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-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/aaai-topic-selection)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aaai-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-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/aaai-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-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 | $0.00088 | $0.01211 |
| Opus 5 | $0.00044 | $0.00606 |
| Sonnet 5 | $0.00018 | $0.00242 |
| Haiku 4.5 | $0.00009 | $0.00121 |
Grade A, and why
aaai-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 12d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AAAI Topic Selection
Use this while the project is still movable. AAAI is broad across artificial intelligence, so a strong submission should make an AI contribution that is intelligible beyond a narrow subfield.
Strong AAAI signals
- Clear AI problem and contribution: method, theory, system, benchmark, dataset, evaluation, social impact, alignment, human-AI interaction, planning, reasoning, learning, NLP, vision, robotics, or knowledge representation.
- Evidence that supports a general AI claim, not only a local application result.
- Responsible treatment of ethics, safety, privacy, fairness, social impact, or misuse when the paper touches those areas.
- Reproducibility path strong enough for checklist scrutiny.
- Narrative clear enough for Phase 1 reviewers from adjacent AI areas.
Weak AAAI signals
- Pure application deployment with little AI insight.
- Benchmark bump without mechanism, analysis, or robust comparison.
- Closed system with no reviewable evidence.
- Paper better framed as statistics, NLP, vision, HCI, robotics, or systems for a specialist venue.
- Policy-sensitive claims with thin ethics or stakeholder analysis.
Routing logic
- Prefer IJCAI for broad AI work with an international AI community emphasis.
- Prefer NeurIPS, ICML, or ICLR for stronger ML method/theory or representation-learning framing.
- Prefer AISTATS or UAI for statistics, uncertainty, causal, or probabilistic emphasis.
- Prefer ACL, CVPR, KDD, CHI, ICRA, or systems venues when the contribution is domain-specific.
- Prefer a workshop if evidence is preliminary but the idea is timely.
Fit-versus-route table
AAAI's breadth is an asset only when the contribution reads as general AI, not a narrow benchmark result. Use the dominant signal to decide between AAAI and a specialist venue.
| Project shape | AAAI fit | Better route if not |
|---|---|---|
| New planning or KR mechanism | strong, core AAAI turf | UAI for pure uncertainty |
| ML method with broad insight | plausible | NeurIPS/ICML for deep theory |
| Domain deployment, thin AI | weak | KDD, CHI, or ICRA |
| Stakeholder-facing impact work | strong via AI for Social Impact | domain policy venue |
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.
- 12d ago First seen · 100 lines · 88 tokens per session scan A a7091c9e070c
aaai-topic-selection is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,090 stars, last pushed 16d ago), licensed MIT. It adds 88 tokens to every session and 1,211 once invoked, about $0.0004 per session on Opus 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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