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 asplos-author-responsegit 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/asplos-author-response)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/asplos-author-response"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/asplos-author-response/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/asplos-author-response"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/asplos-author-response.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 | $0.00066 | $0.01550 |
| Opus 5 | $0.00033 | $0.00775 |
| Sonnet 5 | $0.00013 | $0.00310 |
| Haiku 4.5 | $0.00007 | $0.00155 |
Grade A, and why
asplos-author-response 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 today.
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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASPLOS Author Response
The 2027 CFP defines the rebuttal's job narrowly: (1) correct factual errors in the reviews, and (2) answer the questions reviewers posed. There is no hard length cap, but reviewers are not expected to read past 800 words (verified 2026-07-08). Both facts should shape every drafting decision below. The 2027 windows are fixed and short — July 6-9, 2026 for the April cycle (open on this pack's check date) and December 1-4, 2026 for the September cycle.
Hour one: triage before prose
Sort every review remark into exactly one bucket:
| Bucket | Definition | Response posture |
|---|---|---|
| Factual error | The review misstates what the paper says, measures, or assumes | Correct it, with a section/figure/table pointer — highest priority, CFP-sanctioned |
| Direct question | The reviewer asked for a clarification | Answer in ≤ 3 sentences, citing where the paper already contains the material |
| Evidence dispute | The reviewer doubts a result or baseline | Point to the existing run/ablation that addresses it; concede if none exists |
| Judgment call | "Not novel enough", "fit is marginal" | One calm paragraph max, or zero — rebuttals rarely move taste |
| Revision seed | A fixable gap you agree with | Acknowledge + state the concrete fix; this is you negotiating the Major Revision terms |
The last bucket is ASPLOS-specific leverage: because the decision set includes Major Revision, a response that shows requested work is scoped and feasible in six weeks gives the committee a reason to choose revision over rejection.
The 800-word budget
Assume only the first 800 words are read; structure so truncation is harmless:
Words 1- 80 Global note: 1-2 sentences of thanks + the single most
important correction, stated flatly with a pointer.
Words 80-560 Numbered items, worst objection first. Format per item:
[R2-Q1] Claim/question -> answer -> evidence pointer (§, Fig, Tab).
Words 560-800 Revision seeds: "If given the opportunity, we will X, Y, Z" —
each one concrete, bounded, and honest about what exists today.
Overflow Optional detail annex, clearly marked; assume unread.
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.
- today First seen · 128 lines · 66 tokens per session scan A af8bb6741330
asplos-author-response is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,109 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,550 once invoked, about $0.0003 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 2026-09-15.
Other skills, from other repositories
alterlab-qiime2-amplicon
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 amplicon distribution (2026.1; renamed to "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier classify-sklearn against a…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-phylogenetics
Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 2 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstructing trees from sequences (FASTA) for…
alterlab-pydicom
Reads, writes, and manipulates DICOM (Digital Imaging and Communications in Medicine) medical imaging files with the pydicom Python library. Use when reading/writing/modifying DICOM data, extracting pixel data from CT, MRI, X-ray, or ultrasound images, anonymizing DICOM files, working with DICOM metadata and tags…
alterlab-shap
Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing…
alterlab-timesfm
Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales, sensors, energy, vitals, weather) without training a custom…