dym-scientific-agent-skills-dung-skill-trong-agent

dym-scientific-agent-skills-dung-skill-trong-agent is a skill for Claude Code from Rheinmir/setup. It costs 72 tokens per session (2,233 once invoked), scanned A, original, MIT.

A guide for telling a coding agent which installed skill to use and how to ask it to perform several steps. It explains that the request is written in the agent's chat, not entered as a shell command.

In plain words
What is it for?
Use it to name the intended skill, resolve overlap between related skills, and request outputs such as tables, diagrams, reports, or data files.
Why use it?
Installed skills can be overlooked or confused with similar ones when a request is vague.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions AGENTS.md; built for openclaw.

Good fit Use it to name the intended skill, resolve overlap between related skills, and request outputs such as tables, diagrams, reports, or data files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent
Install

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.

Any agent
npx skills add Rheinmir/setup --skill dym-scientific-agent-skills-dung-skill-trong-agent
Clone the repo
git clone --depth 1 https://github.com/Rheinmir/setup

Made for: Claude Code.

Wrote 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.

agentmods badge for dym-scientific-agent-skills-dung-skill-trong-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent/github.svg)](https://agentmods.dev/skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent)
Your own site
<a href="https://agentmods.dev/skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent"><img src="https://agentmods.dev/badge/skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent/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.

agentmods 80×15 button for dym-scientific-agent-skills-dung-skill-trong-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent"><img src="https://agentmods.dev/badge/skills/rheinmir/setup/dym-scientific-agent-skills-dung-skill-trong-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,233 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.02233
Opus 5.5 $0.00029 $0.00893
Sonnet 5.5 $0.00014 $0.00447
Haiku 4.5 $0.00007 $0.00223

Measured yesterday against content hash 57ee7946bba2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dym-scientific-agent-skills-dung-skill-trong-agent 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 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.

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.

.overstack/doyourmagic/scientific-agent-skills/skills/dym-scientific-agent-skills-dung-skill-trong-agent/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: dym-scientific-agent-skills-dung-skill-trong-agent — Dùng skill trong agent (CHỈ prompt, không phải lệnh shell)

Mọi thứ trong file này gõ vào ô chat của agent, không gõ vào terminal. Lệnh shell nằm ở 04-chay-script-bundled-tu-shell.md. Lẫn hai file này là nguồn lỗi copy-paste phổ biến nhất.

Vì sao dùng: skill đã cài rồi vẫn có thể không được agent chọn, hoặc chọn nhầm skill anh em. File này là cách gọi trúng, và cách viết prompt để agent chạy được nhiều bước. Sinh ra cái gì: các artifact do skill định nghĩa — bảng, hình, báo cáo markdown/PDF, file dữ liệu — trong workspace của bạn.


Skill được chọn thế nào

Host so khớp yêu cầu của bạn với trường description trong frontmatter mỗi SKILL.md. Không có lệnh /skill-name — bạn gọi tên skill trong câu văn.

Dùng skill depmap. Gene nào là dependency chọn lọc trong dòng tế bào ung thư
tuỵ so với toàn bộ panel DepMap?

Khi hai skill phủ nhau, nêu đích danh cái bạn muốn. Chính repo lấy ví dụ: phylogenetics dựng cây, etetoolkit phân tích cây có sẵn; scanpy chạy phân tích, anndata định nghĩa format. Nêu tên xoá được mơ hồ thay vì trông chờ router đoán đúng.

Xem agent đang có skill nào (vẫn là prompt):

Liệt kê các skill bạn đang nạp có liên quan tới genomics, kèm description của từng cái.

Khung prompt 5 phần (lấy từ docs/examples.md)

Repo chỉ rõ 5 yếu tố tách một workflow agent chạy tốt khỏi một workflow agent chạy mơ hồ:

  1. Gọi tên skill bạn muốn.
  2. Nêu tiêu chí quyết định, không chỉ các bước. "Lọc variant" là thiếu; "giữ QUAL > 30 và DP > 20, rồi báo mỗi bộ lọc loại bao nhiêu variant" là chạy được và kiểm toán được.
  3. Khai hợp đồng output trước khi làm. Nói trước "bảng xếp hạng, mỗi dòng một ứng viên, có cột pIC50 dự đoán, khoảng 90%, và láng giềng gần nhất trong tập huấn luyện".
  4. Đòi provenance và đòi cả kết quả âm. Hỏi database nào, tham số gì, ngày nào — và hỏi thẳng phân tích không tìm thấy gì. Workflow chỉ biết báo hit thì sẽ chỉ báo hit.
  5. Tách kế hoạch khỏi hành động không thể hoàn tác. Bất kỳ việc gì ghi lên hệ thống từ xa, tiêu tiền, chuyển dữ liệu ra ngoài, hoặc điều khiển thiết bị vật lý phải nằm ở prompt riêng, sau khi bạn đã đọc kế hoạch.

Read the full file on GitHub · 121 lines

Changes

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.

  1. yesterday First seen · 121 lines · 72 tokens per session scan A 57ee7946bba2

Subscribe to this mod's changes

dym-scientific-agent-skills-dung-skill-trong-agent is a skill published in the GitHub repository Rheinmir/setup (11 stars, last pushed 20d ago), licensed MIT. It adds 72 tokens to every session and 2,233 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 2026-11-07.

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