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 atc-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/atc-reproducibility)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/atc-reproducibility"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/atc-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/atc-reproducibility"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/atc-reproducibility.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 | $0.00078 | $0.00987 |
| Opus 5.5 | $0.00031 | $0.00395 |
| Sonnet 5.5 | $0.00016 | $0.00197 |
| Haiku 4.5 | $0.00008 | $0.00099 |
Grade A, and why
atc-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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ATC Reproducibility
Build the reproducibility story alongside the system, not at the deadline. ATC has an active
artifact culture inherited from USENIX: reviewers expect a runnable, anonymized artifact at review
time, and after acceptance an Artifact Evaluation Committee awards Available / Functional /
Reproduced badges (see atc-artifact-evaluation). The through-line is that a systems result other
people can re-run is worth more than one they must take on faith — and systems provenance cannot be
reconstructed after the fact.
Pin what you cannot reconstruct
Record these at collection time; none can be recovered at the deadline:
[Hardware] CPU/NIC/SSD models, core/memory counts, firmware/BIOS where it matters
[OS/kernel] kernel version, distro, relevant sysctl/tuning, hugepages/NUMA settings
[Toolchain] compiler, library, and runtime versions; build flags
[Workload] trace source + extraction date, generator version + seeds, request mix
[Method] warm-up window, measurement duration, run count, aggregation method
[Code] commit SHAs for your system and every baseline; patches applied
A turnkey path to the headline numbers
The single most valuable artifact property is that an evaluator can regenerate your paper's main figures and tables:
- Ship a claim-to-experiment map: paper claim → script → expected figure/table → expected runtime.
- Provide a one-command entry point per headline result (
./run_fig3.sh) that does setup, run, and plot. - Give a small-scale mode for evaluators who lack your hardware (fewer nodes, a trace sample), and state clearly which results are full-scale-only and why.
- Log expected outputs and tolerances so an evaluator knows what "reproduced" looks like given measurement noise.
Pinned, portable environments
- Prefer a container (Dockerfile) or a pinned environment (lockfile,
requirements, Nix) over "install these 30 packages by hand." - Where the result depends on kernel features or hardware (RDMA, SPDK, io_uring, specific NICs), say so explicitly and document the required host, since a container cannot abstract the hardware away.
- Include traces/datasets (or documented, durable access), not just the query that produced them.
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 · 82 lines · 78 tokens per session scan A 2be6ce06dae9
atc-reproducibility is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,264 stars, last pushed 19d ago), licensed MIT. It adds 78 tokens to every session and 987 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
council
Five independent critic subagents in parallel on an idea, plan, design, R&R strategy, grant, or SKILL.md, then a synthesis pass that ranks findings by how much of the argument rests on them, never by vote count. A markdown work plan gets the plan roster (assumptions, pre-mortem, what to delete, alternatives per piece…
make-pr-easy-to-review
Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.
security-review
Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging.…
one-way-door
Flags irreversible decisions before commit. Use for data models, infra, auth boundaries, API contracts, event schemas, CI/CD.
alterlab-depmap
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or…