ai-engineer-notebooks: Hands-on, framework-free Colab notebooks for the AI Engineer / Forward Deployed Engineer skill set

by calmrocks · MCP Server · ★ 689

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 58/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentsai-engineerai-engineeringcolabevalsfine-tuningforward-deployed-engineergenerative-aigroqjupyter-notebook

Quick Facts

Stars689
Forks63
LanguageJupyter Notebook
CategoryMCP Server
LicenseMIT
Quality Score58/100
Open Issues1
Last Updated2026-10-08
Created2026-08-11
Platformsmcp
Est. Tokens~20k

About ai-engineer-notebooks

AI Engineer Notebooks Learn the applied-LLM stack the way you'll actually be interviewed on it: framework-free, on a free API, from prompting all the way to serving, fine-tuning, and a red-team benchmark. Runnable Colab notebooks for the AI Engineer / Forward Deployed Engineer (FDE) skill set.

Compatible Skills

These tools work well together with ai-engineer-notebooks for enhanced workflows:

  • AI-ML — semantic(0.29)+complementary+same_lang+similar_pop (55%)
  • LLM-Finetuning-Toolkit — semantic(0.28)+complementary+rare_topics+similar_pop (54%)
  • unsloth-buddy — semantic(0.27)+complementary+rare_topics+similar_pop (53%)
  • chatlab — semantic(0.19)+complementary+same_lang+similar_pop (52%)
  • MetaClaw — semantic(0.20)+complementary+rare_topics+similar_pop (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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