learn
Interactive lesson tutor for the AI Engineering from Scratch curriculum. Reads LEARNING.md, fetches the next lesson, teaches it section by section in the terminal, quizzes at the end, and records progress. Works cloned or entirely over raw.githubusercontent.com — no setup required. Trigger phrases: "next lesson", "teach me", "continue the course", "let's learn", "resume learning"
- License 许可
- MIT
- 最近更新
- 2026-11-10
- Stars
- ★ 7.8w
- Forks
- 1.4w
01 前言 Front Matter
- name
- senior-fullstack
- description
- Comprehensive fullstack development skill for building complete web applications with React, Next.js, Node.js, GraphQL, and PostgreSQL. Includes project scaffolding, code quality analysis, architecture patterns, and complete tech stack guidance. Use when building new projects, analyzing code quality, implementing design patterns, or setting up development workflows.
02 资源文件 6
- 参考文档references/architecture_patterns.md1.6 KB
- 参考文档references/development_workflows.md1.6 KB
- 参考文档references/tech_stack_guide.md1.6 KB
- 脚本scripts/code_quality_analyzer.py3.1 KB
- 脚本scripts/fullstack_scaffolder.py3.1 KB
- 脚本scripts/project_scaffolder.py3.1 KB
03 SKILL.md 正文
Senior Fullstack
Complete toolkit for senior fullstack with modern tools and best practices.
Quick Start
Main Capabilities
This skill provides three core capabilities through automated scripts:
# Script 1: Fullstack Scaffolder
python scripts/fullstack_scaffolder.py [options]
# Script 2: Project Scaffolder
python scripts/project_scaffolder.py [options]
# Script 3: Code Quality Analyzer
python scripts/code_quality_analyzer.py [options]
Core Capabilities
1. Fullstack Scaffolder
Automated tool for fullstack scaffolder tasks.
Features:
- Automated scaffolding
- Best practices built-in
- Configurable templates
- Quality checks
Usage:
python scripts/fullstack_scaffolder.py <project-path> [options]
2. Project Scaffolder
Comprehensive analysis and optimization tool.
Features:
- Deep analysis
- Performance metrics
- Recommendations
- Automated fixes
Usage:
python scripts/project_scaffolder.py <target-path> [--verbose]
3. Code Quality Analyzer
Advanced tooling for specialized tasks.
Features:
- Expert-level automation
- Custom configurations
- Integration ready
- Production-grade output
Usage:
python scripts/code_quality_analyzer.py [arguments] [options]
Reference Documentation
Tech Stack Guide
Comprehensive guide available in references/tech_stack_guide.md:
- Detailed patterns and practices
- Code examples
- Best practices
- Anti-patterns to avoid
- Real-world scenarios
Architecture Patterns
Complete workflow documentation in references/architecture_patterns.md:
- Step-by-step processes
- Optimization strategies
- Tool integrations
- Performance tuning
- Troubleshooting guide
Development Workflows
Technical reference guide in references/development_workflows.md:
- Technology stack details
- Configuration examples
- Integration patterns
- Security considerations
- Scalability guidelines
Tech Stack
Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure
Development Workflow
1. Setup and Configuration
# Install dependencies
npm install
# or
pip install -r requirements.txt
# Configure environment
cp .env.example .env
2. Run Quality Checks
# Use the analyzer script
python scripts/project_scaffolder.py .
# Review recommendations
# Apply fixes
3. Implement Best Practices
Follow the patterns and practices documented in:
references/tech_stack_guide.mdreferences/architecture_patterns.mdreferences/development_workflows.md
Best Practices Summary
Code Quality
- Follow established patterns
- Write comprehensive tests
- Document decisions
- Review regularly
Performance
- Measure before optimizing
- Use appropriate caching
- Optimize critical paths
- Monitor in production
Security
- Validate all inputs
- Use parameterized queries
- Implement proper authentication
- Keep dependencies updated
Maintainability
- Write clear code
- Use consistent naming
- Add helpful comments
- Keep it simple
Common Commands
# Development
npm run dev
npm run build
npm run test
npm run lint
# Analysis
python scripts/project_scaffolder.py .
python scripts/code_quality_analyzer.py --analyze
# Deployment
docker build -t app:latest .
docker-compose up -d
kubectl apply -f k8s/
Troubleshooting
Common Issues
Check the comprehensive troubleshooting section in references/development_workflows.md.
Getting Help
- Review reference documentation
- Check script output messages
- Consult tech stack documentation
- Review error logs
Resources
- Pattern Reference:
references/tech_stack_guide.md - Workflow Guide:
references/architecture_patterns.md - Technical Guide:
references/development_workflows.md - Tool Scripts:
scripts/directory
04 同仓库更多 Skills
write-manual
Writes a long technical manual that explains one subject at one pinned version, from its purpose to each request and response. Every listing comes from an offline capture, every claim traces to a ranked source, and an audit must report TOTAL 0. Use it for "write a manual", "write a 101 manual for <subject>", "manual.json", "add a manual section", "build a capture kit", "audit the manual", or "pin a spec version and document it".
start-learning
One-time onboarding for the AI Engineering from Scratch curriculum (542 lessons, 20 phases). Interviews the learner, runs the placement quiz, and writes LEARNING.md, a persistent study plan the learn skill drives. Trigger phrases: "start learning", "set up the course", "begin the curriculum", "onboard me", "create my learning plan"
motion-figure
Builds figures that move or respond to the reader, in three tiers: SMIL animations and slider widgets inside lessons, interactive mechanism figures that show the named parts of a real system with a computed verdict, and ordered dither bands in the course palette. Triggers: "animated figure", "add a slider widget", "interactive figure", "show how this system works", "make this diagram move", "dither masthead", "mechanism figure", "figure fence".
mcpa-certification
AI-native tutor and onboarding workflow for the MCPA (Model Context Protocol Associate) certification in AI Engineering from Scratch. Use when a learner wants to prepare for the MCPA, resume their certification path, learn the next lesson interactively, run and verify practical labs, take the diagnostic or a full mock, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.
lesson-screen
Opens a lesson screen in the browser next to the terminal while a course tutor teaches. It shows the lesson's registered figures, code traces with real values, and clickable predictions. It also builds a figure for the lesson on the fly with the course figure skills and any figure skill the host has installed: a blueprint, a mechanism, an isometric machine, or a 3D model. Clicks return to the tutor as events. Trigger phrases: "open the lesson screen", "show me this visually", "show this in 3D", "draw the architecture", "visualize this lesson", "build a figure for this lesson", "lesson screen".
learn-reverse-engineering
Focused interactive tutor for the Reverse Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to read a compiled program: file formats, disassembly, control flow, lifting, evidence, proof, calling conventions, compiler idioms, linking, types, debugging, bytecode, diffing, symbolic execution, and AI-assisted analysis. Teaches one lesson per invocation and records evidence in REVERSE-ENGINEERING-LEARNING.md.