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Agent skill
Cjms Empirical Validation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when designing the real-data validation of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — forecasting and financial-engineering strands: data provenance…
Install Cjms Empirical Validation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cjms-empirical-validation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation .claude/skills/cjms-empirical-validation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cjms-empirical-validation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation into .claude/skills/cjms-empirical-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cjms-empirical-validation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cjms-empirical-validation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation .agents/skills/cjms-empirical-validation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cjms-empirical-validation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation into .agents/skills/cjms-empirical-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cjms-empirical-validation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cjms-empirical-validation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation .cursor/skills/cjms-empirical-validation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cjms-empirical-validation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation into .cursor/skills/cjms-empirical-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cjms-empirical-validation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cjms-empirical-validation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation .gemini/skills/cjms-empirical-validation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cjms-empirical-validation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation into .gemini/skills/cjms-empirical-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cjms-empirical-validation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cjms-empirical-validationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation .github/skills/cjms-empirical-validation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cjms-empirical-validation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation into .github/skills/cjms-empirical-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cjms-empirical-validation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cjms-empirical-validation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation .opencode/skills/cjms-empirical-validation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cjms-empirical-validation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation into .opencode/skills/cjms-empirical-validation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cjms-empirical-validation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cjms-empirical-validation- GitHub stars
- 1.2k
- Token cost
- ~783 tokens
- SKILL.md length
- 115 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing the real-data validation of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — forecasting and financial-engineering strands: data provenance…
- Works in 4 steps: 数据可溯源：来源（Wind/CSMAR/交易所官网/公开能源数据）、频率、起止区间… → 样本外为王：训练/验证/测试切分或滚动窗口预先声明；窗口长度、步长、再估计频率写清… → 基准电池：至少三层——朴素基准（随机游走/等权组合）、经典基准（ARIMA/GAR… → …
- Rolling out-of-sample tests
- SKILL.md covers 触发时机, 核心：本刊实证的四道硬门, 稳健性设计（按威胁分类，不是堆表） and 与代码库的衔接, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Cjms Empirical Validation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing the real-data validation of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — forecasting and financial-engineering strands: data provenance, rolling out-of-sample tests, benchmark batteries, and significance of improvement. Validates methods on data; simulation-based studies belong to cjms-numerical-experiments.
Its SKILL.md is about 780 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Forecasting and time series and Reproducible research. It works with Model Context Protocol. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Rolling out-of-sample tests
- Benchmark batteries
- Significance of improvement
Example prompts
- “/cjms-empirical-validation”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 数据可溯源：来源（Wind/CSMAR/交易所官网/公开能源数据）、频率、起止区间、缺失处理逐项写明；关键序列给描述统计与平稳性检验。
- 样本外为王：训练/验证/测试切分或滚动窗口预先声明；窗口长度、步长、再估计频率写清。样本内 R² 再高也不算证据。
- 基准电池：至少三层——朴素基准（随机游走/等权组合）、经典基准（ARIMA/GARCH/均值-方差）、最强近敌（最新文献同型方法）。赢不过朴素基准的改进无发表价值。
- 改进要显著：预测类给 DM 检验或 MCS；组合类报夏普比、最大回撤并做子区间稳健性；单点百分比改进不构成结论。
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cjms Empirical Validation loads about 783 tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 115 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 115 words, ~783 tokens.
Download SKILL.mdSave it as
.claude/skills/cjms-empirical-validation/SKILL.md (or your agent's skills folder).- name
- cjms-empirical-validation
- description
- Use when designing the real-data validation of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — forecasting and financial-engineering strands: data provenance, rolling out-of-sample tests, benchmark batteries, and significance of improvement. Validates methods on data; simulation-based studies belong to cjms-numerical-experiments.
