Agent skill

Aistats Topic Selection

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying…

MITAuto-check passedData & Analytics

Install Aistats Topic Selection

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-topic-selection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aistats-topic-selection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/AISTATS-Skills/skills/aistats-topic-selection .claude/skills/aistats-topic-selection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
aistats-topic-selection
GitHub stars
1.2k
Token cost
~873 tokens
SKILL.md length
392 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying…

  • Deciding whether a project is a strong AISTATS fit
  • SKILL.md covers Fit test, Fit signal table, Vignette: where a debiased… and Sharpening moves before…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Comparing AISTATS with NeurIPS

What it does

Aistats Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.

Its SKILL.md is about 870 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Statistics. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的… The licence is MIT.

When your agent uses it

  • Deciding whether a project is a strong AISTATS fit
  • Comparing AISTATS with NeurIPS
  • Statistics journals
  • Application venues

Example prompts

  • “/aistats-topic-selection”

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Aistats Topic Selection loads about 873 tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 392 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~873

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 392 words, ~873 tokens.

Download SKILL.mdSave it as .claude/skills/aistats-topic-selection/SKILL.md (or your agent's skills folder).
name
aistats-topic-selection
description
Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.

AISTATS Topic Selection

Use this before writing. AISTATS is strongest for work at the intersection of artificial intelligence, machine learning, and statistics, especially when statistical reasoning is not merely an evaluation detail.

Fit test

  • Prefer AISTATS when the contribution advances statistical foundations, inference, uncertainty, causal or probabilistic modeling, learning theory, optimization, or empirical methodology with clear AI/ML relevance.
  • Route to ICML, NeurIPS, or ICLR if the main contribution is broad ML systems, representation learning, scaling, or deep learning practice with limited statistical novelty.
  • Route to UAI if the contribution is primarily uncertainty, probabilistic graphical models, causality, decision making under uncertainty, or Bayesian reasoning.
  • Route to COLT if the contribution is mainly formal learning theory and the empirical story is secondary.
  • Route to a statistics journal when the work needs journal-length exposition, extensive proofs, or a statistics audience more than an AI conference audience.
  • Check early whether the result can be made convincing in an 8-page submission body.

Fit signal table

Signal in the projectAISTATS reading
Consistency, minimax rate, regret, or coverage result paired with experimentsCore fit — the house genre
Bayesian, causal, kernel, or high-dimensional methodology with guaranteesCore fit
Deep architecture with strong benchmarks but thin theoryBetter served at NeurIPS, ICML, or ICLR
Pure theory with no plausible experimentCOLT or a statistics journal
Probabilistic reasoning without a learning angleUAI or a statistics venue
Show full SKILL.md (164 more words)Show less

Vignette: where a debiased estimator goes

A project delivers a debiased lasso variant with valid confidence intervals in high dimensions and simulations confirming coverage. AISTATS reading: strong fit — an inference guarantee plus validating experiments is exactly what this venue rewards. Strip the inference theory and keep only prediction benchmarks, and the same project belongs at a general ML venue; grow it into journal-length asymptotic refinements, and Annals of Statistics or JMLR becomes the better home.

Sharpening moves before committing

  • Name the statistical primitive: estimator, test, bound, posterior, or identification result. If no primitive exists, the AISTATS framing does not exist either.
  • Verify the proof load fits the format: the appendix may be long, but the 8-page body must carry the argument's spine on its own.
  • Confirm the experiments can be designed to test the theory rather than merely accompany it; decoration-only benchmarks are a quiet fit failure here.
  • Topic emphasis drifts between cycles; scan the current CFP subject-area list before final routing.

Output format

text
[Fit] strong AISTATS / possible AISTATS / better elsewhere
[Best venue] AISTATS / NeurIPS / ICML / ICLR / UAI / COLT / journal / other
[Contribution sentence] <one sentence>
[Top rejection risk] <novelty/statistics/evidence/clarity/scope>
[Next action] <theory, experiment, framing, or venue switch>

© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in AISTATS-Skills/skills/aistats-topic-selection of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Aistats Topic Selection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Aistats Topic Selection compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Aistats Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~873Automated safety check: PassMIT
Sandbox Benchvercel/next.js143k—~4.1kAutomated safety check: PassMIT
Statistical Analysisspacering-net/codeg3.9k3 repos~5kAutomated safety check: PassMIT
StatsmodelszLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
AI Daily DigestvigorX777/ai-daily-digest1.6k—~1.3kAutomated safety check: PassNone
Statistical Powerspacering-net/codeg3.9k1 repos~3.6kAutomated safety check: NotesMIT

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Questions about Aistats Topic Selection

What does Aistats Topic Selection do?

A skill your agent uses when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying…. Aistats Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.

When should I use Aistats Topic Selection?

Aistats Topic Selection fits situations like: deciding whether a project is a strong AISTATS fit; comparing AISTATS with NeurIPS; statistics journals; application venues.

How do I install Aistats Topic Selection in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-topic-selection -a claude-code`. Or copy the skill folder (AISTATS-Skills/skills/aistats-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/aistats-topic-selection in your project. Claude Code loads it when a task matches its description.

How do I install Aistats Topic Selection in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-topic-selection -a codex`. Or copy the skill folder (AISTATS-Skills/skills/aistats-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/aistats-topic-selection in your project. Codex loads it when a task matches its description.

Can I use Aistats Topic Selection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-topic-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aistats-topic-selection, .gemini/skills/aistats-topic-selection, .github/skills/aistats-topic-selection and .opencode/skills/aistats-topic-selection in your project.

What does Aistats Topic Selection need to run?

SKILL.md names no scripts, command-line tools or credentials: Aistats Topic Selection is instructions for the agent only.

Does Aistats Topic Selection access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Aistats Topic Selection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Aistats Topic Selection use?

Aistats Topic Selection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Aistats Topic Selection use?

About 873 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Aistats Topic Selection?

Skills that share tags, products or a category with Aistats Topic Selection: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars) and AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aistats Topic Selection?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.