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
context7
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
Moses Stamp
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 | context7 | Moses Stamp | FastGPT |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, memory context poisoning |
| Source & provenance | |||
Provenance | upstash/context7 | openclaw/skills | labring/FastGPT |
Category | Automation & Workflows | Productivity & Docs | Automation & Workflows |
Freshness | 2026-04-14 | 2026-04-02 | 2026-04-14 |
| Risk & trust | |||
Trust score | 92 | 85 | 92 |
Audit signals | |||
| Install & compatibility | |||
Supported tools | Universal | OpenClaw | Universal |
Install friction | 75 | 50 | 75 |
| Community | |||
Stars | 52.6K | 0 | 27.7K |
Repository: llm-app
Author: pathwaycom · Source status: Clear source
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
Score basis:Clear source · Risk needs review · Universal
Repository: airflow
Author: apache · Source status: Clear source
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Score basis:Clear source · Risk needs review · Universal
network access, runs shell, writes files |
metadata-only |
Permission hints | repository clone | requires binary: python3, verify source provenance before install | repository clone |
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