What is open-science-reproducibility?

A practice guide for making architecture, engineering, and construction research easier to verify and reuse. It covers FAIR data, which means data should be findable, accessible, compatible with other systems, and reusable.

Use it to plan data management, repositories and DOIs, version control, software environments, containers, preprints, preregistration, open access, and data or code availability statements.

It reduces the chance that research data, code, or methods become impossible for others to inspect or repeat.

open-science-reproducibility — Skill for Claude Code by Abhinavbwj

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 Abhinavbwj/AEC-Scholar --skill open-science-reproducibility

Clone the repo

git clone --depth 1 https://github.com/Abhinavbwj/AEC-Scholar

Made for: Claude Code.

Or install aec-scholar, the plugin that ships this one along with the rest of its 11 skills, 36 commands, 10 agents, 1 hook.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/abhinavbwj/aec-scholar/open-science-reproducibility/github.svg)](https://agentmods.dev/skills/abhinavbwj/aec-scholar/open-science-reproducibility)

Your own site

<a href="https://agentmods.dev/skills/abhinavbwj/aec-scholar/open-science-reproducibility"><img src="https://agentmods.dev/badge/skills/abhinavbwj/aec-scholar/open-science-reproducibility/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/abhinavbwj/aec-scholar/open-science-reproducibility"><img src="https://agentmods.dev/badge/skills/abhinavbwj/aec-scholar/open-science-reproducibility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,221 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.

Model	Per session	Once invoked
Fable 5.1	$0.00096	$0.01221
Opus 5	$0.00048	$0.00611
Sonnet 5	$0.00019	$0.00244
Haiku 4.5	$0.00010	$0.00122

Measured 11d ago against content hash b636598bb6fb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

open-science-reproducibility 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 11d 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.

aec-scholar/skills/open-science-reproducibility/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Open Science & Reproducibility — Practice Guide

Reproducibility is a credibility multiplier and increasingly a funder/journal requirement. AEC's reliance on simulations, bespoke datasets and proprietary tools makes this especially important — and often neglected.

1. FAIR data principles

Findable — deposit with a persistent identifier (DOI) and rich metadata; index it.
Accessible — retrievable via a standard protocol; state access conditions (open vs controlled).
Interoperable — use standard formats/vocabularies (open formats, documented schemas; for built-env data consider IFC, CityGML, standard CSV + data dictionary).
Reusable — clear license, provenance, and enough documentation to reuse correctly.

FAIR ≠ "open": sensitive/proprietary data (site, personal, commercial) can be FAIR yet access-controlled.

2. Data repositories

General: Zenodo (CERN; DOIs, GitHub integration), Figshare, Dryad, OSF, Harvard Dataverse.
Institutional repositories; domain repositories where they exist.
Choose one that issues a DOI, supports your license, and offers versioning. Avoid sharing data only as "available on request" — it usually isn't.

3. Data Management Plan (DMP)

Plan early (many funders require it at proposal stage). Cover: what data are collected/generated; formats & volume; metadata & documentation standards; storage, backup & security during the project; legal/ethical constraints (consent, GDPR, IP, confidentiality); what will be shared, when, where, and under which license; long-term preservation; and roles/responsibilities. Tools: DMPonline / DMPTool / Argos.

4. Computational reproducibility (code & analysis)

Version control everything (Git); tag the exact commit/release used for the paper.
Capture the environment: requirements.txt/environment.yml (conda), renv (R), lockfiles; or a Docker/Apptainer container or Binder for one-click reproduction.
Literate analysis: Jupyter, Quarto/R Markdown so results, figures and tables regenerate from data.
Pin versions of tools, libraries, solvers and (for energy work) weather files — results shift between versions.
Provide a README with exact run instructions and a mapping from scripts → figures/tables in the paper.
Archive a release of the code with a DOI (Zenodo–GitHub integration) and cite it.

Read the full file on GitHub · 81 lines

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.

11d ago First seen · 81 lines · 96 tokens per session scan A b636598bb6fb

Subscribe to this mod's changes

open-science-reproducibility is a skill published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 96 tokens to every session and 1,221 once invoked, about $0.0005 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.

Git and workflow Scientific

manuscript-as-code

Treat manuscripts as software: version control, reproducible builds, figure pipelines, CI, and structured repo layout. Helps teams avoid 'finalv7' chaos and ensures submission-ready artifacts.

foryourhealth111-pixel/Vibe-Skills · 43 tokens

latex-diff

Compare two LaTeX files, project directories, or Git revisions and produce human-readable plus machine-readable severity-graded semantic changes. Use when determining what changed between manuscript versions or preparing a revision audit bundle. Not for proofreading one version; use $proofread.

flonat/flonat-research · 56 tokens

open-science-reproducibility