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 agentmods add skills/kirilxd/claude-tutor/learnnpx skills add kirilxd/claude-tutor --skill learngit 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/skills/kirilxd/claude-tutor/learn)<a href="https://agentmods.dev/skills/kirilxd/claude-tutor/learn"><img src="https://agentmods.dev/badge/skills/kirilxd/claude-tutor/learn.svg" alt="Measured on agentmods" 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 | $0.00128 | $0.03520 |
| Opus 5 | $0.00064 | $0.01760 |
| Sonnet 5 | $0.00026 | $0.00704 |
| Haiku 4.5 | $0.00013 | $0.00352 |
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 5d 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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn — Topic Research & Learning Plan Generator
Overview
Guide the user from a vague "I want to learn X" into a structured, researched learning plan with curated resources. Ask clarifying questions, research the topic, and produce a module-by-module plan saved to disk.
File Storage Rules — EXACT PATHS (no deviation)
The learning system uses three separate directories. Each directory has ONE purpose:
| Directory | Stores | Allowed fields |
|---|---|---|
~/.claude/learning/plans/ |
Learning plans ONLY | topic, slug, created, level, goal, depth, timeCommitment, modules, totalEstimatedTime, diagnostic |
~/.claude/learning/progress/ |
Quiz progress ONLY | topic, quizzes, weakAreas, strongAreas, overallScore, spacedRepetition |
~/.claude/learning/ (root) |
index.json + profile.json ONLY | topics (index), learningStyle/background/createdTopics (profile) |
CORRECT — saving a learning plan: ~/.claude/learning/plans/dns-2026-03-29.json
WRONG — saving a learning plan: ~/.claude/learning/progress/dns.json
CORRECT — saving quiz progress: ~/.claude/learning/progress/dns.json
WRONG — saving quiz progress: ~/.claude/learning/plans/dns-2026-03-29.json
CORRECT — saving any learning data: ~/.claude/learning/plans/dns-2026-03-29.json
WRONG — saving to project directory: ./learning/plans/dns-2026-03-29.json
Always use the ABSOLUTE path ~/.claude/learning/ — never a relative path like ./learning/.
Never add quiz fields (quizzes, weakAreas, strongAreas, spacedRepetition, overallScore) to plan files.
Never add plan fields (modules, resources, goal, depth, timeCommitment) to progress files.
Verify the path is correct BEFORE writing.
Process
Check for Existing Topic
Before starting a new plan, check if this topic already exists.
Read ~/.claude/learning/index.json and look for a matching topic (fuzzy match same as quiz skill: "k8s" → "kubernetes").
If the topic exists:
- Read the plan file and progress file
- Show a brief status:
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.
- 5d ago First seen · 336 lines · 128 tokens per session scan A 40e5ae01259b
learn is a skill published in the GitHub repository kirilxd/claude-tutor (122 stars, last pushed 1mo ago), licensed MIT. It adds 128 tokens to every session and 3,520 once invoked, about $0.0006 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.
Other skills, from other repositories
tutor
Use when the user wants to learn, study, or understand a topic — triggered by requests to teach, tutor, explain concepts, quiz, drill, or build understanding of any subject.
reading-metaskill
当用户想养成阅读习惯、问「读什么书/怎么读」「如何学习新领域/怎么入门某学科」时调用。 核心理念: 阅读是终极元技能; 读你所爱直到爱上阅读, 没有读完义务; 读原著与经典优先; 以教促学; 每天1-2小时即可进入极少数人行列。 不适用于: 具体某本书的书评、考试备考资料选择。 Triggers: 阅读/读书/怎么学习/入门/原著/书单/reading/how to learn.
coach
Learning telemetry, strategy, and schedule — retention stats, calibration, grader audit, n-of-1 experiments, HTML dashboard. Use for "how am I doing", weekly check-ins, strategy questions, auditing the grader, or adjusting how Engram teaches.
textbook-distillation
Turn a textbook or long-form source into a self-paced learning track: intake the material, build a chapter map, draft a lesson plan, then generate self-contained HTML lecture notes in a style the human specifies (layout, palette, emphasis), each lesson carrying worked examples, exercises, and checkpoint questions.…
classify-interview-questions
将批量面经或零散面试题逐题去重并分发：Agent/LLM/AI工程题写入 zero2Agent 的 learn-agent-interview，传统后端八股写入相邻 zero2Leetcode 的夏季八股。大批量输入使用 gpt-5.6-luna API 逐篇并发抽题和语义召回，再审查、去重和写答案；不新建面经实录文章。.
learning-and-development
Builds capability — skills gaps, career frameworks, training that transfers to the job, and internal mobility. Use this to design a career ladder, close a capability gap, decide whether to build or hire a skill, structure onboarding into a role, or work out why training keeps failing to change anything.