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 ccs-artifact-evaluationgit 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/ccs-artifact-evaluation)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ccs-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-artifact-evaluation/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/ccs-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-artifact-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00783 |
| Opus 5.5 | $0.00028 | $0.00313 |
| Sonnet 5.5 | $0.00014 | $0.00157 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
ccs-artifact-evaluation 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CCS Artifact Evaluation
Use this for artifact packaging around CCS. CCS runs an optional artifact-evaluation process after acceptance; passing artifacts earn ACM badges that appear on the paper's first page. Reopen the current call for artifacts for the exact badge set, submission form, and deadlines.
The ACM badge ladder
| Badge | What it certifies | What the committee does |
|---|---|---|
| Artifacts Available | The artifact is publicly archived with a stable identifier | Confirms the artifact is retrievable and permanent |
| Artifacts Evaluated - Functional | The artifact runs and does what the paper says | Executes it against the documented steps |
| Artifacts Evaluated - Reusable | It exceeds functional quality and others can reuse it | Judges structure, documentation, and reusability |
| Results Reproduced | The main paper results are independently reproduced | Reruns experiments and checks they support the claims |
Available is about archival permanence; Functional and Reusable are about quality; Results Reproduced is the highest bar and requires the committee to regenerate your headline numbers.
Artifact plan
- Decide which claims the artifact must support: the exploit, the defense overhead, the measurement pipeline, or the protocol implementation.
- Make the security claim turnkey: one documented command per headline result, with expected output, runtime, and the hardware assumed.
- Anonymize nothing at this stage — artifact evaluation is post-acceptance and single-blind or open — but do pin versions, dependencies, and a container so it runs on a clean machine.
- For dangerous artifacts (working exploits, malware, live attack tooling), gate access, document safe-handling, and follow the responsible-disclosure posture from the paper.
- Justify anything withheld: licensing, disclosure embargo, subject safety, or premature-release risk, and offer partial, synthetic, or redacted substitutes that still let evaluators check the method.
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 · 65 lines · 69 tokens per session scan A 83ac9976e00c
ccs-artifact-evaluation is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 783 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 2026-08-30.
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