Norman-bury/research-writing-skill is an agent skill that turns academic paper writing into a tracked, reusable workflow with planning, drafting, reviews, figures, literature work, and LaTeX outputs. It is intended for undergraduate students, graduate students, and early-career researchers working on theses, coursework papers, or initial submissions. Its catalogue entries are the skills, instructions, plugin, and hook that implement the workflow across coding-agent platforms.
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 Norman-bury/research-writing-skill --skill statistical-analysisgit clone --depth 1 https://github.com/Norman-bury/research-writing-skillWrote 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/norman-bury/research-writing-skill/statistical-analysis)<a href="https://agentmods.dev/skills/norman-bury/research-writing-skill/statistical-analysis"><img src="https://agentmods.dev/badge/skills/norman-bury/research-writing-skill/statistical-analysis/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/norman-bury/research-writing-skill/statistical-analysis"><img src="https://agentmods.dev/badge/skills/norman-bury/research-writing-skill/statistical-analysis.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 | $0.00023 | $0.01751 |
| Opus 5 | $0.00012 | $0.00875 |
| Sonnet 5 | $0.00005 | $0.00350 |
| Haiku 4.5 | $0.00002 | $0.00175 |
Grade A, and why
statistical-analysis 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 10d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
统计分析指南
本技能提供学术论文中统计分析的选择、执行和报告指南。
一、统计检验选择
比较两组
| 数据特征 | 推荐检验 |
|---|---|
| 独立、连续、正态 | 独立样本t检验 |
| 独立、连续、非正态 | Mann-Whitney U检验 |
| 配对、连续、正态 | 配对样本t检验 |
| 配对、连续、非正态 | Wilcoxon符号秩检验 |
| 二分类结果 | 卡方检验或Fisher精确检验 |
比较三组及以上
| 数据特征 | 推荐检验 |
|---|---|
| 独立、连续、正态 | 单因素方差分析 |
| 独立、连续、非正态 | Kruskal-Wallis检验 |
| 配对、连续、正态 | 重复测量方差分析 |
| 配对、连续、非正态 | Friedman检验 |
关系分析
| 分析目标 | 推荐方法 |
|---|---|
| 两个连续变量关系 | Pearson相关（正态）或Spearman相关（非正态） |
| 连续结果与预测变量 | 线性回归 |
| 二分类结果与预测变量 | 逻辑回归 |
二、假设检验
正态性检验
from scipy import stats
# Shapiro-Wilk检验（样本量<5000）
stat, p_value = stats.shapiro(data)
print(f"Shapiro-Wilk检验: W={stat:.4f}, p={p_value:.4f}")
if p_value > 0.05:
print("数据符合正态分布假设")
else:
print("数据不符合正态分布，考虑使用非参数检验")
方差齐性检验
from scipy import stats
# Levene检验
stat, p_value = stats.levene(group1, group2)
print(f"Levene检验: F={stat:.4f}, p={p_value:.4f}")
if p_value > 0.05:
print("方差齐性假设满足")
else:
print("方差不齐，使用Welch's t检验")
三、效应量
常用效应量参考
| 检验 | 效应量 | 小 | 中 | 大 |
|---|---|---|---|---|
| t检验 | Cohen's d | 0.20 | 0.50 | 0.80 |
| ANOVA | η²_p | 0.01 | 0.06 | 0.14 |
| 相关 | r | 0.10 | 0.30 | 0.50 |
| 回归 | R² | 0.02 | 0.13 | 0.26 |
Python计算效应量
import pingouin as pg
# t检验返回Cohen's d
result = pg.ttest(group1, group2)
d = result['cohen-d'].values[0]
print(f"Cohen's d = {d:.2f}")
# ANOVA返回偏η²
aov = pg.anova(dv='score', between='group', data=df)
eta_p2 = aov['np2'].values[0]
print(f"Partial η² = {eta_p2:.3f}")
四、APA格式报告
独立样本t检验
A组（n = 48, M = 75.2, SD = 8.5）得分显著高于B组
（n = 52, M = 68.3, SD = 9.2），t(98) = 3.82, p < .001,
d = 0.77, 95% CI [0.36, 1.18]。
单因素方差分析
单因素方差分析显示处理条件对测试分数有显著主效应，
F(2, 147) = 8.45, p < .001, η²_p = .10。事后比较使用
Tukey HSD表明，条件A（M = 78.2, SD = 7.3）得分显著
高于条件B（M = 71.5, SD = 8.1, p = .002）。
多元回归
多元线性回归预测考试成绩，整体模型显著，
F(3, 146) = 45.2, p < .001, R² = .48。学习时间
（β = .35, p < .001）和先前GPA（β = .28, p < .001）
是显著预测变量。
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.
- 10d ago First seen · 188 lines · 23 tokens per session scan A e2b425a9f657
statistical-analysis is a skill published in the GitHub repository Norman-bury/research-writing-skill (3,183 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 1,751 once invoked, about $0.0001 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 2026-08-30.
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