AI Engineering from Scratch is a free, open-source curriculum that teaches people to build AI systems through lessons and reusable artifacts such as prompts, skills, agents, and MCP servers. It is for learners who want practical foundations or want to create AI applications, and the catalogue skills support parts of that curriculum.
Borrowing it
Nothing to install: this file belongs to rohitg00/ai-engineering-from-scratch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/AGENTS.mdgit clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratchWrote 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/instructions/rohitg00/ai-engineering-from-scratch/agents-md)<a href="https://agentmods.dev/instructions/rohitg00/ai-engineering-from-scratch/agents-md"><img src="https://agentmods.dev/badge/instructions/rohitg00/ai-engineering-from-scratch/agents-md/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/instructions/rohitg00/ai-engineering-from-scratch/agents-md"><img src="https://agentmods.dev/badge/instructions/rohitg00/ai-engineering-from-scratch/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.04136 | $0.04136 |
| Opus 5.5 | $0.01654 | $0.01654 |
| Sonnet 5.5 | $0.00827 | $0.00827 |
| Haiku 4.5 | $0.00414 | $0.00414 |
Grade A, and why
ai-engineering-from-scratch AGENTS.md 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 2d 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Operating manual for contributors and AI agents touching this repo. Read it before opening a PR.
The repo is a curriculum, not a SaaS app. The lessons are the product. Every rule below keeps 542 lessons coherent over time.
Philosophy
542 lessons. 20 phases. Every algorithm built from raw math before a single framework gets imported. You write backprop, the tokenizer, the attention mechanism, and the agent loop by hand in Python, TypeScript, Rust, or Julia. Then you run the same operation through the production library so the framework stops being a black box. The "Build It / Use It" split is the spine. Each lesson ships a reusable artifact you can plug into your daily workflow.
Repo layout
phases/
NN-phase-slug/
NN-lesson-slug/
docs/en.md # lesson explainer
code/ # implementation + tests
quiz.json # 6 questions
outputs/ # reusable artifact (skill / prompt / agent / MCP server)
README.md # public face; lesson counts auto-synced
ROADMAP.md # phase/lesson status
glossary/terms.md # canonical term definitions
site/
build.js # parses README + ROADMAP + glossary -> data.js
data.js # generated; rebuilt by CI on main push
certifications/claude/
program.json # program metadata, source policy, official links
tracks/*.json # exam blueprint, ordered route, study plans
lessons/NN-slug/ # shared certification lesson contract
assessments/<exam-code>/ # original diagnostics and full mocks
manuals/
AUTHORING.md # manual contract: shape, figures, capture kit, prose limits
<id>/manual.json # pinned subject, palette, parts and sections
<id>/sections/*.md # one file per section
<id>/figures/*.svg # static figures; figure kit sources in figures/src/
<id>/capture/ # offline capture kit and the records it wrote
scripts/ # automation
.github/workflows/
curriculum.yml # invariant + auto-sync workflow
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.
- 2d ago Changed · +15 lines · +376 tokens per session f30a4f59dba4
- 14d ago Changed · +3 lines · +129 tokens per session fcebb777e98f
- 23d ago Changed 5c5d3980ffff
- 25d ago Changed 49f024b89260
- 27d ago Changed · +8 lines · +235 tokens per session 27fabcd65289
- 1mo ago Changed · +11 lines · +232 tokens per session 59d6183d5edc
- 1mo ago Changed · +2 lines · +63 tokens per session 24a1cb3e1111
- 1mo ago Changed 9aabea3913cb
- 2mo ago First seen · 310 lines · 3,101 tokens per session scan A d4bc4cccea16
ai-engineering-from-scratch AGENTS.md is an instructions file published in the GitHub repository rohitg00/ai-engineering-from-scratch (77,007 stars, last pushed yesterday), licensed MIT. It adds 4,136 tokens to every session, about $0.0165 per session on Opus 5.5. 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 2026-08-30.
ai-engineering-from-scratch AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ai-engineering-from-scratch AGENTS.mdrohitg00/ai-engineering-from-scratch | Instructions file | 77k | yesterday | MIT | A | 4,136 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |