aistats-supplementary

aistats-supplementary is a skill for Claude Code from brycewang-stanford/Awesome-Journal-Skills. It costs 66 tokens per session (696 once invoked), scanned A, original, MIT.

A guide to preparing supplementary material for an AISTATS paper. Supplementary material is extra proof, experiment, documentation, or code material that supports the main paper without replacing its essential explanation.

In plain words
What is it for?
Use it to organize appendices, proofs, simulations, ablations, dataset information, and anonymized executable files for review.
Why use it?
It helps authors keep required details available while meeting deadlines, anonymity rules, file limits, and archive-cleanliness requirements.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the aistats-skills plugin — 12 skills shipped together

Good fit Use it to organize appendices, proofs, simulations, ablations, dataset information, and anonymized executable files for review.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary
About the project

Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.

brycewang-stanford/Awesome-Journal-Skills · 1,259 stars · on GitHub · copaper.ai

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 brycewang-stanford/Awesome-Journal-Skills --skill aistats-supplementary
Clone the repo
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills

Made for: Claude Code.

Or install aistats-skills, the plugin that ships this one along with the rest of its 12 skills.

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 aistats-supplementary

README.md
[![agentmods](https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary/github.svg)](https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary)
Your own site
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary/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 aistats-supplementary

Your own site · 80×15
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aistats-supplementary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 696 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 $0.00066 $0.00696
Opus 5.5 $0.00026 $0.00278
Sonnet 5.5 $0.00013 $0.00139
Haiku 4.5 $0.00007 $0.00070

Measured 1mo ago against content hash 61c6d355fd58, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

aistats-supplementary 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 1mo 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.

AISTATS-Skills/skills/aistats-supplementary/SKILL.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AISTATS Supplementary

Use this when assembling AISTATS supplementary material. The supplement can support the paper, but the main submission must remain understandable without it.

Supplement structure

  • Put theorem proofs, derivations, extra ablations, simulation details, dataset documentation, and robustness tables in a clean appendix or supplementary manuscript.
  • Put executable assets in a separate archive when current OpenReview forms allow it.
  • Respect the current supplementary deadline. AISTATS 2026 placed supplementary-material submission after the paper deadline but before review.
  • Keep all supplementary files double-blind: no authors, institutions, acknowledgements, private paths, repository owners, grants, or identifying metadata.
  • Do not use the supplement to hide essential motivation, core method description, main theorem statements, or primary empirical results.
  • Verify archives open on a clean machine and contain no credentials, cache directories, large irrelevant files, or hidden OS metadata.

Appendix architecture for proofs

  • Order appendix sections to mirror main-text theorem order; statistician reviewers navigate by theorem number, not by page.
  • Restate each theorem before its proof so the appendix reads standalone without flipping back to the two-column body.
  • Standard partition: notation and assumptions, main proofs, auxiliary lemmas, additional simulations, then real-data details.
  • Keep one proof-sketch paragraph per major theorem in the body itself; an AISTATS submission whose entire argument lives in the supplement reads as unreviewable within the page limit.
  • Cross-reference every appendix table and lemma from the body at least once; orphaned supplement material is invisible to reviewers and wasted under reviewer discretion.
  • If the cycle allows appendices inside the main PDF after the references, confirm against the current instructions whether that or a separate file upload is expected.

What gets opened first

Read the full file on GitHub · 66 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.

  1. 1mo ago First seen · 66 lines · 66 tokens per session scan A 61c6d355fd58

Subscribe to this mod's changes

aistats-supplementary is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,259 stars, last pushed 19d ago), licensed MIT. It adds 66 tokens to every session and 696 once invoked, about $0.0003 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 2026-09-15.

Related

Other skills, from other repositories

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-pyhealth

Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals…

AlterLab-IEU/AlterLab-Academic-Skills · 132 tokens

alterlab-anndata

Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…

AlterLab-IEU/AlterLab-Academic-Skills · 133 tokens

alterlab-tiledbvcf

Store and query genomic variant data at scale with TileDB-VCF — ingest VCF/BCF into compressed TileDB arrays, add samples incrementally, run fast parallel region/sample queries, and export back to VCF. Use when managing population-genomics variant datasets that are too large for flat VCF, building joint variant…

AlterLab-IEU/AlterLab-Academic-Skills · 93 tokens

alterlab-datamol

Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel batch processing, returning native…

AlterLab-IEU/AlterLab-Academic-Skills · 120 tokens

alterlab-eda

Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its…

AlterLab-IEU/AlterLab-Academic-Skills · 258 tokens