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/kirilxd/claude-tutorWrote 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/commands/kirilxd/claude-tutor/learn)<a href="https://agentmods.dev/commands/kirilxd/claude-tutor/learn"><img src="https://agentmods.dev/badge/commands/kirilxd/claude-tutor/learn/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/commands/kirilxd/claude-tutor/learn"><img src="https://agentmods.dev/badge/commands/kirilxd/claude-tutor/learn.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 | $0.00020 | $0.00201 |
| Opus 5.5 | $0.00008 | $0.00080 |
| Sonnet 5.5 | $0.00004 | $0.00040 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
learn 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.
What it actually says
/learn
The user wants to learn about: $ARGUMENTS
Follow the learn skill instructions to:
- Check if a plan already exists for this topic (resume if so)
- Load or create learner profile
- Ask clarifying questions about scope, level, and goals (one at a time, 2-5 max)
- If not a beginner, run a diagnostic assessment
- Research the topic with 3-5 web searches
- Generate a structured learning plan with modules and curated resources
- Present the plan for user approval
- Save to ~/.claude/learning/plans/ and update index.json
- Offer to start teaching Module 1
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 · 21 lines · 20 tokens per session scan A f876c836846b
learn is a command published in the GitHub repository kirilxd/claude-tutor (142 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 201 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
learn
Initialize a new learning topic $topic or continue learning an existing one using the FASTER framework.
progress
Show detailed progress report for current learning topic.
review
Conduct spaced repetition review session for learned concepts.
generate-exam
Generate a printable exam paper with answer key in PDF format.
explain
Explain complex concepts in simple terms with examples and analogies.
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.