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.
SkillScore83Maintained
Automatic
◆Research pack
Last verified 8/15/2026
What it changes
Passed 20 of 24 test cases with the skill, and 9 of 24 without it.
Without skill38%
With skill83%
Checks it passed that the model alone missed
The recommended venue is AAAI.
The recommended venue is JAAMAS.
The recommended venue is NeurIPS.
and 11 more in the full report
12 fixed8 passed either way3 still failing1 broke
Pass-rate lift
+46 pp
Tokens
-2%
Agent turns
0%
Response time
-50%
Model gemini-3.6-flashMeasured 8/15/2026v120 of 24 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 12
Prompt
Our study analyzes emergent communication protocols in mixed-motive social dilemma games to verify incentive compatibility and equilibrium shifts. Provide venue evaluation using key tags [Fit], [Best venue], [Interaction primitive], [Contribution sentence], [Top rejection risk], and [Next action].
What was checked, without and with the skill
Checked
Without skill
With skill
The AAMAS fit rating is classified as strong AAMAS.
Missed
Passed
The interaction primitive is identified as protocol.
Missed
Passed
case-08 · gemini-3.6-flash · 8/15/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md61 linessingle file
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 an 8-page body.
Fit signal table
Signal in the project
AAMAS reading
A solution concept, mechanism, or coordination result paired with multiagent experiments
Core fit - the house genre
Emergent behavior that only appears because agents co-adapt
Core fit
Strong single-agent method benchmarked in a multiagent environment
Better served at NeurIPS or ICML
Pure market/auction theory with economics as the point
EC or an econ-CS journal
Broad AI reasoning with no interaction at its center
AAAI or IJCAI
Vignette: where a communication-learning project goes
A project trains agents to communicate and shows higher cooperation in a mixed-motive game.
AAMAS reading: strong fit if the analysis is about the interaction - what the emergent protocol
signals, whether it is incentive-compatible, how it changes the equilibrium. Strip the incentive
and coordination analysis and keep only a reward curve, and the same project reads as a general
MARL paper better suited to NeurIPS or ICML; grow it into a full theory of the signaling
equilibrium, and EC or JAAMAS becomes the better home.