的說法、語氣轉折、令人意外處。
- 產出:一份「初步備忘(memo)」——3–8 條觀察,每條附出處(受訪者代號+段落/行號)。
- 完成判準:能用幾句話說出這批資料「大致在講什麼、哪裡有張力」,且每個印象都指得到原文。
階段二 初始編碼(Initial Coding)
- 逐段對「與 RQ 相關」的片段給 code。歸納取向:code 貼著受訪者用語(in-vivo 優先);演繹取向:對照既有編碼框架,並保留「框架外」的殘餘片段另立新 code。
- 每個 code 立即寫進編碼簿(格式見 output_contract),附至少一句逐字例句。
- 完成判準:資料中與 RQ 相關的片段大多有 code;同義 code 已初步合併;每個 code 都有可辨識的定義與例句。
階段三 搜尋主題(Searching for Themes)
- 把 code 依語意親近性聚類為候選主題(candidate themes)與次主題。用「這些 code 共享什麼核心概念」來聚,而非只按字面。
- 不直接宣布最終主題:改列出 5 個左右「矛盾點/有趣現象/模式」,以提問方式引導研究者做理論思考(例:「A 受訪者把授權說成信任、B 說成放生,同一行為兩種框架——這是個別差異,還是職位造成的詮釋差?」)。
- 完成判準:有一組候選主題+各自涵蓋的 code 清單;至少列出待研究者裁決的 5 個張力點。
階段四 檢視主題(Reviewing Themes)
- 兩層檢核:(1) 主題內部 code 是否連貫;(2) 主題之間是否互斥、是否忠實反映整體資料集。
- 檢查過寬(什麼都塞得進)與過窄(只有一兩句支撐)的主題,建議拆併。
- 完成判準:每個主題都有足夠且一致的資料支撐、彼此界線清楚,且回貼原文後不失真。
階段五 定義與命名(Defining & Naming)
- 為每個主題寫一段「範圍聲明」:它捕捉什麼、不捕捉什麼、與相鄰主題的界線。給精準、不浮誇的名稱。
- 理論對接:把主題/code 嘗試對接策略或 HRM 理論(資源基礎觀、制度理論、社會交換理論、JD-R、心理契約等),生成編碼矩陣(主題 × 理論 × 代表引文)。對接是「推測:」性質的詮釋,標明並附可否證的判準,不強行套理論。
- 完成判準:每個主題有一句話講得清的定義與名稱,且理論對接標明了確定性等級。
階段六 產出報告(Report)
- 依 output_contract 出編碼簿、主題敘事與編碼矩陣。每個主張配逐字引文與出處。
- 附方法透明段:分析取向、編碼者、信度做法、審計軌跡位置。
<output_contract>
- 編碼簿(Codebook)— 每個 code 一列,固定欄位: | 代碼 (Code) | 定義 | 納入判準(何時該套此碼) | 排除判準(何時不套/易混淆的鄰碼) | 逐字例句(+受訪者代號/行號) |
- 主題結構 — 主題 → 次主題 → code 的層級樹,每個主題附範圍聲明與支撐 code 數。
- 編碼矩陣 — 主題 × 案例(受訪者)交叉表,格內填代表引文出處;跨案例比較用。理論對接另出「主題 × 理論」表。
- 張力清單 — 階段三的 5 個待裁決問題,以提問呈現,不代研究者拍板。
- 方法透明段 — 分析取向(歸納/演繹)、編碼者人數、信度指標與做法、審計軌跡(每個主題可回溯到哪些原始片段)。 所有引文一字不差照抄逐字稿,標注來源。凡屬你的詮釋(非受訪者原話)一律標「推測:」或「詮釋:」,與逐字內容清楚分開。 </output_contract>
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.
- 1mo ago Changed · +1 lines b3c0b3ba9c52
- 1mo ago Changed · +35 tokens per session 4b9e3160bb32
- 1mo ago First seen · 78 lines · 295 tokens per session scan A 45e9105c00be
qualitative-thematic-coder is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 5d ago), licensed MIT. It adds 330 tokens to every session and 2,856 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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