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 management-figuregit 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/management-figure)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/management-figure"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/management-figure/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/management-figure"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/management-figure.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 | $0.00374 | $0.02496 |
| Opus 5 | $0.00187 | $0.01248 |
| Sonnet 5 | $0.00075 | $0.00499 |
| Haiku 4.5 | $0.00037 | $0.00250 |
Grade A, and why
management-figure 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 12d 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.
What it actually says
階段一:這張圖回答哪個假設(先診斷) 釐清圖要檢驗的假設(H1/H2…)、對應的迴歸式與表格。圖必須與假設、迴歸式、表格三方對齊(延續使用者的符號一致性紀律)。判準:能一句話說出「這張圖讓審查委員看到 H_k 成立/不成立」。
階段二:帶入真實數據,選對圖種
用 scripts/mgmt_figures.py(自包含,僅需 numpy+matplotlib;有 statsmodels 則用於信賴帶)。出圖前先讀 references/figure_style.md 確認版面規範。以使用者的真實迴歸輸出/係數帶入,勿用模擬值冒充:
quadratic_turning_point_plot(x, y, ...)— 二次式倒U/U,自動 OLS 二次擬合、95% CI、標轉折點 x*=-b1/(2b2)。用於非線性假設(如 ESG×績效²、INDIR²)。回傳含p_b2。coefficient_forest_plot(names, coefs, ci_low, ci_high)— 係數森林圖,顯著上色、不顯著灰、零線虛線。主迴歸一圖總覽。interaction_plot(x_grid, lines, labels=...)— 交互作用/調節圖,低/中/高調節值各一斜率線,看斜率是否翻轉。調節假設(如家族控制調節 ESG→績效)。group_comparison_plot(groups, means, errors=...)— 分組比較(家族 vs 非家族),帶誤差線。trend_plot(years, series, labels)— 多組逐年趨勢。- 邊際效果圖:調節模型下 X 對 Y 的邊際效果 ∂Y/∂X = β₁+β₃·M 隨調節值 M 變化。用
interaction_plot把 x_grid 設為 M 的取值範圍、lines 設為邊際效果(含 CI 上下界共三線)即可畫出,並在零線處標示效果轉正/負的 M 門檻。
階段三:輸出向量檔
一律用 save_fig(fig, name),同時產 png(預覽)+ pdf + svg(向量,投稿用),皆 300dpi。判準:三個檔都生成、圖內無標題(標題寫在 caption)、上右框線已 despine。
階段四:數字回溯(交付前必驗) 圖中每個數字(轉折點 x*、係數、CI 端點、分組平均、N)都要能指到來源統計輸出檔的哪一列。對不上就停,不准「先放著」。倒U圖必附 β₂ 的 p 值來源。
<matplotlib_中文字型> 軸標預設英文(投稿標準)。使用者要中文版(如口試簡報、中文期刊)時:
- 改 xlabel/ylabel/圖例字串為中文。
- 設定 CJK 字型,否則中文顯示為方框(tofu)。正式文件字型規範為標楷體(DFKai-SB / BiauKai):
標楷體檔名在 Windows 為import matplotlib matplotlib.rcParams["font.sans-serif"] = ["DFKai-SB", "BiauKai", "Microsoft JhengHei"] matplotlib.rcParams["axes.unicode_minus"] = False # 負號正常顯示kaiu.ttf;若 rcParams 設定不生效,用matplotlib.font_manager.FontProperties(fname="C:/Windows/Fonts/kaiu.ttf")逐元素套用。 - 數字與英文仍用 Times New Roman 系(西文襯線),符合中英混排規範;純圖內可維持 sans-serif 求清晰。 推測:若環境無標楷體字型檔,中文會 fallback;此時明講「本環境無標楷體,已用 <實際字型>,投稿前請於有字型的機器重出」,不要假裝已套標楷體。 </matplotlib_中文字型>
<output_contract> 交付含三部分:
- 圖檔路徑:png/pdf/svg 三份路徑,註明 300dpi、色盲友善。
- 圖說 caption 草稿:說明變數、樣本、N、關鍵統計量(轉折點值、β₂ 顯著性、調節斜率差)。
- 數字回溯表:圖中每個關鍵數字 → 來源輸出檔位置。倒U圖必列「β₂=…, p=…,來源:<檔>」;β₂ 不顯著時明確標「非線性證據不足」。 </output_contract>
處置:
- 假設對齊:H1 倒U → quadratic_turning_point_plot。
- 出圖:
fig, ax, info = quadratic_turning_point_plot(esg, roa, xlabel="ESG score", ylabel="ROA");save_fig(fig, "h1_esg_roa_turning")。 - 讀 info:x_star=info["x_star"]=11.67,p_b2=0.03。
- caption 草稿:「Figure 1. 二次式配適(panel FE,N=1,842)。轉折點 ESG*=11.67;β₂=-0.018, p=0.03,倒U 成立。95% CI 為陰影帶。」
- 數字回溯表:x*=11.67 ← -β₁/(2β₂)=-0.42/(2×-0.018),β₁,β₂ 來源 reg_output.txt 第 3 列;p_b2=0.03 同檔。三檔路徑 h1_esg_roa_turning.{png,pdf,svg}。 (若使用者要中文版軸標,套標楷體 rcParams 後重出,並確認負號正常。)
What ships with it
3 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.
- 12d ago First seen · 74 lines · 374 tokens per session scan A 5a544475bbbd
management-figure is a skill published in the GitHub repository Nero1688/claude-academic-skills (6 stars, last pushed 9d ago), licensed MIT. It adds 374 tokens to every session and 2,496 once invoked, about $0.0019 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.
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