OpenMAIC is a multi-agent classroom platform that uses agents to plan, build, and revise interactive courses from prompts and uploaded materials. It is designed for immersive learning experiences and supports course components such as slides, quizzes, interactives, projects, images, video, voices, and PowerPoint imports, with catalogue skills covering its workflows.
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 THU-MAIC/OpenMAIC --skill teacher-style-clonegit clone --depth 1 https://github.com/THU-MAIC/OpenMAICWrote 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/thu-maic/openmaic/teacher-style-clone)<a href="https://agentmods.dev/skills/thu-maic/openmaic/teacher-style-clone"><img src="https://agentmods.dev/badge/skills/thu-maic/openmaic/teacher-style-clone/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/thu-maic/openmaic/teacher-style-clone"><img src="https://agentmods.dev/badge/skills/thu-maic/openmaic/teacher-style-clone.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 | $0.00022 | $0.00833 |
| Opus 5 | $0.00011 | $0.00417 |
| Sonnet 5 | $0.00004 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
teacher-style-clone 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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Teacher style extraction and transfer
Extract how the teacher teaches, then teach the user's requested topic in that style. The source material is evidence about delivery and presentation; it is not automatically factual source material for the new course.
The learner-facing name for this mode is 「名师风格」. Never expose the words "clone" or "style transfer" to the learner. The result should simply feel like a lesson taught in the evidenced style.
Step 1 — confirm every derivative is ready
Call list_materials before drawing conclusions. Identify the attached source
materials and confirm extraction is complete. For recordings, verify that the
session exposes the transcript and keyframe derivatives. If extraction is idle,
call extract_material first; if it is pending or running, call
wait_for_materials and list again; if it failed, tell the user what failed instead
of inventing a profile from incomplete evidence.
Step 2 — read the whole transcript in order
Use read_material to page through every transcript derivative from offset 0
to the final page. Do not sample only the beginning. As you read, build a
compact style profile in your reasoning covering:
- openings and transitions;
- catchphrases, address terms, and sentence rhythm;
- concrete-versus-abstract example habits;
- pacing, recaps, rhetorical questions, pauses, and humor;
- what is said aloud versus emphasized visually.
Support each claim with short transcript evidence and its timestamp. Frequency and distribution matter: distinguish a repeated habit from a phrase used once.
Step 3 — inspect keyframes for visual style
When the transcript contains a [keyframe@mm:ss](mat_xxx) marker, sample the
referenced frame with read_material. Use frames across the recording rather
than adjacent duplicates. Record visual evidence such as board-writing density,
slide composition, hierarchy, color use, teacher position, gestures, and the
relationship between speech and what remains on screen.
Do not infer visual style when the recording has no readable keyframes.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 84 lines · 22 tokens per session scan A 9bf4ae1bb9a4
teacher-style-clone is a skill published in the GitHub repository THU-MAIC/OpenMAIC (34,007 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 833 once invoked, about $0.0001 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.
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