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-reproducibilitygit 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-reproducibility)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-reproducibility"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-reproducibility/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-reproducibility"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-reproducibility.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 | $0.00060 | $0.01261 |
| Opus 5.5 | $0.00024 | $0.00504 |
| Sonnet 5.5 | $0.00012 | $0.00252 |
| Haiku 4.5 | $0.00006 | $0.00126 |
Grade A, and why
acl-reproducibility 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Reproducibility
Use this before an ARR deadline and again at camera-ready. At ACL the reproducibility instrument is the Responsible NLP checklist: it is mandatory, reviewers read it alongside the paper, and ARR policy makes incorrect or misleading checklist content a desk-rejection ground. Treat it as a claims audit, not paperwork.
The checklist as a claims audit
- Section A: a real Limitations discussion and a risks discussion — reviewers are told honest limitations must not be penalized, so under-disclosing is strictly worse than disclosing.
- Section B: every dataset and model you used needs citation, version,
license, and intended-use consistency (see
acl-artifact-evaluation). - Section C: computational experiments — parameters, budget, infrastructure, hyperparameter search, and descriptive statistics with error bars.
- Section D: human annotators/participants — instructions, pay, consent, ethics-board status, demographics where relevant.
- Section E: AI assistants used in research, coding, or writing.
Every "yes" answer should carry a section/appendix pointer; every "N/A" should survive a hostile reading of the paper.
Reporting floor for the modern NLP paper
| Experiment type | Minimum disclosure that survives ACL review |
|---|---|
| Fine-tuned models | Model + version, seeds, LR/schedule, epochs, selection criterion, dev-set use, runs count |
| Prompted LLMs | Exact prompts, decoding params (temperature, top-p, max tokens), model snapshot date/version, n samples |
| API-based closed models | Access dates, version string, cost/queries, caching strategy, note on irreproducibility risk |
| Human evaluation | Instructions, item counts, raters per item, agreement statistic, pay |
| New metrics | Implementation source, correlation evidence, code in supplement |
Contamination and leakage auditing
- State which evaluation sets could plausibly appear in pretraining corpora and what you did about it: n-gram overlap scans, canary checks, dataset release date vs model cutoff reasoning.
- For benchmarks you release, record a creation date and content hash so future contamination is auditable.
- For claimed generalization, verify the test languages/domains genuinely weren't leaked through translation or paraphrase of training data.
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 · 121 lines · 60 tokens per session scan A 4e980fdc9a2c
acl-reproducibility is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,261 once invoked, about $0.0002 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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