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-experimentsgit 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-experiments)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/asplos-experiments"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/asplos-experiments/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-experiments"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/asplos-experiments.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 | $0.00069 | $0.01621 |
| Opus 5 | $0.00034 | $0.00811 |
| Sonnet 5 | $0.00014 | $0.00324 |
| Haiku 4.5 | $0.00007 | $0.00162 |
Grade A, and why
asplos-experiments 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 Experiments
An ASPLOS evaluation answers to three communities at once: architects who will audit the modeling, OS people who will audit the workload realism, and PL people who will audit what the software layer actually does. The section's core discipline is matching each claim to an instrument whose error model can carry it — and saying what that error model is.
The instrument ladder
| Instrument | What it can prove | What it cannot | Must be reported |
|---|---|---|---|
| Real silicon | End-to-end effects, OS interactions, true tails | Designs needing hardware that doesn't exist | CPU/stepping, kernel + config, microcode, BIOS knobs (SMT/turbo/prefetchers), memory topology |
| FPGA prototype | Feasibility, cycle behavior of new logic at the prototype's clock | Absolute performance of an ASIC-class part | Board, clock, resource utilization, what was scaled down and why |
| Cycle-level simulator (e.g. gem5-class) | Relative effects of microarchitectural change under stated configs | Anything outside modeled fidelity — I/O, OS noise, firmware behavior are commonly stylized | Simulator + exact version/commit, config files, warm-up and region-selection method, validation against a real machine where possible |
| Analytical/energy models (McPAT-class, first-order area) | Trend-level energy/area comparisons | Absolute mW or mm² as truth | Model version, technology node assumptions, and the claim written as trend not absolute |
The cardinal sin is a claim-instrument mismatch: absolute latency claims from an unvalidated simulator, or OS-interaction claims from a user-space harness. Rapid and full reviewers both hunt for it.
Cycle-accuracy caveats are content, not apology
When simulation carries a claim, the paper must state: which structures are modeled in detail vs stylized; how simulation regions were chosen (full runs, checkpoints, sampled regions à la SimPoint-style methodology); how long the warm-up was; and — strongest of all — a validation experiment showing the simulator tracks a real machine on a measurable subset. A one-paragraph validation against silicon buys credibility that no amount of extra benchmarks can.
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 · 69 tokens per session scan A 2aa665fa1341
asplos-experiments is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,109 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 1,621 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.
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