reproducibility-architect

reproducibility-architect is a skill for Claude Code, Codex from Nero1688/claude-academic-skills. It costs 521 tokens per session (2,854 once invoked), scanned A, original, MIT.

A guide for packaging research so another person can rerun its data processing and analysis. A replication package is the project files, instructions, code, data guidance, and software details needed to reproduce published results.

In plain words
What is it for?
Use it to organize raw and generated data, separate code from outputs, record R or Python environments and random seeds, and create a one-command process for producing tables and figures.
Why use it?
It reduces uncertainty about how results were produced and helps address review requirements when data is licensed or cannot be shared.

Skill for Claude CodeCodex

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

Good fit Use it to organize raw and generated data, separate code from outputs, record R or Python environments and random seeds, and create a one-command process for producing tables and figures.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/reproducibility-architect/github.svg)](https://agentmods.dev/skills/nero1688/claude-academic-skills/reproducibility-architect)
Your own site
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/reproducibility-architect"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/reproducibility-architect/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 reproducibility-architect

Your own site · 80×15
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/reproducibility-architect"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/reproducibility-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 521 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,854 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 $0.00521 $0.02854
Opus 5.5 $0.00208 $0.01142
Sonnet 5.5 $0.00104 $0.00571
Haiku 4.5 $0.00052 $0.00285

Measured 7d ago against content hash 34180c6a444e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

reproducibility-architect 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 7d 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.

skills/reproducibility-architect/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

可重現性與複製包架構師(Reproducibility Architect)

Step 1|可重現的專案結構(地基)

固定骨架,讓 replicator 一眼看懂:

project/
├── README.md            # replicator 指南(見 Step 5)
├── run_all.R / run_all.py   # master script:一鍵從 raw 跑到 output
├── renv.lock / requirements.txt  # 環境鎖定
├── data/
│   ├── raw/             # 原始資料,唯讀,絕不被程式改寫
│   └── derived/         # 程式產生的中間檔(可由 raw 重建,不進版本庫)
├── code/
│   ├── 01_clean.R
│   ├── 02_integrate.R   # 多源整合(接 multi-source-data-integrator)
│   ├── 03_analysis.R
│   └── 04_tables_figures.R
└── output/
    ├── tables/          # 每個檔對應論文某表
    └── figures/         # 每個檔對應論文某圖

鐵律:raw 唯讀(所有清理輸出到 derived);輸出檔名對應論文表號圖號 (reviewer 追得到「表 4 是哪支程式產的」);master script 按序呼叫,中途不需手動介入。

Step 2|計算環境捕捉(別人跑得出來的關鍵)

  • R:renv::init() → renv::snapshot() 產 renv.lock(鎖定套件版本); replicator renv::restore() 還原。
  • Python:pip freeze > requirements.txt 或 conda environment.yml; 進階用 Docker 鎖 OS 層(頂刊複製包漸增)。
  • 亂數種子:所有 bootstrap/模擬/隨機分派設固定 seed,寫在 master script 開頭。
  • 版本聲明:R/Python 版本、作業系統、關鍵套件版本寫進 README—— 計量套件(如 did、fixest)更新可能改變結果,版本是可重現的一部分。
  • 路徑:一律相對路徑(用 here::here / Path);絕不硬編本機絕對路徑 (別人的電腦沒有你家目錄的絕對路徑)。

Step 2b|可重現報告層:Quarto(把「程式→表圖→文字」縫成一份可重跑的原始檔)

複製包能重跑出表圖只是一半;頂刊資料編輯越來越常問「稿件裡的數字是從哪個輸出貼過來的」。 Quarto(.qmd)讓程式、圖表與正文住在同一份原始檔,render 時重算——論文裡的每個數字都有 可回溯的程式碼塊,直接消滅手動搬數字的貼錯風險。

  • 何時用:投稿要交複製包且稿件含大量表圖;或審稿要求「換一個設定重跑全部結果」。 不用:純理論稿、或期刊只收 Word 且你已有穩定的表格直出流程(r-spss 的 modelsummary→docx)。
  • 最小結構:paper.qmd(正文+程式碼塊)+ _quarto.yml(輸出格式、execute: freeze: auto 凍結已算結果避免每次全跑)+ references.bib。表格用 gt/modelsummary、圖用 ggplot2, 全部在程式碼塊內產生,不貼外部圖片。
  • 輸出 Word 給期刊:format: docx 搭 reference-doc: template.docx——模板裡先把正文樣式設好 中文標楷體、英數 Times New Roman(本家族字型紀律),render 出來的 docx 才會合規; 交付前仍照 D 檔第 5 節跑字型掃描,不因為是 Quarto 產的就免驗。
  • 從 R Markdown/bookdown/xaringan 遷移:quarto convert old.Rmd 起手,YAML 與 chunk 選項語法 有差(#| echo: false 取代 {r, echo=FALSE}),遷移邏輯與官方 R 套件慣例以 posit-dev/skills 為權威來源(見 ATTRIBUTION.md),本技能不重製其內容。
  • 與 Step 1/2 的關係:.qmd 是 master script 的「敘事層」,環境仍由 renv 鎖;quarto render 應能在乾淨環境從 raw/ 一路跑到成品,否則它只是漂亮的筆記本、不是複製包。

Read the full file on GitHub · 115 lines

Files

What ships with it

2 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. 7d ago Changed 34180c6a444e
  2. 1mo ago Changed · +21 lines · -85 tokens per session 46e67254992a
  3. 1mo ago First seen · 94 lines · 606 tokens per session scan A 6c9ec5d9f32a

Subscribe to this mod's changes

reproducibility-architect is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 5d ago), licensed MIT. It adds 521 tokens to every session and 2,854 once invoked, about $0.0021 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

alterlab-qiime2-amplicon

Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier…

AlterLab-IEU/AlterLab-Academic-Skills · 270 tokens

alterlab-deep-research

Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology…

AlterLab-IEU/AlterLab-Academic-Skills · 266 tokens

alterlab-imaging-data-commons

Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…

AlterLab-IEU/AlterLab-Academic-Skills · 90 tokens

alterlab-cobrapy

Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…

AlterLab-IEU/AlterLab-Academic-Skills · 91 tokens

alterlab-deeptools

Process and visualize deep-sequencing coverage with the deepTools CLI — convert BAM to bigWig (bamCoverage), build log2 ratio tracks (bamCompare), run QC (multiBamSummary correlation, PCA, plotFingerprint), apply the ATAC-seq Tn5 shift (alignmentSieve --ATACshift), and make TSS/peak heatmaps and profiles…

AlterLab-IEU/AlterLab-Academic-Skills · 173 tokens

alterlab-esm

Run ESM protein language models — ESMC for embeddings and representations, ESMFold2 for structure prediction, and ESM3 for generative multimodal protein design across sequence, structure, and function — locally or through the hosted Biohub Platform API (formerly Forge). Use when working with protein sequences…

AlterLab-IEU/AlterLab-Academic-Skills · 101 tokens