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 agentmods add skills/ali66611/glskill/gl-paper-replicationnpx skills add Ali66611/glskill --skill gl-paper-replicationgit clone --depth 1 https://github.com/Ali66611/glskillWrote 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/ali66611/glskill/gl-paper-replication)<a href="https://agentmods.dev/skills/ali66611/glskill/gl-paper-replication"><img src="https://agentmods.dev/badge/skills/ali66611/glskill/gl-paper-replication.svg" alt="Measured on agentmods" 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 | $0.00072 | $0.03062 |
| Opus 5 | $0.00036 | $0.01531 |
| Sonnet 5 | $0.00014 | $0.00612 |
| Haiku 4.5 | $0.00007 | $0.00306 |
Grade A, and why
gl-paper-replication 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 4d 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GL 论文复现 Skill
这是公开轻量版论文复现 skill。它帮助普通用户从一篇实证论文出发，建立可复现的项目结构、整理变量和数据需求、生成基础复现步骤、保存代码和日志、输出复现报告，并形成学习笔记，解释每段代码、每张表和每个模型在做什么。
边界
- 不依赖任何私有总控工作流。
- 不包含私有数据路径、私有记忆、私有模板、密钥或个人项目结论。
- 不提供自动显著性筛选、最优组合搜索、黑箱调参或结果包装。
- 复现结果与原文不一致时，记录差异；不得编造系数、P 值、样本量、星号或表格。
适用场景
- 用户提供论文 PDF、Word、网页、DOI、题目或截图，想复现论文实证部分。
- 用户想搭建“顶刊/核心期刊论文复现”项目文件夹。
- 用户想从论文中提取研究问题、变量、数据来源、模型、表格和复现任务。
- 用户已有数据，希望生成基础 Stata 清洗、描述性统计、基准回归和复现报告。
- 用户复现完后想学习代码、表格和模型，知道每一步为什么这样写。
推荐下游能力
按需要使用，不要求一次全部安装：
| 场景 | 可用能力 |
|---|---|
| 读取论文 | pdf, docx, web-access |
| 表格和清单 | xlsx |
| Stata 清洗和回归 | stata, stata-data-cleaning, stata-regression |
| 文献和引用 | literature-search, cnki-search, gs-search, zotero |
| 写作和交付 | academic-paper-writer, word, docx |
第一步：建立项目结构
每个复现任务必须建立独立文件夹。默认中文结构：
论文复现项目/
00_项目说明/
01_论文材料/
原文/
附件/
02_文献与变量/
03_数据/
原始数据/
清洗数据/
04_代码/
stata/
python/
05_日志/
06_结果/
描述性统计/
基准回归/
稳健性/
机制异质性/
07_复现报告/
08_交付文件/
09_学习笔记/
先创建这些文件：
00_项目说明/项目说明.md
00_项目说明/复现目标.md
00_项目说明/待确认问题.md
02_文献与变量/变量清单.md
02_文献与变量/数据需求.md
03_数据/数据说明.md
07_复现报告/复现差异说明.md
09_学习笔记/代码讲解.md
09_学习笔记/表格解读.md
09_学习笔记/模型学习卡片.md
09_学习笔记/学习路线.md
09_学习笔记/复现收获.md
第二步：读取论文并提取信息
先读论文，不直接跑代码。提取：
- 论文题目、作者、期刊、年份。
- 研究问题和核心结论。
- 样本范围、年份、观测单位。
- 被解释变量、解释变量、控制变量、机制变量、调节变量、异质性分组。
- 数据库或数据来源。
- 基准模型、固定效应、聚类方式。
- 要复现的表格编号和内容。
写入：
00_项目说明/项目说明.md
00_项目说明/复现目标.md
02_文献与变量/变量清单.md
02_文献与变量/数据需求.md
第三步：复现准入检查
正式运行前必须确认：
- 是否已有论文原文和必要附件。
- 是否明确样本期和观测单位。
- 是否明确数据来源。
- 是否明确 Y、X 和控制变量。
- 是否明确固定效应和聚类方式。
- 是否已有可读取数据。
- 是否知道要复现哪几张表。
- 是否明确输出：代码、表格、报告、Word 或全部。
任一关键项缺失时，只能写待确认问题、数据需求和复现计划，不运行正式回归。
第四步：整理数据和变量
原始数据放入：
03_数据/原始数据/
原始数据只读，不覆盖。清洗后另存：
03_数据/清洗数据/
变量清单至少包含：
| 字段 | 含义 |
|---|---|
| 变量角色 | Y / X / 控制变量 / 机制 / 调节 / 异质性 |
| 变量名 | 数据中的变量名 |
| 中文含义 | 变量解释 |
| 构造方式 | 对数、滞后、比率、虚拟变量等 |
| 数据来源 | 数据库、表名或用户文件 |
| 原文依据 | 原文表格、变量定义或脚注 |
| 备注 | 缺失、替代口径、无法复现原因 |
第五步：生成基础 Stata 复现文件
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.
- 4d ago First seen · 362 lines · 72 tokens per session scan A acf21c1d60d9
gl-paper-replication is a skill published in the GitHub repository Ali66611/glskill (6 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 3,062 once invoked, about $0.0004 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.
Other skills, from other repositories
audit-reproducibility
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
review-paper
Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees + editorial decision, calibrated to a target journal). R&R continuation via --peer --r2/--r3; hostile-editor stress test via…
grant-proposal
Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an /interview-me spec, delegates the data-management plan to /data-management-plan and the facilities statement to /capture-environment, and emits a funder-requirements checklist. Use…
preregister
Draft a structured preregistration document (OSF, AsPredicted, or AEA RCT Registry style) from a research spec or free-form study description. Output is a Markdown file with hypotheses, design, sampling plan, analysis plan, exclusions, and inference criteria — annotated with MUST / SHOULD / MAY clarity flags. Use when…
capture-environment
Snapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning…
data-management-plan
Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data…