minimind-learning

minimind-learning is a skill for Claude Code, Codex from joyehuang/minimind-notes. It costs 76 tokens per session (4,343 once invoked), scanned A, original, Apache-2.0.

A Chinese-language learning assistant for MiniMind, a small language-model project, that records study notes and recognizes common machine-learning terms.

In plain words
What is it for?
Use it while learning topics such as attention, positional encoding, normalization, Transformer models, fine-tuning, and reinforcement learning.
Why use it?
It helps organize ongoing study and preserve explanations, questions, and learning progress in repository notes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths.

Good fit Use it while learning topics such as attention, positional encoding, normalization, Transformer…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/joyehuang/minimind-notes/minimind-learning-skill
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 joyehuang/minimind-notes --skill minimind-learning-skill
Clone the repo
git clone --depth 1 https://github.com/joyehuang/minimind-notes

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 minimind-learning

README.md
[![agentmods](https://agentmods.dev/badge/skills/joyehuang/minimind-notes/minimind-learning-skill.svg)](https://agentmods.dev/skills/joyehuang/minimind-notes/minimind-learning-skill)
Your own site
<a href="https://agentmods.dev/skills/joyehuang/minimind-notes/minimind-learning-skill"><img src="https://agentmods.dev/badge/skills/joyehuang/minimind-notes/minimind-learning-skill.svg" alt="Measured on agentmods" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,343 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 $0.00076 $0.04343
Opus 5 $0.00038 $0.02171
Sonnet 5 $0.00015 $0.00869
Haiku 4.5 $0.00008 $0.00434

Measured 7d ago against content hash 8acecdd0bffb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

minimind-learning 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 7d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/init-for-learning.bat, scripts/init-for-learning.sh, scripts/validate_notes.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

minimind-learning-skill/SKILL.md · 587 lines

How it starts

The opening of the file, as written. The whole thing — 587 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MiniMind Learning Assistant

自动化学习笔记系统,静默记录你的 MiniMind 学习历程。

When to use

自动激活场景

  1. 学习开始时

    • 用户说:"开始今天的学习"、"开始学习"、"今天学什么"
    • 用户说:"继续学习"、"学习 MiniMind"
  2. 讨论 MiniMind 内容时

    • 提问包含:RMSNorm, LayerNorm, RoPE, YaRN, Attention, GQA, SwiGLU, Transformer, LoRA, DPO, PPO, GRPO, SFT, RLHF, RLAIF, MoE
    • 问题词:什么是、如何、为什么、怎样、解释、原理
    • 遇到问题:报错、错误、失败、Bug
  3. 显式记录请求

    • 用户说:"记录一下"、"保存笔记"、"写入笔记"

How to use

1. 初始化检查

首次激活时,确保笔记结构存在:

# 检测 Git 仓库根目录
git rev-parse --show-toplevel

# 验证是 MiniMind 仓库(存在以下文件)
# - model/model_minimind.py
# - trainer/train_pretrain.py
# - README.md (包含 "MiniMind")

# 创建笔记目录(如不存在)
mkdir -p docs/learning_materials

# 从模板初始化文件(如不存在)
# - docs/notes.md
# - docs/learning_log.md
# - docs/knowledge_base.md
# - docs/learning_materials/README.md

模板位置~/.claude/skills/minimind-learning/templates/

2. 学习引导模式

当用户说"开始学习"时,主动引导:

👋 欢迎开始今天的 MiniMind 学习!

你想学习哪个模块?

**基础组件**:
1. 归一化技术 - RMSNorm, LayerNorm
2. 位置编码 - RoPE, YaRN
3. 注意力机制 - Attention, GQA
4. 前馈网络 - FeedForward, SwiGLU

**训练技术**:
5. 预训练 - Pretraining
6. 监督微调 - SFT
7. 参数高效微调 - LoRA
8. 强化学习 - DPO, PPO, GRPO

直接告诉我编号或主题名称,我会为你讲解!

(学习过程中我会自动记录笔记到 `docs/` 目录)

3. 触发检测

每次用户消息后,检查是否满足以下任一条件:

Tier 1: 即时触发(立即更新笔记)
# MiniMind 术语(50+)
TERMS = [
    # 架构
    "RMSNorm", "LayerNorm", "BatchNorm", "GroupNorm",
    "RoPE", "YaRN", "ALiBi", "位置编码",
    "Attention", "注意力", "GQA", "MQA", "FlashAttention",
    "FeedForward", "前馈", "SwiGLU", "GELU", "GLU",
    "Transformer", "TransformerBlock",

    # 训练
    "pretrain", "预训练", "pretraining",
    "SFT", "监督微调", "fine-tuning", "微调",
    "LoRA", "lora", "LoRA-r", "LoRA-alpha",
    "DPO", "PPO", "GRPO", "SPO",
    "RLHF", "RLAIF", "强化学习",
    "distillation", "蒸馏", "知识蒸馏",

    # 模型
    "MiniMind", "MiniMind-Dense", "MiniMind-MoE",
    "MoE", "混合专家", "expert routing",
    "MiniMind-Reason", "R1",
]

# 问题词
QUESTION_WORDS = ["什么是", "如何", "为什么", "怎样", "解释", "原理", "作用"]

# 问题指示
PROBLEM_MARKERS = ["报错", "错误", "失败", "Bug", "不工作", "问题"]

# 检查
if any(term in user_message for term in TERMS):
    trigger_tier_1()
elif any(word in user_message for word in QUESTION_WORDS):
    trigger_tier_1()
elif any(marker in user_message for marker in PROBLEM_MARKERS):
    trigger_tier_1()

Read the full file on GitHub · 587 lines

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. 7d ago First seen · 587 lines · 76 tokens per session scan A 8acecdd0bffb

Subscribe to this mod's changes

minimind-learning is a skill published in the GitHub repository joyehuang/minimind-notes (184 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 76 tokens to every session and 4,343 once invoked, about $0.0004 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.