实证与数据检验（cjms-empirical-validation）
触发时机
- 预测/金融类稿件只有全样本内拟合，没有样本外
- "新方法更优"只凭一张误差表，没有显著性检验
- 数据来源、频率、区间交代不清，无法复现
核心：本刊实证的四道硬门
预测与决策、市场与投资分析两个栏目的稿件，外审按四道门检查：
- 数据可溯源：来源（Wind/CSMAR/交易所官网/公开能源数据）、频率、起止区间、缺失处理逐项写明；关键序列给描述统计与平稳性检验。
- 样本外为王：训练/验证/测试切分或滚动窗口预先声明；窗口长度、步长、再估计频率写清。样本内 R² 再高也不算证据。
- 基准电池：至少三层——朴素基准（随机游走/等权组合）、经典基准（ARIMA/GARCH/均值-方差）、最强近敌（最新文献同型方法）。赢不过朴素基准的改进无发表价值。
- 改进要显著：预测类给 DM 检验或 MCS；组合类报夏普比、最大回撤并做子区间稳健性；单点百分比改进不构成结论。
稳健性设计（按威胁分类，不是堆表）
| 结论威胁 | 对应检验 |
|---|---|
| 结果靠某段行情 | 子样本/牛熊分段、危机窗口单独报告 |
| 结果靠调参 | 参数敏感性网格、默认参数对照 |
| 结果靠某个数据源 | 换数据源/频率复跑 |
| 结果靠事后信息 | 检查前视偏差：特征、标准化、模型选择全部只用当期可得信息 |
与代码库的衔接
面板/因果类支线（如政策冲击对市场的影响）可直接改用 ../../resources/code/ 的 Stata/Python 骨架（清洗→描述→DiD/IV/RDD→稳健性→出表）；时间序列预测线建议同样落成"一键复现"目录结构，随稿准备可提供的复现材料。
执行桥（StatsPAI / Stata MCP）
四道硬门里有三道是算出来的，不是写出来的。工具全表见
execution-with-mcp。
- 改进显著性：
bootstrap给误差差值的区间；预测线的 DM/MCS 若无现成实现， 至少用bootstrap+romano_wolf把"赢了几个基准"的多重比较校正掉——基准电池 越厚，单看一列 p 值越容易赢在运气上，这正是本刊外审最常追的一刀。 - 参数敏感性：
spec_curve把"换个窗长/换个参数就翻盘"一次画完，比补三张表更 能回答审稿人的"结果靠调参吗"。 - 因果支线：政策冲击类用
detect_design→preflight→did/iv/rdd，再audit_result列出还欠哪些稳健性，逐条按它给的suggest_function补。 - 子样本：牛熊分段与危机窗口用同一个
result_id派生，避免各段口径悄悄不一致。
服务器未连接时退回 ../../resources/code/ 的骨架照抄改写，并在稿中说明数字的来源——
不报没算过的数。
自检清单
- 数据来源/频率/区间/缺失处理可复现，关键变量有描述统计
- 样本外方案预先声明，滚动细节（窗长、步长、再估计）完整
- 基准电池三层齐全，含最强近敌
- 改进的统计显著性（DM/MCS 或子区间一致性）已报告
- 稳健性按威胁组织，每项检验能说出防的是哪条质疑
- 无前视偏差：逐环节核查信息时点
本刊实证节的外审期待
| 退稿信号（审稿常用语） | 根因 | 本刊期望的修法 |
|---|---|---|
| "缺乏样本外检验" | 只报全样本拟合 | 滚动窗口方案入正文，细节可复现 |
| "对比方法选择不当" | 基准电池缺最强近敌 | 补近三年同型方法，正面交锋 |
| "改进幅度的显著性存疑" | 只报点值 | DM/MCS + 子区间一致性 |
| "结果可能依赖样本区间" | 区间恰避开极端行情 | 危机窗口单独报告，边界诚实 |
| "存在前视偏差之嫌" | 分解/标准化用了全样本信息 | 逐环节声明信息时点；分解类方法尤其要滚动重估 |
最后一条是预测栏目的高频雷区：EMD/VMD 类"分解-预测-集成"研究若对全样本一次性分解再切分训练测试，属于典型前视偏差，近年外审盯得很紧。
微型走查：碳价预测的检验设计
虚构稿件《基于模态自适应组合的全国碳市场价格预测》（示意设计）：
数据：全国碳排放权交易市场日收盘价，2021-07 至 2025-12，来源与
缺失处理（节假日对齐）写明
样本外：滚动窗口 500 日，步长 1 日，每步重新分解与训练（防前视）
基准电池：朴素=随机游走；经典=ARIMA、GARCH；机器学习=LSTM、XGBoost；
最强近敌=近三年文献的 VMD-LSTM 组合
指标：RMSE / MAE / MAPE + DM 检验（vs 逐个基准）+ MCS 90% 存活集
稳健性：履约季 vs 非履约季分段；窗长 250/750 敏感性；
剔除政策公告日复跑要点：履约季分段是碳市场特有的结构性检验——用情境知识设计稳健性，比堆通用检验更能说服本刊审稿人。
反模式
- 用样本内拟合优度讲故事，样本外一笔带过
- 基准只选弱者：跟十年前的方法比，回避最新同型文献
- 百分比误差改进 0.3% 就宣称"显著优于"，无任何检验
- 数据区间恰好停在方法失效的行情之前
- 把机器学习黑箱直接端上来，不报告特征与调参协议
输出格式
【数据】来源<…> 频率<…> 区间<…> 缺失处理<…>
【样本外方案】<切分或滚动细节>
【基准电池】朴素<…> 经典<…> 最强近敌<…>
【显著性】DM/MCS/子区间：<结果>
【稳健性矩阵】威胁→检验 × n
【下一步】cjms-numerical-experiments（如有仿真）或 cjms-managerial-insights© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cjms Empirical Validation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cjms Empirical Validation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~783 | Automated safety check: Pass | MIT | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Setup Timescaledb Hypertablestimescale/pg-aiguide | 1.9k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Scientific Toolkit SkillzLanqing/codex-claude-academic-skills | 4.7k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Roas Forecastingirinabuht12-oss/marketing-skills | 4.1k | — | ~681 | Automated safety check: Pass | None | |
