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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,.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/oyi77/1ai-skills --skill ai-engineering-curriculumコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Android and mobile application security testing — emulators, rooting, traffic interception, dynamic instrumentation. Use when testing mobile apps for vulnerabilities, reversing APKs, or bypassing security controls on Android.
Self-reflection + Self-criticism + Auto-learning from corrections + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when working with self improving.
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities. Use when working with conducting full scope red team engagement.
| 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,. |
| domain | core |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | core-platform |
| tags | ["ai","curriculum","machine-learning","deep-learning","llm","agents","education"] |
| version | 1.0.0 |
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. |