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 event4u-app/agent-config --skill learning-tutorgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/learning-tutor)<a href="https://agentmods.dev/skills/event4u-app/agent-config/learning-tutor"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/learning-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/event4u-app/agent-config/learning-tutor"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/learning-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 | $0.00049 | $0.02355 |
| Opus 5.5 | $0.00020 | $0.00942 |
| Sonnet 5.5 | $0.00010 | $0.00471 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade A, and why
learning-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 1mo 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
learning-tutor
Structured tutoring modes for learning WITH the agent instead of consuming explanations FROM it. The default agent behavior on "teach me X" is a prose lecture — pedagogically the weakest format. This skill replaces the lecture with six evidence-shaped modes: triage what to learn, practice before theory, withhold answers until the learner has tried, verify understanding with probes the learner cannot bluff through.
When to use
Use this skill when the user:
- Wants to get functional in a new tool, framework, language or topic fast ("teach me X", "get me productive in X", "crash course").
- Wants to practice a concept rather than read about it ("drill me", "let me apply it", "give me exercises").
- Pastes content they don't understand and asks for a real explanation ("I don't get this", "explain this doc so it clicks").
- Has a concrete goal with a deadline and wants a learning plan ("learning plan for X", "I need to ship Y in two weeks and don't know Z").
- Wants their existing knowledge stress-tested ("quiz me", "do I actually understand X?", "find my blind spots").
- Just learned something and wants to verify it stuck ("check my understanding", "let me explain it back to you").
Do NOT use when:
- The user asks a direct factual or how-to question — answer it
(per
direct-answers); don't convert a question into a curriculum. - The user wants the agent to DO the task, not to learn it.
- The "learning" target is this package's own artifacts (rules/skills) —
that is
learning-to-rule-or-skill, a different skill. - The user wants a plan/idea stress-tested — that is
adversarial-reviewor/challenge-me, not knowledge tutoring.
Procedure
- Select the mode from the user's intent (table below). Pick ONE; if two fit, pick the one matching the user's verb ("learn" → teach modes 1–4, "check/quiz/verify" → probe modes 5–6) — don't ask.
- State the chosen mode in one line and freeze the target (skill/topic/goal).
- Run the mode's own procedure (sections below), holding its withhold rules — they are the skill.
- Carry the output header (see
## Output format) on every reply so a resumed session continues instead of restarting. - Close the session with the mode's closing analysis and exactly one next action for the learner.
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.
- 1mo ago First seen · 217 lines · 49 tokens per session scan A 3a3addaa2518
learning-tutor is a skill published in the GitHub repository event4u-app/agent-config (13 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 2,355 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-04.
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