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 Rheinmir/setup --skill dym-scientific-agent-skills-tham-dinh-truoc-khi-caigit clone --depth 1 https://github.com/Rheinmir/setupWrote 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/rheinmir/setup/dym-scientific-agent-skills-tham-dinh-truoc-khi-cai)<a href="https://agentmods.dev/skills/rheinmir/setup/dym-scientific-agent-skills-tham-dinh-truoc-khi-cai"><img src="https://agentmods.dev/badge/skills/rheinmir/setup/dym-scientific-agent-skills-tham-dinh-truoc-khi-cai/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/rheinmir/setup/dym-scientific-agent-skills-tham-dinh-truoc-khi-cai"><img src="https://agentmods.dev/badge/skills/rheinmir/setup/dym-scientific-agent-skills-tham-dinh-truoc-khi-cai.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 | $0.00093 | $0.02084 |
| Opus 5.5 | $0.00037 | $0.00834 |
| Sonnet 5.5 | $0.00019 | $0.00417 |
| Haiku 4.5 | $0.00009 | $0.00208 |
Grade A, and why
dym-scientific-agent-skills-tham-dinh-truoc-khi-cai 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 yesterday.
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.
grep -rnE 'requests\.|urlopen|subprocess|os\.system|curl |wget ' skills/literature-review/ How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: dym-scientific-agent-skills-tham-dinh-truoc-khi-cai — Thẩm định một skill TRƯỚC khi cài
Vì sao dùng: Agent Skill là chỉ dẫn mà agent của bạn sẽ tuân theo — nó có thể bảo agent chạy code tuỳ ý, cài package, gọi mạng, sửa file. Repo này nhận đóng góp cộng đồng và tự nói thẳng là không đảm bảo đã soát hết. Đây là bước bắt buộc trước 01. Sinh ra cái gì: một quyết định cài/không cài cho từng skill, dựa trên nội dung skill + báo cáo quét của repo + (tuỳ chọn) một lần quét cục bộ của chính bạn.
Bước 1 — Đọc SKILL.md mà không cài
# render SKILL.md trong terminal, không ghi file nào
gh skill preview K-Dense-AI/scientific-agent-skills literature-review
# ghim một version cụ thể để đọc
gh skill preview K-Dense-AI/scientific-agent-skills [email protected]
Chạy tương tác, gh skill preview còn cho picker để duyệt thêm scripts/, references/. Đây mới là chỗ cần soi: một skill xấu hiếm khi xấu ở SKILL.md, nó xấu trong scripts/.
Cần đọc bằng shell (grep, diff) thì clone read-only rồi soi:
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sas
cd /tmp/sas
# skill này chạm mạng ở đâu, gọi shell ở đâu
grep -rnE 'requests\.|urlopen|subprocess|os\.system|curl |wget ' skills/literature-review/
# skill này đòi biến môi trường nào
grep -rn 'environ\|getenv\|API_KEY' skills/literature-review/
Bước 2 — Tra báo cáo quét của repo
Repo chạy Cisco AI Defense Skill Scanner hằng tuần (cron Thứ Hai 09:00 UTC, .github/workflows/security-scan.yml), quét gia tăng — skill không đổi thì mang kết quả cũ sang; rescan toàn bộ ít nhất mỗi 30 ngày hoặc khi đổi scanner/model.
- Người đọc:
docs/security-report.md - Máy đọc (có
last_scannedtừng skill):docs/security-report.json
Tổng ở lần quét 2026-08-31 (đọc từ docs/security-report.json → totals):
163 skill · 988 finding · CRITICAL 34 · HIGH 9 · MEDIUM 241 · LOW 703 · INFO 1
skill "an toàn": 147/163
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.
- yesterday First seen · 137 lines · 93 tokens per session scan A 07261f6d06d9
dym-scientific-agent-skills-tham-dinh-truoc-khi-cai is a skill published in the GitHub repository Rheinmir/setup (11 stars, last pushed 20d ago), licensed MIT. It adds 93 tokens to every session and 2,084 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-11-07.
dym-scientific-agent-skills-tham-dinh-truoc-khi-cai compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dym-scientific-agent-skills-tham-dinh-truoc-khi-caiRheinmir/setup | Skill | 11 | 20d ago | MIT | A | 93 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |