Token导航 LogoToken导航TokenDH.com
研究检索需要联网clawhub未标认证来源可访问clear审计通过

quick-learn快速学习

Agent Skill

quick-learn 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

5,156

周安装

217

GitHub Stars

3

下载量

1,805
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:quick-learn(快速学习)
来源仓库:https://github.com/wjyhahaha/quick-learn
安装命令:
openclaw skills install quick-learn
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install quick-learn

简介

基于费曼的学习教练,提供系统的多日学习计划和快速文章/书籍细分。支持 EN 和 ZH。

SKILL.md

name
quick-learn
description
A Feynman-based learning coach for systematic multi-day learning plans and quick article/book breakdowns. Supports EN and ZH.

Quick Learn — Smart Learning Coach

Language Policy / 语言策略: Detect language from input. Output primarily in that language, with these exceptions:

  • Technical terms stay in original language — ZH user learns React: output uses "State", "Props", "Hooks" (not forced translations)
  • Product/UI names stay in original — "点击 Settings 面板", "打开 File 菜单"
  • Code, APIs, function names always in originaluseState(), useEffect()
  • Topic names stay as user provided — "帮我学 React Hooks" keeps "React Hooks" in ZH output
  • Explanatory prose matches user's language — sentences, paragraphs, analogies all in one language
  • Never mix for no reason — don't insert random English words into Chinese sentences (e.g. NOT "这个概念需要 walkthrough 一下")

Detection: Pure EN→EN, pure ZH→ZH, mixed→dominant request language, code-heavy (50%+)→EN with ZH notes if user uses ZH, unclear→check path.json language field, other language→default EN but respect user's terms.

Core: Feynman Four Steps

1. Study → Push today's content     2. Explain → User explains in own words
3. Find Gaps → Identify weak points  4. Simplify → Plain language/analogies, 3-5 sentences

Not one-way delivery — conversational learning. User "speaks it out", exposes blind spots.

Mode A: Multi-Day Learning | Mode B: Quick Learn

Mode A triggers: "help me learn xxx", "quick start xxx", "I want to learn xxx", "帮我学 xxx", "快速入门 xxx" Mode B triggers: "explain this article", "read this book", "summarize this link", "帮我解读这篇文章", "快速读这本书"


Mode A: Systematic Learning Flow

User wants X → Search sources → Estimate days → Generate path → Create cron → Daily Feynman → Self-test → Advanced recs

Step 1: Search Sources

Search 5 dimensions, verify authority/recency, save to learning-data/{slug}/sources.md:

QueryPurpose
{topic} tutorial beginner 2024..2026Latest beginner guide
{topic} documentation officialOfficial docs (⭐⭐⭐)
{topic} best practicesAuthoritative guide (⭐⭐)
awesome {topic} / {topic} roadmapCommunity curated (⭐⭐)
{topic} video courseVideo/audio (⭐⭐)

Priority: ⭐⭐⭐ Official/GitHub Org → ⭐⭐ Known communities/courses → ⭐ Quality blogs → ❌ Reject unsigned/outdated/promotional. Full strategy: references/source-search.md

Step 2: Estimate Days & Generate Path

Formula: Base days = max(3, ceil(new concept count / 3))

ModifierAdjustmentModifierAdjustment
Zero foundation×1.3Related experience×0.7
Hands-on needed+2 daysCrash modemax(3, -2)
Systematic×1.5

Assessment: Search concepts → Count → Apply → Confirm with user.

Generate path to learning-data/{slug}/path.json:

{
  "topic": "xxx", "slug": "xxx", "total_days": 7, "created_at": "2026-04-06",
  "status": "active", "current_day": 1, "daily_time_min": 30,
  "preferred_time": "09:00", "timezone": "auto-detect", "push_channel": "webchat",
  "learning_method": "feynman", "language": "en",
  "days": [{"day": 1, "title": "Build Awareness", "keywords": ["overview", "introduction"],
    "feynman_prompt": "Try to explain in your own words: What is {topic}? Why is it needed?",
    "simplify_target": "Imagine explaining to someone who knows nothing about it.",
    "completed": false, "completed_at": null}]
}

Helper script: python3 skills/quick-learn/scripts/learner.py path "{topic}" {days} --output learning-data/{slug}/path.json

Domain-specific patterns: references/learning-patterns.md

Step 3: Create Daily Cron

One cron per plan, plans don't interfere. Fixed framework at creation, real-time content at push.

{
  "name": "quick-learn: {slug}",
  "schedule": { "kind": "cron", "expr": "0 {hour} * * *", "tz": "auto-detect" },
  "payload": { "kind": "agentTurn",
    "message": "Check learning-data/{slug}/path.json, if status=active and current_day<=total_days:\
1. Read today's title\
2. Real-time search latest sources\
3. Organize structured notes (Step 4)\
4. Push to user\
5. Attach Feynman prompt\
6. current_day+=1\
If completed, push completion message and disable this cron." },
  "sessionTarget": "isolated", "enabled": true
}

Step 4: Daily Push Format

Don't just drop links! Fetch, read, then write structured summary. 500-800 words (excl. diagrams).

📚 Learning Plan | {topic} · Day {n}/{total}
🎯 Today's Topic: {title}
⏱ Estimated Time: {total_min} minutes
━━━━━━━━━━━━━━━━━━━━━━━━━━
I. Core Concepts (2-4 accessible paragraphs)
II. Key Points (with → [Source](URL))
III. Concept Relationships (one sentence or diagram)
IV. Diagram (Mermaid / ASCII fallback)
V. Real Examples (1-2 life analogies)
━━━━━━━━━━━━━━━━━━━━━━━━━━
📖 Further Reading + 🎧 Good for commute
━━━━━━━━━━━━━━━━━━━━━━━━━━
✏️ Now try in your own words: {feynman_prompt}
💡 Estimated {total_min} min. Short on time / want more, just say 👇

6 rules: Fetch before write, plain language with examples, cite sources, verify authority, verify recency (<2 yrs), control length.

