Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oyi77/1ai-skills --skill ai-engineering-curriculumgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oyi77/1ai-skills/ai-engineering-curriculum)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/ai-engineering-curriculum"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/ai-engineering-curriculum/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/ai-engineering-curriculum"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/ai-engineering-curriculum.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 39 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.01190 |
| Opus 5 | $0.00025 | $0.00595 |
| Sonnet 5 | $0.00010 | $0.00238 |
| Haiku 4.5 | $0.00005 | $0.00119 |
Grade A, and why
ai-engineering-curriculum scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When NOT to Use
- Task is outside your authorization scope
- You need to implement controls (use implementing-* skills)
- Task is about analysis, not action (use analyzing-* skills)
- You don't have access to target systems
- Task requires compliance expertise (consult professionals)
- Task is about defense, not offense (use defensive skills)
Overview
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
Capabilities
- Navigate a structured 20-phase AI engineering curriculum
- Identify skill gaps by mapping current knowledge to phases
- Recommend targeted lessons based on learner level
- Run placement quizzes to find starting phase
- Run per-phase comprehension checks
When to Use
Trigger phrases:
-
"ai engineering curriculum"
-
"User wants to learn AI engineering from scratch"
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"Need to assess someone's AI skill level"
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"Looking for structured learning path in ML/DL/LLMs/Agents"
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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
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Searching for specific AI topic coverage
Phase Map
| 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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 143 lines · 50 tokens per session scan A adcb5fa948f9
ai-engineering-curriculum is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 1,190 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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Use when reasoning about Transformer self-attention, multi-head attention, positional encoding, masked decoder attention, or why attention replaced recurrence/convolutions in sequence models; not for generic NLP or unrelated attention topics.
ml-llm-wiki
Use when answering questions from this machine-learning knowledge base. Triggers: questions about transformers, attention cost and efficiency, and long-context scaling; 'what do we know about attention', 'check the ML wiki'. Read-only querying of compiled knowledge; to add, update, supersede, lint, audit, or critique…