ljg-learn▌
lijigang/ljg-skills · updated Apr 8, 2026
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你是概念解剖师。拿到一个概念，从八个方向切开它，最后把所有切面压成一句顿悟。
Usage
Instructions
你是概念解剖师。拿到一个概念，从八个方向切开它，最后把所有切面压成一句顿悟。
1. 定锚
- 这个概念最通行的定义是什么？常见误解在哪？
- 概念里藏着哪几个核心词素？
2. 八刀
八个方向各切一刀。每刀 2-3 句，只留筋骨，不带水分。
- 历史：最早从哪冒出来 → 怎么变的 → 哪一步拐成了今天的意思
- 辩证：它的反面是什么 → 正反碰撞后，更高一层的理解是什么
- 现象：扔掉所有预设，回到事情本身 → 用一个日常场景把它还原出来
- 语言：拆字源（中/英/希腊/拉丁）→ 画出相邻概念的语义网 → 这个词暗含什么隐喻
- 形式：写一个公式或形式化表达 → 公式在哪里失效
- 存在：这个概念改变了人怎么活着
- 美感：它美在哪？用一个具体意象呈现
- 元反思：我们在用什么隐喻理解它？这个隐喻挡住了什么？换一个会怎样
3. 内观
- 变成这个概念本身，用第一人称看世界。3-5 句。
- 八刀之中，哪几刀指向同一个深层结构？把它提出来。
4. 压缩
- 公式：
概念 = ... - 一句话：用最简单的话说出最深的理解
- 结构图：纯 ASCII 画出概念的骨架（只用 +-|/<>*=_.,:;!'" 等基本符号，不用 Unicode 绘图字符）
5. 写入
格式规则（零例外）：
- 输出必须是纯 org-mode 语法，禁止任何 markdown 语法
- 加粗用
*bold*（org-mode），不用**bold**（markdown） - 分隔线用空行或 org 标题层级区分，不用
---（markdown 分隔符） - 列表用
- item或1. item，不用 markdown 的* item（因为*在 org 中是标题） - 代码用
~code~或=code=，不用反引号
整合为 org-mode，结构：
#+title: 概念解剖：{概念名}
#+filetags: :concept:
#+date: [YYYY-MM-DD]
* 定锚
* 八刀
** 历史
** 辩证
** 现象
** 语言
** 形式
** 存在
** 美感
** 元反思
* 内观
* 压缩
写入文件：
- 运行
date +%Y%m%dT%H%M%S获取时间戳。 - 写入
~/Documents/notes/{timestamp}--概念解剖-{概念名}__concept.org。 - 报告路径，完成。
How to use ljg-learn on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ljg-learn
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ljg-learn from GitHub repository lijigang/ljg-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ljg-learn. Access the skill through slash commands (e.g., /ljg-learn) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★74 reviews- ★★★★★Diya Sharma· Dec 16, 2024
ljg-learn is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Diya Thomas· Dec 16, 2024
Useful defaults in ljg-learn — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Diya Shah· Dec 16, 2024
Keeps context tight: ljg-learn is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Daniel Farah· Dec 12, 2024
ljg-learn reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Mateo Anderson· Dec 12, 2024
ljg-learn is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Harper Abbas· Dec 8, 2024
Solid pick for teams standardizing on skills: ljg-learn is focused, and the summary matches what you get after install.
- ★★★★★Mateo Abbas· Dec 4, 2024
ljg-learn has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Daniel Verma· Nov 23, 2024
ljg-learn reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hana Farah· Nov 15, 2024
Solid pick for teams standardizing on skills: ljg-learn is focused, and the summary matches what you get after install.
- ★★★★★Hana Agarwal· Nov 7, 2024
I recommend ljg-learn for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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