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-artifact-evaluationgit 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-artifact-evaluation)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aamas-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aamas-artifact-evaluation/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-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aamas-artifact-evaluation.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 | $0.00070 | $0.00789 |
| Opus 5.5 | $0.00028 | $0.00316 |
| Sonnet 5.5 | $0.00014 | $0.00158 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
aamas-artifact-evaluation 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AAMAS Artifact Evaluation
Use this for evidence packaging around AAMAS. Because the venue is about interaction, an artifact must make a multiagent claim inspectable: the game, the other agents, and the protocol, not just a single trained model.
Artifact plan
- Decide what a reviewer needs to believe the interaction claim: game or environment code, opponent/population definitions, the training regime, seeds, payoff logs, proofs, or qualitative episode traces.
- Keep decision-critical evidence in the main paper or appendix; optional bulk runs can live in the supplementary zip.
- Anonymize repository history, paths, environment names, license headers, cluster paths, and commit authors for the review version.
- Include a minimal reproduction map: environment build, dependencies, hardware, commands, expected outputs, per-run wall-clock, seeds, and known nondeterminism (especially in self-play).
- For a deployed or human-subject setting, give enough provenance for credible reproduction without violating data-use terms.
- After acceptance, replace anonymous archives with a public, licensed, citable artifact.
What AAMAS evidence reviewers open first
The single fact that shapes packaging: a reviewer will re-run a small game far sooner than they will retrain a large policy, so make the strategic core turnkey before polishing anything.
| Claim type | First artifact inspected | Common failure caught |
|---|---|---|
| Convergence to an equilibrium | The game definition and the learning-rule code | Solution concept named in the paper but not encoded in the evaluation |
| Emergent cooperation/defection | The environment and reward specification | Result depends on an undocumented reward-shaping constant |
| Beats other agents | The opponent/population set and match protocol | Only self-play reported; no held-out opponents |
| Mechanism is truthful | The payment rule plus a strategic-deviation test | No script that lets an agent try to game the mechanism |
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 · 70 lines · 70 tokens per session scan A 1bb60008b1c5
aamas-artifact-evaluation is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 789 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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