ijcai-topic-selection
Use when deciding whether an AI project is a strong IJCAI or IJCAI-ECAI fit, comparing main track versus special tracks, Survey Track, AIJ/JAIR expedited-publication potential, or alternative AI venues such as NeurIPS, ICML, ICLR, AAAI, AISTATS, UAI, COLT, KDD, CVPR, ACL, or ICRA.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# IJCAI Topic Selection Use this before writing. IJCAI main-track submissions should report significant, original, previously unpublished AI results with broad relevance to the AI community. ## Fit test - Ask whether the paper advances an AI problem, AI technique, AI application domain, or cross-disciplinary AI boundary rather than only reporting an application result. - Prefer IJCAI when the contribution is broadly AI-facing, technically complete in a compact format, and can survive a two-phase review process with limited response leverage. - Consider special tracks for human-centered AI, AI for social good, AI4Tech, AI and health, or AI and robotics when the venue-specific framing is stronger than a general main-track frame. - Consider the Survey Track only for mature areas and author teams with established topic expertise. - Route elsewhere if the contribution is primarily ML theory, representation learning, computer vision, NLP, robotics, HCI, systems, or data mining and a specialist venue has a clearer audience. - Check policy fit early: anonymity, recent rejection disclosures, dual-submission limits, data ethics, LLM-use constraints, and in-person presentation feasibility. ##
- Fit test
- Venue routing matrix
- Worked vignette: a game-theory mechanism
- Reviewer/fit pushback and the venue-specific fix
- Output format
What does the ijcai-topic-selection skill do?
Use when deciding whether an AI project is a strong IJCAI or IJCAI-ECAI fit, comparing main track versus special tracks, Survey Track, AIJ/JAIR expedited-publication potential, or alternative AI venues such as NeurIPS, ICML, ICLR, AAAI, AISTATS, UAI, COLT, KDD, CVPR, ACL, or ICRA.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijcai-topic-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.