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 agentmods add skills/sno-ai/mda/intronpx skills add sno-ai/mda --skill introgit clone --depth 1 https://github.com/sno-ai/mdaWrote 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/sno-ai/mda/intro)<a href="https://agentmods.dev/skills/sno-ai/mda/intro"><img src="https://agentmods.dev/badge/skills/sno-ai/mda/intro.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.00274 |
| Opus 5 | $0.00018 | $0.00137 |
| Sonnet 5 | $0.00007 | $0.00055 |
| Haiku 4.5 | $0.00004 | $0.00027 |
Grade A, and why
intro-example 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 6d 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.
What it actually says
Intro
This is a minimal MDA source file. It demonstrates MDA-extended frontmatter fields at the top level and the relationship-graph footnote pattern.
A relationship
This document references the SKILL.md target schema as its conceptual parent1.
Footnotes
-
{"rel-type": "parent", "doc-id": "spec-skill-md-v1.0", "rel-desc": "MDA SKILL.md target schema"} ↩
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.
- 6d ago First seen · 31 lines · 37 tokens per session scan A 640afa6ffa03
intro-example is a skill published in the GitHub repository sno-ai/mda (616 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 274 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
omd-video
视频→逐段结构化笔记 (MiMo-v2.5 原生吃画面+音频, 非 whisper 转写; 可重入管线)。讲解/课程视频里 PPT 框架图/代码/提示词是画面独有、音频拿不到的信息。产 ALL-NOTES.md 交 /omd-council 或 dagresearch 做综合。Trigger:/omd-video、抖音/B站/YouTube 讲解视频、课程系列、把这些视频学一遍/提炼、画面里有代码/图表/PPT。Skip:文字原文综合→/omd-council;网页内容→dagresearch(检索版)。.
canvas-course-ops
Use this skill for course, assignment, and LMS-style operational tasks that need clear status tracking and student-safe messaging.
teach
Teach the user a new skill or concept, within this workspace.
neuron-structured-output
Design and implement structured output classes for Neuron AI agents using SchemaProperty attributes and validation rules. Use this skill when the user mentions structured output, JSON schema extraction, data validation, output classes, DTOs for AI responses, extracting structured data from LLM, or configuring property…
agent-evaluation
Run one specified Test Agent on one specified Benchmark Case exactly once, privately score that execution, and return one protocol result.
agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a…