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 acl-supplementarygit 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/acl-supplementary)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-supplementary"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-supplementary/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/acl-supplementary"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-supplementary.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 | $0.00067 | $0.01242 |
| Opus 5.5 | $0.00027 | $0.00497 |
| Sonnet 5.5 | $0.00013 | $0.00248 |
| Haiku 4.5 | $0.00007 | $0.00124 |
Grade A, and why
acl-supplementary 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Supplementary
Use this when splitting an ACL paper between body, appendix, and archive. The governing ARR principle: reviewers are not required to consider material in appendices or supplements, so anything decision-critical that lives only there is effectively invisible.
The ACL page anatomy
[ content pages: 8 long / 4 short ] <- the reviewed argument lives here
[ Limitations (REQUIRED, unlimited) ] <- after conclusion, outside page count
[ Ethics statement (optional) ]
[ References (unlimited) ]
[ Appendices (unlimited, same PDF) ] <- optional reading for reviewers
+ separate .tgz/.zip archive <- software / data supplement
Missing Limitations is a desk-reject condition; treating it as one throwaway sentence is a review-stage penalty even when it passes the gate.
What must not leave the body
- The main results table and the headline comparison.
- Task definition and enough of the method that a reviewer can judge novelty.
- At least a summary of the error analysis — a pointer-only error analysis reads as not having one.
- Human-evaluation design in one paragraph: raters, items, agreement.
- The experimental setup at reproduction-outline level; full grids can go down.
What appendices are good at
- Full prompt texts and few-shot exemplars (reference them per experiment).
- Complete hyperparameter tables and search ranges.
- Per-language / per-dataset breakdowns behind an averaged headline number.
- Annotation guidelines and interface screenshots.
- Extended qualitative examples and additional ablations.
- Proofs or derivations for the occasional formal result.
Limitations section that actually works
| Weak pattern | Stronger ACL pattern |
|---|---|
| "Results may not generalize" | Name the languages, domains, and model scales actually tested and the nearest untested regime |
| "LLMs can hallucinate" | State which conclusions depend on a specific model snapshot and API behavior |
| Silent on data | Note license constraints, demographic skew, or collection-window bias in the corpora used |
| Written last-minute | Mirrors the risks reviewers will find anyway, defusing them on your terms |
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 · 123 lines · 67 tokens per session scan A a25605d12c05
acl-supplementary is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 1,242 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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