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 aaai-experimentsgit 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/aaai-experiments)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aaai-experiments"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-experiments/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/aaai-experiments"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-experiments.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 | $0.00066 | $0.01068 |
| Opus 5.5 | $0.00026 | $0.00427 |
| Sonnet 5.5 | $0.00013 | $0.00214 |
| Haiku 4.5 | $0.00007 | $0.00107 |
Grade A, and why
aaai-experiments 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AAAI Experiments
Use this before submission to ensure empirical evidence supports the AI contribution. AAAI reviewers may come from adjacent AI subfields, so experiments must be interpretable beyond one benchmark community.
Experiment audit
- Map every experimental block to a claim in the introduction.
- Compare against strong, recent, and fairly tuned baselines.
- Include ablations that isolate mechanisms rather than removing multiple components at once.
- Report uncertainty, variance, and statistical tests when small differences matter.
- Test robustness to data split, prompt, seed, environment, user population, or distribution shift when relevant.
- For human evaluation, document task, instructions, annotator pool, quality control, aggregation, and ethics/IRB status.
- Report compute, hardware, data access, model size, and training/inference cost.
Claim-to-evidence ledger
Build this table before adding new experiments. It keeps the AAAI evidence package aligned with the main text and with the reproducibility checklist.
| Manuscript claim | Required evidence | Phase-1 risk if missing | Checklist hook |
|---|---|---|---|
| New AI capability | benchmark + qualitative failure cases | broad reviewer sees only engineering | datasets, metrics, baselines |
| Better mechanism | single-factor ablations | gain looks like tuning luck | ablation and hyperparameter answers |
| Robust deployment | shift / seed / subgroup stress test | result seems brittle | variance, compute, environment |
| Social-impact or safety claim | stakeholder, harm, and misuse analysis | ethical claim looks asserted | ethics, limitations, data access |
For each row, mark ready / weak / missing and name the fastest fix that can be run before the supplementary-material deadline. Do not leave a claim in the abstract if its evidence row is weak.
AAAI-specific review pressure
- Phase 1 reviewers need a fast reason to trust the evidence.
- The reproducibility checklist must match the experiment descriptions.
- AI for Social Impact and AI Alignment claims require stronger treatment of stakeholders, harms, risk mitigation, and scope.
- New results usually cannot rescue the paper in rebuttal, so submit complete evidence upfront.
- The AI-assisted review pilot is non-decisional, but it may surface checklist mismatches; make result provenance, seeds, data splits, and limits machine-readable enough that a human SPC/AC can quickly audit them.
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 · 95 lines · 66 tokens per session scan A cf9f2f488d25
aaai-experiments is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,068 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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