菜单
Structured AI engineering curriculum — 382 skills + 99 prompts across 20 phases covering ML, deep learning, LLMs, agents, and production systems. Use when learning AI, building AI skills,.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Enforce staged execution discipline on large tasks: written stage plan, parallel sub-agent delegation, failable verification at each stage, and skeptical self-review before delivery. Use when tasks span multiple files, multiple sources, or multiple sessions. Also triggers on "do this thoroughly", "be systematic", "deep work mode", "be thorough".
Write production-quality code from specs — reads requirements, researches patterns, implements with tests, and iterates until verification passes. Use when implementing features, fixing bugs with known root causes, or building new modules.
Ship code to production through a controlled pipeline with verification gates and rollback plans. Use when deploying features, managing CI/CD, running database migrations, or performing post-incident hotfix recovery.
Investigate topics deeply with cross-referenced sources and produce evidence-backed findings. Use when evaluating technologies before adoption, analyzing competitors, or investigating bug root causes across docs and issues.
Read code changes with adversarial intent to find bugs, security holes, logic errors, and performance traps. Use when reviewing PRs, auditing refactoring for regressions, or running pre-deploy safety checks.
Detect and fix code style violations, enforce project conventions, and ensure consistent formatting across the codebase. Use when cleaning lint errors before PRs, migrating linters, or bulk-applying new rules.
| name | ai-engineering-curriculum |
| description | Structured AI engineering curriculum — 382 skills + 99 prompts across 20 phases covering ML, deep learning, LLMs, agents, and production systems. Use when learning AI, building AI skills,. |
AI Engineering from Scratch: a complete 473-lesson curriculum spanning 20 phases from math foundations to autonomous agent systems. Covers Python, TypeScript, Rust, and Julia. Built for agents and humans who need a structured path through modern AI engineering.
Source: rohitg00/ai-engineering-from-scratch
Install: npx skills add rohitg00/ai-engineering-from-scratch
Trigger phrases:
"ai engineering curriculum"
"User wants to learn AI engineering from scratch"
"Need to assess someone's AI skill level"
"Looking for structured learning path in ML/DL/LLMs/Agents"
User wants to learn AI engineering from scratch
Need to assess someone's AI skill level
Looking for structured learning path in ML/DL/LLMs/Agents
Building AI training programs or onboarding materials
Searching for specific AI topic coverage
| Phase | Topic | Focus |
|---|---|---|
| 0 | Setup | Environment, tools, Python/TS/Rust/Julia config |
| 1 | Math | Linear algebra, calculus, probability, statistics |
| 2 | ML Fundamentals | Supervised/unsupervised, evaluation, pipelines |
| 3 | Deep Learning | Neural networks, backprop, CNNs, RNNs |
| 4 | Computer Vision | Image classification, detection, segmentation |
| 5 | NLP | Text processing, embeddings, sequence models |
| 6 | Speech/Audio | ASR, TTS, audio processing |
| 7 | Transformers | Attention, encoder-decoder, positional encoding |
| 8 | Generative AI | GANs, VAEs, diffusion models |
| 9 | Reinforcement Learning | Policy gradient, Q-learning, PPO |
| 10 | LLMs from Scratch | Tokenization, training, scaling laws |
| 11 | LLM Engineering | Fine-tuning, RAG, prompt engineering, evals |
| 12 | Multimodal AI | Vision-language models, cross-modal reasoning |
| 13 | Tools/Protocols | MCP, function calling, tool use patterns |
| 14 | Agent Engineering | ReAct, planning, memory, tool orchestration |
| 15 | Autonomous Systems | Self-improving agents, reflection, verification |
| 16 | Multi-Agent/Swarms | Agent coordination, delegation, consensus |
| 17 | Infrastructure/Production | Serving, monitoring, scaling, cost optimization |
| 18 | Ethics/Safety/Alignment | RLHF, red teaming, guardrails, interpretability |
| 19 | Capstone | End-to-end project combining all phases |
Placement quiz that assesses current knowledge across phases and recommends a starting point.
Per-phase quiz that tests comprehension after completing each phase.
User: "Where should I start learning AI?"
Agent: Runs /find-your-level placement quiz, maps results to phase map, recommends starting phase
User: "I know Python and basic ML, what next?"
Agent: Maps to Phase 3-4, recommends deep learning and computer vision modules
User: "Quiz me on transformers"
Agent: Runs /check-understanding for Phase 7
After completing this skill, confirm:
| Rationalization | Reality |
|---|---|
| "I will add monitoring later" | Without monitoring, you cannot detect failures. Add it from day one. |
| "One model is enough" | Different tasks need different models. Route intelligently. |
| "Premature optimization" | Infrastructure decisions are hard to change later. Design for scale early. |