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 ase-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/ase-experiments)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-experiments"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-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/ase-experiments"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-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 | $0.00067 | $0.01117 |
| Opus 5.5 | $0.00027 | $0.00447 |
| Sonnet 5.5 | $0.00013 | $0.00223 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
ase-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 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Experiments
Match the evidence to the automation's claim. ASE evaluations are judged on whether a tool or technique actually does what it claims on real subjects, compared fairly against the closest runnable automation. This is the axis reviewers weight most, and the one that most often becomes a Revision criterion.
Start from the claim shape
Different automations demand different evidence:
| Automation claim | Evidence that matches | Common failure |
|---|---|---|
| Detection (bugs, smells, vulnerabilities) | Precision/recall/F on real defects with a defined ground truth | Synthetic-only defects; unclear ground truth |
| Generation / synthesis (tests, code, patches) | Validity of the produced artifact (compiles, passes, holds the property) | Similarity-to-reference proxy instead of validity |
| Repair | Verified behavior change: re-run + oracle; assertion/spec preservation | "Plausible patch" without an overfitting check |
| Localization / ranking | Rank-based effectiveness on real faults vs. alternatives | Cherry-picked programs; one metric only |
| Scalability / performance | Real-system sizes, wall-clock with a fair config | Toy inputs; unequal baseline budget |
Real subject systems
- Use real software — open-source projects, real bug/defect datasets, real CI logs — not toy programs you constructed to make the tool look good.
- Report subject provenance: names, versions/commit SHAs, sizes, and the extraction date. Reviewers reproduce from this.
- Justify subject selection and disclose exclusions; self-selected subjects are the classic external-validity threat.
Fair, runnable tool baselines
- Compare against the closest runnable automation, configured at an equal, documented budget (time, iterations, tuning, seeds). ASE reviewers routinely rerun or scrutinize baselines.
- Pin baseline versions/commits and note reimplementation vs. original.
- If no tool baseline exists, construct a defensible non-trivial baseline (a static rewrite, a random or heuristic variant) rather than comparing only to "nothing."
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 · 98 lines · 67 tokens per session scan A a705ca4c5a19
ase-experiments is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It adds 67 tokens to every session and 1,117 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.
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