/aamas-related-work
Use when positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-related-work --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/aamas-related-work
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the
SKILL.md
aamas-related-work.SKILL.mdname: aamas-related-work
description: Use when positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the cross-community citation coverage AAMAS reviewers expect.
AAMAS Related Work
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission, anonymity, and prior-publication rules before advising authors; much of the nearest multiagent work lives at *other* venues, which shapes both positioning and eligibility.
Positioning checks
- Separate the interaction novelty from engineering improvement: a new solution concept,
mechanism, learning-dynamics result, coordination protocol, negotiation strategy, or empirical multiagent finding.
- Compare across communities: AAMAS itself, the AI conferences (AAAI, IJCAI), the ML conferences
(NeurIPS, ICML), and economics-and-computation venues (EC), plus the JAAMAS journal. Many landmark multiagent papers are *not* at AAMAS, so a bibliography that only cites AAMAS is as suspicious as one that ignores it.
- Treat conference proceedings and journals as archival unless the current rules say otherwise.
- Cite arXiv and workshop versions without breaking double-blind; do not point reviewers to an
identity-revealing page.
- Explain overlap with any concurrent or prior version, and do not submit duplicate archival
work.
Cross-community coverage table
| Literature lane | Typical sources | What AAMAS reviewers check | |---|---|---| | Agents / multiagent core | AAMAS, JAAMAS, IJCAI, AAAI agent tracks | Whether the nearest multiagent method is compared or explicitly distinguished | | Multiagent RL | NeurIPS, ICML, ICLR, prior AAMAS | Whether MADDPG/QMIX/COMA-style cousins are cited even though they are not AAMAS papers | | Game theory / mechanism design | EC, GEB, algorithmic game-theory venues | Whether the solution concept and known impossibility results are acknowledged | | Social choice / negotiation | AAMAS, AAAI, COMSOC, ADT | Whether standard axioms and strategyproofness usage are followed |
A bibliography citing only deep-MARL benchmarks tells a game-theory reviewer that known equilibrium or mechanism results may be getting rediscovered - a recognizable AAMAS reject pattern no amount of benchmark strength repairs. The mirror failure - only citing classical game theory while ignoring the deep-MARL wave - loses the empirical reviewer.
Positioning vignette
Imagine the paper proposes a communication protocol that improves cooperation in a mixed-motive game. Its nearest neighbors: a NeurIPS MARL paper with emergent communication but no incentive analysis, an EC paper with a cheap-talk equilibrium but no learning, and a prior AAMAS paper on the same game with a weaker coordination result. The novelty sentence should name all three: incentives where the MARL line had none, learning where the game-theory line stayed static, and stronger coordination than the direct AAMAS predecessor.
Concurrent-work judgment calls
- Independently concurrent arXiv work: cite neutrally, state the technical difference, and avoid
priority claims reviewers cannot verify.
- Your own workshop version: usually non-archival and citable, but verify against the current
CFP and phrase the citation so double-blind survives.
- When unsure about a venue's archival status, declare the overlap in the submission form rather
than gambling on a chair's reading.
Output format
[Eligibility] clear / needs declaration / risky
[Closest literatures] <agents / MARL / game-theory / social-choice>
[Nearest 3 works] <work -> distinction>
[Archival-overlap risk] <none / issues>
[Novelty sentence] <AAMAS-ready interaction contrast>
Read more
name: aamas-related-work description: Use when positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the cross-community citation coverage AAMAS reviewers expect.
AAMAS Related Work
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission, anonymity, and prior-publication rules before advising authors; much of the nearest multiagent work lives at *other* venues, which shapes both positioning and eligibility.
Positioning checks
- Separate the interaction novelty from engineering improvement: a new solution concept,
mechanism, learning-dynamics result, coordination protocol, negotiation strategy, or empirical multiagent finding.
- Compare across communities: AAMAS itself, the AI conferences (AAAI, IJCAI), the ML conferences
(NeurIPS, ICML), and economics-and-computation venues (EC), plus the JAAMAS journal. Many landmark multiagent papers are *not* at AAMAS, so a bibliography that only cites AAMAS is as suspicious as one that ignores it.
- Treat conference proceedings and journals as archival unless the current rules say otherwise.
- Cite arXiv and workshop versions without breaking double-blind; do not point reviewers to an
identity-revealing page.
- Explain overlap with any concurrent or prior version, and do not submit duplicate archival
work.
Cross-community coverage table
| Literature lane | Typical sources | What AAMAS reviewers check | |---|---|---| | Agents / multiagent core | AAMAS, JAAMAS, IJCAI, AAAI agent tracks | Whether the nearest multiagent method is compared or explicitly distinguished | | Multiagent RL | NeurIPS, ICML, ICLR, prior AAMAS | Whether MADDPG/QMIX/COMA-style cousins are cited even though they are not AAMAS papers | | Game theory / mechanism design | EC, GEB, algorithmic game-theory venues | Whether the solution concept and known impossibility results are acknowledged | | Social choice / negotiation | AAMAS, AAAI, COMSOC, ADT | Whether standard axioms and strategyproofness usage are followed |
A bibliography citing only deep-MARL benchmarks tells a game-theory reviewer that known equilibrium or mechanism results may be getting rediscovered - a recognizable AAMAS reject pattern no amount of benchmark strength repairs. The mirror failure - only citing classical game theory while ignoring the deep-MARL wave - loses the empirical reviewer.
Positioning vignette
Imagine the paper proposes a communication protocol that improves cooperation in a mixed-motive game. Its nearest neighbors: a NeurIPS MARL paper with emergent communication but no incentive analysis, an EC paper with a cheap-talk equilibrium but no learning, and a prior AAMAS paper on the same game with a weaker coordination result. The novelty sentence should name all three: incentives where the MARL line had none, learning where the game-theory line stayed static, and stronger coordination than the direct AAMAS predecessor.
Concurrent-work judgment calls
- Independently concurrent arXiv work: cite neutrally, state the technical difference, and avoid
priority claims reviewers cannot verify.
- Your own workshop version: usually non-archival and citable, but verify against the current
CFP and phrase the citation so double-blind survives.
- When unsure about a venue's archival status, declare the overlap in the submission form rather
than gambling on a chair's reading.
Output format
[Eligibility] clear / needs declaration / risky [Closest literatures] <agents / MARL / game-theory / social-choice> [Nearest 3 works] <work -> distinction> [Archival-overlap risk] <none / issues> [Novelty sentence] <AAMAS-ready interaction contrast>
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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Open skill - /aaai-camera-ready
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and
Open skill - /aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and
Open skill - /aaai-related-work
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a
Open skill - /aaai-reproducibility
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that
Open skill

