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.
git clone --depth 1 https://github.com/Abhinavbwj/AEC-ScholarWrote 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/commands/abhinavbwj/aec-scholar/reproducibility)<a href="https://agentmods.dev/commands/abhinavbwj/aec-scholar/reproducibility"><img src="https://agentmods.dev/badge/commands/abhinavbwj/aec-scholar/reproducibility.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 5.1 | $0.00021 | $0.00458 |
| Opus 5 | $0.00010 | $0.00229 |
| Sonnet 5 | $0.00004 | $0.00092 |
| Haiku 4.5 | $0.00002 | $0.00046 |
Grade A, and why
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 8d 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.
What it actually says
Make the study reproducible: $ARGUMENTS
Use the open-science-reproducibility and aec-datasets-tools skills (+ research-methods for
model validation).
Produce a tailored reproducibility plan and checklist:
-
Data — repository choice (Zenodo/OSF/Figshare/Dataverse) with a DOI, metadata + data dictionary, license, and an honest access decision (open vs controlled, with the reason). Flag anything blocked by consent/GDPR/IP/confidentiality.
-
Code & environment — version control (Git) + tagged release, environment capture (
requirements.txt/environment.yml/renv/lockfile or Docker/Binder), literate analysis (Quarto/Jupyter/ R Markdown), a README mapping scripts → each figure/table, and a Zenodo-archived release with a citable DOI. -
Models (AEC-specific) — share simulation inputs where IP permits (e.g. EnergyPlus IDF + EPW, OpenSees/FEM inputs, Grasshopper definitions), document assumptions/boundary conditions/solver + versions, and report calibration/validation vs measured data with uncertainty/sensitivity analysis.
-
Pre-registration / preprint — advise whether preregistration or a registered report fits (confirmatory work) and a preprint route (engrXiv/arXiv) consistent with the target journal's policy.
-
Statements — draft the data & code availability statement and a software/data citation list (
citation-formats), plus an AI-use disclosure if applicable.
Deliver the plan as an actionable checklist (☐ items). If you can see the user's project files, audit what's present vs missing. Never label a workflow reproducible if it isn't, and never recommend sharing data that breaches consent/IP/confidentiality.
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.
- 8d ago First seen · 35 lines · 21 tokens per session scan A 3c5fa0d80856
reproducibility is a command published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 458 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.
Other commands, from other repositories
verify-math
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replication-package
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diff
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simulation-calibrator
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arg-diagram
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graphite-morphology-classify
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