Github Mlabonne Llm Course
Repository: llm-course
Author: mlabonne · Source status: Clear source
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Score basis:Clear source · Risk needs review · Universal
Compare skills
Pick 2–4 skills and compare what really matters: fit, risk, install effort, and community signal.
Comparison matrix
Highlights show current best; tooltip explains diff/best rules.
SAS-v2.1 diff rules / risk tag notes
Start with the matrix. Open this section when you need to understand audit grades, top threats, control gaps, and best-value highlights.
Suggested baseline
Search to add skills, or paste 2–4 comma-separated slugs.
How differences are detected
A row is marked different when selected skills have distinct values. Only-differences mode hides rows that are identical.
How best values are highlighted
Audit score, evidence confidence, trust score, and community signal prefer higher values; execution risk and install friction prefer lower values.
How to read risk tags
Risk tags come from SAS-v2.1 public-evidence signals and point to command, network, secret, context, or supply-chain items to review before install.
Selected audit signals
claude-cookbooks
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
openai-cookbook
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
FastGPT
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | claude-cookbooks | openai-cookbook | FastGPT |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Audit grade | C · Review first | C · Review first | C · Review first |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, memory context poisoning |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Permission review, Network, Command |
Evidence confidence | 65% | 65% | 67% |
| Source & provenance | |||
Provenance | anthropics/claude-cookbooks | openai/openai-cookbook | labring/FastGPT |
Category | Automation & Workflows | Automation & Workflows | Automation & Workflows |
Freshness | |||
| Risk & trust | |||
Trust score | 92 | 92 | 92 |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 39.8K | 72.7K | 27.7K |
Repository: langextract
Author: google · Source status: Clear source
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
Score basis:Clear source · Risk needs review · Universal
Repository: caveman
Author: JuliusBrussee · Source status: Clear source
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
Score basis:Clear source · Risk needs review · Universal
2026-04-14 |
2026-04-14 |
2026-04-14 |
metadata-only |
metadata-only |
metadata-only |
Permission hints | repository clone | repository clone | repository clone |
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75 |
75 |
75 |