aamas-topic-selection
Use when deciding whether a project is a strong AAMAS fit, comparing AAMAS with AAAI, IJCAI, NeurIPS, ICML, EC, and the JAAMAS journal, identifying whether the agents are truly the research object, naming the interaction primitive (solution concept, mechanism, coordination, negotiation), and sharpening the multiagent framing before writing begins.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-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.
# AAMAS Topic Selection Use this before writing. AAMAS is strongest when the *agents* are the research object - when the result exists because multiple self-interested or cooperating agents interact - not when a single-agent method is dressed in multiagent vocabulary. ## Fit test - Prefer AAMAS when the contribution advances game-theoretic reasoning, multiagent learning, mechanism design, auctions, negotiation, argumentation, coordination and teamwork, agent-based simulation, or social choice, with the interaction as the object. - Route to NeurIPS or ICML if the core is a single-agent or general ML method and the multiagent setting is only a testbed. - Route to AAAI or IJCAI if the contribution is broad AI - planning, knowledge representation, reasoning - without an interaction result at its center. - Route to EC (Economics and Computation) if the contribution is primarily equilibrium computation, market design, or auction theory with the economics framing dominant. - Route to the JAAMAS journal (or its AAMAS presentation track) when the work needs journal-length exposition and a full-length archival treatment. - Check early whether the interaction result can be made convincing in
- Fit test
- Fit signal table
- Vignette: where a communication-learning project goes
- Sharpening moves before committing
- Output format
What does the aamas-topic-selection skill do?
Use when deciding whether a project is a strong AAMAS fit, comparing AAMAS with AAAI, IJCAI, NeurIPS, ICML, EC, and the JAAMAS journal, identifying whether the agents are truly the research object, naming the interaction primitive (solution concept, mechanism, coordination, negotiation), and sharpening the multiagent framing before writing begins.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-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 909 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.