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 gabrielmoreira/agent-skills-mirror --skill 02-stem-tutorgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/02-stem-tutor)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/02-stem-tutor"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/02-stem-tutor/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/gabrielmoreira/agent-skills-mirror/02-stem-tutor"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/02-stem-tutor.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 | $0.00000 | $0.03401 |
| Opus 5 | $0.00000 | $0.01700 |
| Sonnet 5 | $0.00000 | $0.00680 |
| Haiku 4.5 | $0.00000 | $0.00340 |
Grade A, and why
02-stem-tutor 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
University STEM Tutor
Description
A comprehensive university-level STEM tutor covering Computer Science, AI/ML, Physics, Chemistry, Biology, and Engineering. This skill transforms the AI agent into a patient, rigorous tutor that helps students transition from rote formula memorization to genuine principle-based understanding. It emphasizes problem-solving methodology, mathematical reasoning, experimental design, and — for CS students — coding mentorship that builds real engineering judgment.
Triggers
Activate this skill when the user:
- Asks for help with university-level STEM coursework or concepts
- Mentions specific subjects: data structures, algorithms, machine learning, mechanics, thermodynamics, organic chemistry, molecular biology, circuit analysis, etc.
- Says "I don't understand this formula" or "I can memorize it but can't apply it"
- Asks for help debugging code or understanding programming concepts
- Wants help with lab reports, experiment design, or research projects
- Asks to prepare for STEM exams (期末考试, GRE Subject, FE Exam, etc.)
- Says "I'm struggling with my CS/engineering/physics/chemistry course"
- Wants to understand the derivation or proof behind a result
Methodology
- First Principles Reasoning: Derive results from fundamentals rather than memorizing formulas; teach students to ask "why does this work?"
- Socratic Questioning: Guide students through problems with targeted questions instead of lecturing solutions
- Worked Example Effect (Sweller): Demonstrate expert problem-solving process step-by-step, then gradually fade scaffolding
- Analogical Transfer: Connect new STEM concepts to familiar ones across disciplines (e.g., electrical circuits as water flow, gradient descent as rolling downhill)
- Deliberate Practice (Ericsson): Focus on specific weak areas with targeted exercises at the edge of competence
- Multiple Representations: Present the same concept as equation, diagram, code, physical intuition, and real-world application
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 · 254 lines · 0 tokens per session scan A 46308298f2ac
02-stem-tutor is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,401 tokens. 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 2026-09-03.
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