learn

learn is a skill for Claude Code, Codex from fthsrbst/mnema. It costs 77 tokens per session (551 once invoked), scanned A, original, MIT.

A learning assistant skill for explaining software and technology topics in layers, from a simple mental model to working code and common mistakes. It can also create searchable study notes and short quizzes.

In plain words
What is it for?
Use it to learn a new programming concept, practise with a small exercise, save a concise learning note, or revisit a topic with spaced review and a quiz.
Why use it?
It turns one-off explanations into material that can be reviewed later and checks whether the learner can apply the idea.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to learn a new programming concept, practise with a small exercise, save a concise learning note, or revisit a topic with spaced review and a quiz.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fthsrbst/mnema/learn
Install

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.

Any agent
npx skills add fthsrbst/mnema --skill learn
Clone the repo
git clone --depth 1 https://github.com/fthsrbst/mnema

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for learn

README.md
[![agentmods](https://agentmods.dev/badge/skills/fthsrbst/mnema/learn/github.svg)](https://agentmods.dev/skills/fthsrbst/mnema/learn)
Your own site
<a href="https://agentmods.dev/skills/fthsrbst/mnema/learn"><img src="https://agentmods.dev/badge/skills/fthsrbst/mnema/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.

agentmods 80×15 button for learn

Your own site · 80×15
<a href="https://agentmods.dev/skills/fthsrbst/mnema/learn"><img src="https://agentmods.dev/badge/skills/fthsrbst/mnema/learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 551 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.00551
Opus 5.5 $0.00031 $0.00220
Sonnet 5.5 $0.00015 $0.00110
Haiku 4.5 $0.00008 $0.00055

Measured 1mo ago against content hash 7e9d2a331bf7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/learn/SKILL.md · 34 lines

What it actually says

Learn — Öğrenme Akışı

Amaç: öğrenilen her konu aranabilir, tekrar edilebilir kalıcı bilgiye dönüşsün.

Akış

  1. Seviye tespiti: memory_search("<konu>") + rag_search("<konu>") — daha önce bu konuda not var mı? Varsa üstüne inşa et, sıfırdan anlatma.
  2. Katmanlı anlatım:
    • Önce zihinsel model (bu ne, hangi sorunu çözüyor, neye benziyor)
    • Sonra çalışan minimal kod örneği (kullanıcının bildiği stack'te)
    • Sonra "gerçek hayatta nerede kırılır" (yaygın hatalar, edge case'ler)
  3. Uygulatma: küçük bir egzersiz öner; kullanıcı kodu yazsın, sen review et. İzlemek değil yapmak öğretir.
  4. Not üret ve indeksle: konu kapanırken kompakt bir öğrenme notu yaz ve rag_add ile kaydet:
    title: "Öğrenme notu: <konu>"
    uri: "learn/<konu-slug>"        ← tekrar öğrenmede re-index olur
    project: "learning"
    text:
    # <Konu>
    ## Zihinsel model  (2-3 cümle)
    ## Temel API/kavramlar  (madde madde)
    ## Kod örneği  (çalışan, minimal)
    ## Tuzaklar  (yaşanan hatalar dahil)
    ## Açık sorular  (henüz anlaşılmayanlar)
    
  5. Mini quiz: 3-4 soru sor (kod okuma + "bu neden patlar?" tarzı). Yanlışları nota işle.

Tekrar (spaced repetition)

Kullanıcı "tekrar edelim" derse: rag_search(project="learning") ile eski notları çek, "Açık sorular" bölümlerinden ve tuzaklardan quiz üret. Doğru cevaplananları nottan düşür, notu rag_add ile (aynı uri) güncelle.

Changes

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.

  1. 1mo ago First seen · 34 lines · 77 tokens per session scan A 7e9d2a331bf7

Subscribe to this mod's changes

learn is a skill published in the GitHub repository fthsrbst/mnema (2 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 551 once invoked, about $0.0003 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 2026-08-31.