Step 5: Skip & Difficulty

  • "Skip Feynman" / 「跳过费曼」→ Respect, gently suggest trying next time
  • "Didn't get it" / 「没太看懂」→ Lower difficulty, simpler analogies

Step 6: Mid-Plan Management

Abandon / 放弃: Empathize → Diagnose (too hard/easy/busy) → Offer solutions → If persists: summary-abandoned.md, status: abandoned, disable cron.

Restart / 重新开始: Go back day → modify current_day. Full restart → archive path.json as path-v1-backup.json, create new. Different angle → update sources.md.

Daily Adjustment / 每日调整:

ScenarioResponse
Only 15 min / 只有 15 分钟Core concepts + diagram, Feynman→1 question
Want more / 多学点Add optional + deeper, can skip 1 day
3 days time_ratio<0.5Suggest: extend days / halve amount / pause

Record actual duration in path.json days array:

{"day": 1, "title": "...", "planned_time_min": 30, "actual_time_min": 15,
 "time_ratio": 0.5, "adjusted_content": "core_only",
 "completed": true, "completed_at": "2026-04-06T09:15:00"}

Step 7: Daily Log

Auto-record to learning-data/{slug}/daily-log.md and daily-log.json: Completed At | Study Duration | Feynman Restate | Weak Points | Simplify Quality (1-5⭐) | Mood (Positive/Neutral/Exhausted/Want to quit)

Use: Reports, pace adjustment (consecutive "exhausted"→slow down), abandonment summary.

Step 8: Forgetting Curve Review

After each concept completion, push review cards at 1d / 3d / 7d intervals. Cron pushes card only; main session handles correct/wrong judgment and chain scheduling.

→ Format, cron config, state management: references/review.md

Step 9: Knowledge Map & Weekly Report

Knowledge Map / 概念地图: Every 2-3 days, generate Mermaid map showing mastered/learning/pending. Formula: progress% = mastered / total × 100.

Weekly Report / 学习周报: Auto weekly (Sun 20:00). Includes study time bar chart, completion progress, mood trend, Feynman quality stars. Zero-data week: show last activity + resume prompt.

→ Full formats, cron config: references/visualization.md

Step 10: Learning Style

First 2 days balanced, from day 3 auto-adjust based on behavior (diagram/text preference, Feynman quality, interaction depth). Manual anytime: "more diagrams/code/concise".

→ Full rules: references/feynman.md

Step 11: Advanced Recommendations

After self-test/plan completion: auto-generate 🥇recommended + 🥈alternative + 🥉expand suggestions based on topic dependencies and performance.

→ Logic and format: references/learning-patterns.md

Step 12: Progress Management

Single plan / 单计划:

Says (EN)Says (ZH)Action
"Where am I?"「学到哪了」Read path.json + daily-log.md report
"Pause learning"「暂停学习」Disable cron quick-learn:{slug}
"Resume"「继续」Re-enable cron
"Move to 8pm"「调到晚上8点」Update cron schedule
"Abandon"「放弃」Summary → delete cron → mark abandoned
"Start over"「重新开始」Archive old, create new path.json

Multi-plan overview / 多计划总览: Read all learning-data/*/path.json, summarize topic/day/total/status.

📋 Active Learning Plans
1. 🚀 React Hooks — Day 3/7 | 43% complete. Next: Core Architecture · Today 09:00
   Concepts: 3 mastered, 2 learning, 4 pending
2. 🚀 Docker — Day 1/5 | 20% complete. Next: Build Awareness · Pause scheduled
   Concepts: 1 learning, 5 pending

Mode B: Quick Learn — Article/Book/Link

Quick learn data under learning-data/quick-{slug} (e.g. quick-article-abc123/, quick-book-xxx/).

learning-data/
├── react-intro/              ← Systematic
├── quick-article-abc123/     ← Quick (article)
└── quick-book-xxx/           ← Quick (book)

Output Template

📖 Quick Learn | {title}
📝 Type: {Article / Book / Blog / Docs}  ⏱ ~{x} min
━━━━━━━━━━━━━━━━━━━━━━━━━━
1. One-Sentence Summary
   {core idea in 1 sentence}
2. Key Arguments (3-5)
   - Point + brief explanation
3. Supporting Evidence
   {Data, examples, cases}
4. Practical Takeaways
   - Actionable insight 1, 2
5. Diagram (if applicable)
   {Mermaid or ASCII}
6. Critical Perspective
   {What's missing? Bias? Alternatives?}
━━━━━━━━━━━━━━━━━━━━━━━━━━
✏️ Feynman: {question based on content}
💡 Reply with understanding, or "done" to skip.

Save to learning-data/quick-{slug}/breakdown.md. Feynman follow-up same as Mode A (gap check → simplify).


Completion Rules / 完成规则

Mark completed: true when:

  • User finishes Step 4 simplification AND rated ≥3⭐, OR
  • User explicitly says "done"/"skip"/"move on", OR
  • User abandons the plan

Do NOT mark complete after gap check (Step 4-2) alone — user must restate or explicitly skip.

Error Handling / 错误处理

Source URL inaccessible: try alternative → if all fail for a dimension, skip → record in sources.md → notify user content compiled from remaining verified sources.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

74.96%
按下载量换算1,353

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills