Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-topic-selectiongit clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aamas-topic-selection)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aamas-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aamas-topic-selection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aamas-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aamas-topic-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.00832 |
| Opus 5.5 | $0.00032 | $0.00333 |
| Sonnet 5.5 | $0.00016 | $0.00166 |
| Haiku 4.5 | $0.00008 | $0.00083 |
Grade A, and why
aamas-topic-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 67 lines · 81 tokens per session scan A ea737ecf6f11
aamas-topic-selection is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 832 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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