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 xjtulyc/awesome-rosetta-skills --skill systematic-reviewgit clone --depth 1 https://github.com/xjtulyc/awesome-rosetta-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/xjtulyc/awesome-rosetta-skills/systematic-review)<a href="https://agentmods.dev/skills/xjtulyc/awesome-rosetta-skills/systematic-review"><img src="https://agentmods.dev/badge/skills/xjtulyc/awesome-rosetta-skills/systematic-review/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/xjtulyc/awesome-rosetta-skills/systematic-review"><img src="https://agentmods.dev/badge/skills/xjtulyc/awesome-rosetta-skills/systematic-review.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 | $0.00047 | $0.06594 |
| Opus 5 | $0.00023 | $0.03297 |
| Sonnet 5 | $0.00009 | $0.01319 |
| Haiku 4.5 | $0.00005 | $0.00659 |
Grade A, and why
systematic-review scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(f"{NCBI_BASE}/esearch.fcgi", params=params, timeout=30) The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 12d ago First seen · 742 lines · 47 tokens per session scan A 3a0eeb78d11f
systematic-review is a skill published in the GitHub repository xjtulyc/awesome-rosetta-skills (34 stars, last pushed 5mo ago), with no licence file. It adds 47 tokens to every session and 6,594 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
environment-life-review-forge
Adapts evidence synthesis workflows for environmental, ecological, biomedical, and life-science questions. Use for PECO/PICO frameworks, exposure-outcome reviews, ecological heterogeneity, dose-response evidence, risk-of-bias planning, environmental indicators, NDVI or vegetation-index models, partial least squares…
meta-analysis-forge
Designs and audits first-order meta-analyses of primary studies. Use for effect-size extraction, effect-size harmonization, fixed/random/multilevel models, robust variance estimation, heterogeneity, prediction intervals, meta-regression, publication-bias diagnostics, sensitivity checks, coding sheets, reproducible…
evidence-synthesis-forge
Orchestrates systematic reviews, scoping reviews, evidence maps, meta-analyses, umbrella reviews, and AI-assisted evidence synthesis. Use when designing protocols, eligibility criteria, search strategies, screening workflows, coding manuals, effect-size plans, synthesis reports, or reproducible evidence-review…
umbrella-review-skeptic
Reviews umbrella reviews and second-order meta-analyses. Use when synthesizing existing systematic reviews/meta-analyses, assessing primary-study overlap, duplicate evidence, review quality, AMSTAR/ROBIS-style concerns, discordant conclusions, temporal second-order meta-regression, ecosystem-service trade-offs, and…
meta-ml-screener
Designs machine-learning assisted systematic review workflows. Use for active-learning screening, deduplication, study classification, LLM-assisted extraction, risk-of-bias triage, topic modeling, moderator discovery, and audit logs, while preserving human verification and transparent evidence-synthesis decisions.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…