# Aaai Topic Selection

Use when deciding whether a project is a strong AAAI submission across its broad AI...

- Repository: https://github.com/brycewang-stanford/Awesome-Journal-Skills (1346 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-topic-selection -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-topic-selection -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-topic-selection -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-topic-selection -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Awesome-Journal-Skills`

## Description

Use when deciding whether a project is a strong AAAI submission across its broad AI scope, should be reframed or routed to a dedicated track such as AI for Social Impact or AI Alignment, or should instead go to IJCAI, NeurIPS, ICML, ICLR, AISTATS, UAI, ACL, CVPR, KDD, CHI, ICRA, or another specialist venue.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# AAAI Topic Selection

Use this while the project is still movable. AAAI is broad across artificial intelligence, so a
strong submission should make an AI contribution that is intelligible beyond a narrow subfield.

## Strong AAAI signals

- Clear AI problem and contribution: method, theory, system, benchmark, dataset, evaluation, social
  impact, alignment, human-AI interaction, planning, reasoning, learning, NLP, vision, robotics, or
  knowledge representation.
- Evidence that supports a general AI claim, not only a local application result.
- Responsible treatment of ethics, safety, privacy, fairness, social impact, or misuse when the
  paper touches those areas.
- Reproducibility path strong enough for checklist scrutiny.
- Narrative clear enough for Phase 1 reviewers from adjacent AI areas.

## Weak AAAI signals

- Pure application deployment with little AI insight.
- Benchmark bump without mechanism, analysis, or robust comparison.
- Closed system with no reviewable evidence.
- Paper better framed as statistics, NLP, vision, HCI, robotics, or systems for a specialist venue.
- Policy-sensitive claims with thin ethics or stakeholder analysis.

## Routing logic

- Prefer IJCAI for broad AI work with an international AI community emphasis.
- Prefer NeurIPS, ICML, or ICLR for stronger ML method/theory or representation-learning framing.
- Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis.
- Prefer ACL, CVPR, KDD, CHI, ICRA, or systems venues when the contribution is domain-specific.
- Prefer a workshop if evidence is preliminary but the idea is timely.

## Fit-versus-route table

AAAI's breadth is an asset only when the contribution reads as general AI, not a narrow benchmark
result. Use the dominant signal to decide between AAAI and a specialist venue.

The rest: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/AAAI-Skills/skills/aaai-topic-selection/SKILL.md