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-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/acl-artifact-evaluation)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-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/acl-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-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 | $0.00063 | $0.01221 |
| Opus 5.5 | $0.00025 | $0.00488 |
| Sonnet 5.5 | $0.00013 | $0.00244 |
| Haiku 4.5 | $0.00006 | $0.00122 |
Grade C, and why
acl-artifact-evaluation scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf .git; # or re-init a fresh repo for the archive copy How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Artifact Evaluation
Use this to plan the evidence package around an ACL paper. ACL has no separate artifact-badge track; instead, artifact scrutiny is folded into review through the supplement archive and Section B ("scientific artifacts") of the Responsible NLP checklist, which reviewers cross-check against the PDF.
What counts as an artifact here
- Code: training/inference scripts, evaluation harnesses, prompt templates.
- Data: new corpora, annotations, filtered subsets of existing corpora, test suites, adversarial sets.
- Model outputs: generations, ranked lists, logits used in analysis — often the cheapest way to make an LLM paper checkable without GPUs.
- Human-subject materials: annotation guidelines, interface screenshots, consent text, compensation description.
Submission-time packaging rules
- Supplements upload as .tgz/.zip through the OpenReview form; links to tracked cloud storage are not acceptable, and any linked page must be anonymous.
- Scrub identity everywhere reviewers can look: file paths, git metadata, notebook author fields, license headers, dataset hosting pages, README contact lines.
- Reviewers are not required to open supplements. The paper plus checklist must stand alone; the archive is for verification, not for essential content.
Checklist items your artifact must satisfy
| Responsible NLP item (Section B) | Artifact implication |
|---|---|
| Cited creators + versions of used artifacts | Pin dataset/model versions in the README and bibliography |
| License / terms of use stated | Include the license you release under and those you consumed under |
| Use consistent with intended use | Justify research use of scraped or user-generated data |
| PII and offensive content handled | Describe scanning/anonymization steps actually performed |
| Documentation of domains, languages, demographics | Ship a data statement or datasheet, not just row counts |
| Statistics on splits reported | Train/dev/test sizes in both paper and README |
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 · 124 lines · 63 tokens per session scan C a866cc248683
acl-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 63 tokens to every session and 1,221 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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