management-figure

management-figure is a skill for Claude Code, Codex from Nero1688/claude-academic-skills. It costs 374 tokens per session (2,496 once invoked), scanned A, original, MIT.

A chart-making toolkit for evidence-based management, finance, and strategy research. It creates publication-ready plots from regression results and tracking data, including coefficient, interaction, group-comparison, trend, and curved-relationship charts.

In plain words
What is it for?
Use it to visualize regression coefficients, turning points, moderation effects, marginal effects, family-owned versus other firms, and changes over time in PNG, PDF, and SVG formats.
Why use it?
It helps ensure that figures match the research hypotheses, statistical models, and reported results. It also keeps chart numbers traceable to their source outputs.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to visualize regression coefficients, turning points, moderation effects, marginal effects, family-owned versus other firms, and changes over time in PNG, PDF, and SVG formats.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nero1688/claude-academic-skills/management-figure
Install

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.

Any agent
npx skills add Nero1688/claude-academic-skills --skill management-figure
Clone the repo
git clone --depth 1 https://github.com/Nero1688/claude-academic-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for management-figure

README.md
[![agentmods](https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/management-figure/github.svg)](https://agentmods.dev/skills/nero1688/claude-academic-skills/management-figure)
Your own site
<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.

agentmods 80×15 button for management-figure

Your own site · 80×15
<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>
Per session 374 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,496 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash 5a544475bbbd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/mgmt_figures.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/management-figure/SKILL.md · 74 lines

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:

  1. 改 xlabel/ylabel/圖例字串為中文。
  2. 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):
    import matplotlib
    matplotlib.rcParams["font.sans-serif"] = ["DFKai-SB", "BiauKai", "Microsoft JhengHei"]
    matplotlib.rcParams["axes.unicode_minus"] = False  # 負號正常顯示
    
    標楷體檔名在 Windows 為 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。
  3. 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:

  1. 圖檔路徑:png/pdf/svg 三份路徑,註明 300dpi、色盲友善。
  2. 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)。
  3. 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:

  1. 假設對齊:H1 倒U → quadratic_turning_point_plot。
  2. 出圖:fig, ax, info = quadratic_turning_point_plot(esg, roa, xlabel="ESG score", ylabel="ROA")save_fig(fig, "h1_esg_roa_turning")
  3. 讀 info:x_star=info["x_star"]=11.67,p_b2=0.03。
  4. 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。」
  5. 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。)
Files

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.

Changes

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.

  1. 12d ago First seen · 74 lines · 374 tokens per session scan A 5a544475bbbd

Subscribe to this mod's changes

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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