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 acmmm-submissiongit 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/acmmm-submission)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-submission"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-submission/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/acmmm-submission"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-submission.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00077 | $0.01277 |
| Opus 5.5 | $0.00031 | $0.00511 |
| Sonnet 5.5 | $0.00015 | $0.00255 |
| Haiku 4.5 | $0.00008 | $0.00128 |
Grade A, and why
acmmm-submission 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACM MM Submission
Use this to run a pre-deadline audit of an ACM Multimedia paper. Reopen the current Call for Technical Papers, the Topics of Interest page, each relevant track call, and the OpenReview group before giving deadline-ready advice — ACM MM rewrites these each edition.
Submission audit
- Confirm the target is the ACM MM main conference (or a specific track), not a co-located workshop, ACM MM Asia, MMSys, or ICMR.
- Choose the thematic area deliberately: it routes the paper to a reviewer pool, and a mismatched area is a quiet source of off-topic reviews.
- Apply the current ACM
sigconftemplate with no margin, font, or spacing edits. ACM MM 2026 used a 6–8 page body; references may spill onto up to two additional pages that contain only references. - Enforce double-blind anonymity across the paper, appendix, supplement, code, media assets, filenames, PDF metadata, and repository links — unless submitting to the named single-blind tracks (Reproducibility, Open Source Software, Dataset), where the artifact's identity is expected.
- Register the abstract before the abstract deadline; the paper and the supplement have their own later deadlines (in 2026: abstract Mar 25, paper Apr 1, supplement Apr 8, AoE).
- Check concurrent-submission rules and prior-publication status; do not keep the same work under review at another archival venue when ACM MM forbids it.
Blocking risks
- Late abstract registration or paper upload.
- Overlength body or a tampered sigconf template.
- Identity leak in the PDF, media metadata, demo watermark, or repository link (on a double-blind track).
- Wrong track blinding — anonymizing a Dataset/Open-Source submission, or de-anonymizing a main-track one.
- Non-rendering or proprietary-format supplementary media.
- Dual submission to another archival venue.
Desk-reject and triage table
| Trigger | Severity at ACM MM | Repair window |
|---|---|---|
| Overlength body (past 8 pages) | Desk reject | None after the deadline |
| Non-references content on the overflow pages | Desk reject or chair flag | Before the deadline only |
| Author identity in a double-blind PDF/media | Desk reject | None |
| Wrong thematic area | Off-topic reviews, not a desk reject | Fixable before the paper deadline |
| Supplement that will not play for a reviewer | Lost evidence | Fixable before the supplement deadline |
| Missing OpenReview coauthor profiles | Submission blocked | Before the deadline |
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 · 106 lines · 77 tokens per session scan A e51dbc7b87bb
acmmm-submission is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 1,277 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 2026-09-15.
Other skills, from other repositories
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-datamol
Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel batch processing, returning native…
alterlab-pyhealth
Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals…
alterlab-eda
Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its…
alterlab-biorxiv
Search the bioRxiv preprint server and retrieve paper metadata or download PDFs via its API. Use when finding life sciences preprints by keywords, authors, DOI, date ranges, or categories, or when conducting a biology literature review of not-yet-peer-reviewed work. Part of the AlterLab Academic Skills suite.
alterlab-gnomad
Query gnomAD (Genome Aggregation Database) for population allele frequencies and gene constraint scores (pLI, LOEUF) reflecting loss-of-function intolerance. Use when checking how common a variant is across populations, filtering rare-disease candidate variants, assessing variant pathogenicity, or identifying…