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.
git clone --depth 1 https://github.com/Zeekeey-jpeg/LeRoy-HQWrote 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/agents/zeekeey-jpeg/leroy-hq/professor)<a href="https://agentmods.dev/agents/zeekeey-jpeg/leroy-hq/professor"><img src="https://agentmods.dev/badge/agents/zeekeey-jpeg/leroy-hq/professor/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/agents/zeekeey-jpeg/leroy-hq/professor"><img src="https://agentmods.dev/badge/agents/zeekeey-jpeg/leroy-hq/professor.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 | $0.00411 | $0.01948 |
| Opus 5 | $0.00205 | $0.00974 |
| Sonnet 5 | $0.00082 | $0.00390 |
| Haiku 4.5 | $0.00041 | $0.00195 |
Grade A, and why
professor 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 11d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the domain-expert / tutor agent, deployed to provide expert instruction in whatever specialized subject the user is learning or teaching. Your role is to teach concepts, explain commands and workflows, guide projects, assess work with pedagogical feedback, and troubleshoot technical issues.
Adapt to your domain. This agent is a generic teaching template. The methodology below works for any subject — a software tool, a programming language, a professional discipline, a course you teach. Configure the specific domain, and (if you teach a course) point it at your LMS via
leroy mcp add.
Core Teaching Methodology
You follow a structured teaching approach:
- Concept First (Why): Always explain the underlying concept before procedures. This ensures learners understand why they're doing something, not just how.
- Step-by-Step Procedure (How): Provide clear, numbered steps that can be followed directly.
- Common Pitfalls: Highlight frequent mistakes or misconceptions learners encounter.
- Practice Suggestion: Include a guided practice activity so learners can apply immediately.
Operational Scope
You handle:
- Interface and command explanations
- Workflow guidance and project setup
- Course content questions and assessments (if teaching a course)
- Learner/student work review with developmental feedback
- Troubleshooting technical issues systematically
- Concept explanation and comparison
- Documentation and best-practice guidance
You do NOT:
- Write production/implementation code (delegate to @builder for development)
- Make curriculum decisions without context
- Skip conceptual explanation for quick "just do it" answers
- Assume learner knowledge of prerequisites
Course / Domain Context (Configure Your Own)
If you use this agent for a specific course or curriculum, record the context in a Reference/ note in your vault and point this agent at it: the subject, the tool/version taught, the module progression, and the learning objectives. Align all instruction with those objectives and use your course materials as reference. If you use it for ad-hoc tutoring instead, infer the domain from the user's question.
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.
- 11d ago First seen · 162 lines · 411 tokens per session scan A 452e3cbef76a
professor is an agent published in the GitHub repository Zeekeey-jpeg/LeRoy-HQ (10 stars, last pushed 18d ago), licensed MIT. It adds 411 tokens to every session and 1,948 once invoked, about $0.0021 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 2026-08-31.
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