| Datalineage Summarygoogle/skills | 21k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 |
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ANALYSIS SKILL — Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL.
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Questions about Cjms Empirical Validation
What does Cjms Empirical Validation do?
A skill your agent uses when designing the real-data validation of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — forecasting and financial-engineering strands: data provenance…. Cjms Empirical Validation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing the real-data validation of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — forecasting and financial-engineering strands: data provenance, rolling out-of-sample tests, benchmark batteries, and significance of improvement.
When should I use Cjms Empirical Validation?
Cjms Empirical Validation fits situations like: rolling out-of-sample tests; benchmark batteries; significance of improvement.
How do I install Cjms Empirical Validation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a claude-code`. Or copy the skill folder (Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cjms-empirical-validation in your project. Claude Code loads it when a task matches its description.
How do I install Cjms Empirical Validation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a codex`. Or copy the skill folder (Chinese-Journal-of-Management-Science-Skills/skills/cjms-empirical-validation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cjms-empirical-validation in your project. Codex loads it when a task matches its description.
Can I use Cjms Empirical Validation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cjms-empirical-validation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cjms-empirical-validation, .gemini/skills/cjms-empirical-validation, .github/skills/cjms-empirical-validation and .opencode/skills/cjms-empirical-validation in your project.
What does Cjms Empirical Validation need to run?
SKILL.md names no scripts, command-line tools or credentials: Cjms Empirical Validation is instructions for the agent only. Our summary lists: Python 3.
Does Cjms Empirical Validation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Cjms Empirical Validation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Cjms Empirical Validation use?
Cjms Empirical Validation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cjms Empirical Validation use?
About 783 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cjms Empirical Validation?
Skills that share tags, products or a category with Cjms Empirical Validation: Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars), Setup Timescaledb Hypertables (timescale/pg-aiguide, 1.9k stars), Scientific Toolkit Skill (zLanqing/codex-claude-academic-skills, 4.7k stars) and Roas Forecasting (irinabuht12-oss/marketing-skills, 4.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cjms Empirical Validation?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.