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 deep-researchgit 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/deep-research)<a href="https://agentmods.dev/skills/thu-maic/openmaic/deep-research"><img src="https://agentmods.dev/badge/skills/thu-maic/openmaic/deep-research/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/deep-research"><img src="https://agentmods.dev/badge/skills/thu-maic/openmaic/deep-research.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 | $0.00099 | $0.01742 |
| Opus 5 | $0.00049 | $0.00871 |
| Sonnet 5 | $0.00020 | $0.00348 |
| Haiku 4.5 | $0.00010 | $0.00174 |
Grade A, and why
deep-research 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 9d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research course design
You are designing a course whose content rests on facts you must verify, not recall. Research first, outline second, generate third. A number, date, name or finding enters the course only because you saw it in a source you fetched or in the user's own material — and you can say which one.
Structure
- Open with one
slide: it frames the research question and previews what kind of evidence the course will examine. Not a definitions or history-of-the-field page. - The body carries the findings, in whatever scene types fit: slides for
sourced exposition,
interactivefor evidence the learner can inspect,quizfor checking whether the learner can tell a supported claim from an unsupported one. - Close on what the evidence establishes and where it runs out — not on a generic summary.
Step 1 — Start from what the session already has
Call list_materials before any search. Materials the user attached —
documents, links, data, recordings — are the primary authority on their own
subject; web research supplements them, it does not replace them. If a
derivative is still extracting, extract_material or wait_for_materials as
in any other course. URLs the user pasted in chat can be fetched directly with
fetch_url.
Step 2 — Split the topic into facets
Break the request into 2–4 searchable facets — distinct questions the course must answer with evidence. Typical facets: current state or latest developments; authoritative figures and baseline data; concrete cases and incidents; risks, controversies or open questions. Write the facet list down before searching. Not every facet needs a search: a facet that is stable textbook knowledge is skipped and taught as such.
Step 3 — Search with a budget
- At most 8
web_searchcalls for the whole session, planned across the facets. One precise query beats several vague ones; write queries in the language of the course. - The session shares one run with planning,
set_rosterand every page's generation, and every extra call is latency the user watches. Research is one slice of the run, not the main act. If the run gets long, cut facets — never generation. - Read each result before searching again: the next query should be shaped by what the last one returned, not a rewording of it.
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.
- 9d ago First seen · 158 lines · 99 tokens per session scan A 063ce7f57f17
deep-research is a skill published in the GitHub repository THU-MAIC/OpenMAIC (33,238 stars, last pushed 2d ago), licensed MIT. It adds 99 tokens to every session and 1,742 once invoked, about $0.0005 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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