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.
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 rohitg00/ai-engineering-from-scratch --skill skill-release-gategit 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/skills/rohitg00/ai-engineering-from-scratch/skill-release-gate)<a href="https://agentmods.dev/skills/rohitg00/ai-engineering-from-scratch/skill-release-gate"><img src="https://agentmods.dev/badge/skills/rohitg00/ai-engineering-from-scratch/skill-release-gate/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/rohitg00/ai-engineering-from-scratch/skill-release-gate"><img src="https://agentmods.dev/badge/skills/rohitg00/ai-engineering-from-scratch/skill-release-gate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.00846 |
| Opus 5 | $0.00018 | $0.00423 |
| Sonnet 5 | $0.00007 | $0.00169 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
skill-release-gate 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 9d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill release gate
Use this skill before publishing or distributing an Agent Skill directory bundle.
Workflow
- Resolve
SKILL_ROOTto the absolute directory containing this installedSKILL.md. Do not assume the process cwd is the installed bundle. - Resolve
TARGET_ROOTfrom the original workspace working directory and resolve the user-supplied candidate as an absoluteTARGET_BUNDLE. - Read
references/eval-contract.mdfromSKILL_ROOT. - Inspect the positive and near-miss trigger cases in
evals/cases.jsonunderTARGET_BUNDLE. - Inspect the shared baseline and with-skill assertions in
evals/artifacts.jsonunderTARGET_BUNDLE. - Inspect the explicit script and safety results in
evals/evidence.jsonunderTARGET_BUNDLE. - Inspect the declared runtime capabilities in
assets/hosts.jsonunderTARGET_BUNDLEand verify the target file hashes against itsassets/manifest.json. - For production, replace deterministic predictions, artifacts, evidence,
and host capabilities with captured results; set all four captured modes;
and bind every raw trigger observation, both artifacts, the complete
evidence set, and the non-empty host matrix to non-empty sources and
matching SHA-256 provenance digests. These local checks can set
localEvidenceReady, but locally recomputable hashes do not prove capture. - Obtain an external JSON attestation whose
evidenceRootmatches the report, plus the SHA-256 of its exact bytes from a separate trusted policy or release channel. The attestation must be a regular file outside the target bundle. - Before execution, show the exact resolved argv. The installed evaluator is
scripts/evaluate_skill.pyunderSKILL_ROOT. For the shipped lesson fixture, build argv frompython3, that absolute evaluator path,--fixture-demo, and the absoluteTARGET_BUNDLE. For production, use the same installed script with--attestation,--trusted-attestation-sha256, and the absoluteTARGET_BUNDLE, without--fixture-demo. - Return
checksPassed,fixturePassed,localEvidenceReady,trustAnchorValid,productionReady, andpassedwith the evidence root, evaluation modes, failed checks, precision, recall, every raw trigger observation, per-case repeated-run rates, artifact comparison, script and safety evidence, installed-tree verification, and portability matrix. Include the resolved script path, resolved target path, cwd, exact argv, and exit code. Mark unavailable observations unverified.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 63 lines · 36 tokens per session scan A b57d0f111224
skill-release-gate is a skill published in the GitHub repository rohitg00/ai-engineering-from-scratch (52,899 stars, last pushed yesterday), licensed MIT. It adds 36 tokens to every session and 846 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
workflow-patterns
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
python-sdk
Implement or modify Python SDK behavior under python/composio, including tools, toolkits, sessions, auth configs, connected accounts, client integration, and shared Python models. Use for Python core runtime/API work; pair with python-testing and cross-sdk-parity when TypeScript must match.
typescript-providers
Implement, modify, test, or document TypeScript provider packages under ts/packages/providers, including framework adapters for OpenAI, Anthropic, Google, LangChain, Mastra, Vercel, LlamaIndex, Cloudflare, and Claude Agent SDK. Use for provider-specific TS work; do not use for core-only changes.
typescript-testing
Select and run TypeScript SDK verification for packages, examples, type checks, linting, builds, Vitest suites, and runtime E2E tests. Use when adding tests, diagnosing TypeScript CI, choosing a focused test command, or validating TypeScript package changes. Do not use for Python-only checks.