gpt4all
Repository: gpt4all
Author: nomic-ai · Source status: Clear source
GPT4All: Run Local LLMs on Any Device.
Score basis:Clear source · High execution risk · Universal · Evidence completeness 65%
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.
Score-basis 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
Pre-install score, evidence completeness, 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
langextract
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
oss-llmops-stack
Execution risk:High
Threat tags:prompt injection, tool poisoning, unexpected code execution
Control gaps:missing license, broad permissions, shell without guardrails
devspace
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | langextract | oss-llmops-stack | devspace |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 92 · Manual review | 83 · Manual review | 79 · Manual review |
Threat tags | unexpected code execution, data exfiltration, human approval gap | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, data exfiltration, human approval gap |
Evidence completeness | 65% | 67% | 65% |
| Source & provenance | |||
Provenance | google/langextract | langfuse/oss-llmops-stack | devspace-sh/devspace |
Category | Automation & Workflows | Automation & Workflows | Operations & Infra |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | metadata-only | No explicit signals |
Permission hints | repository clone | repository clone | repository clone, local runtime dependencies |
| Install & compatibility | |||
Install friction | 75 | 75 | 40 |
| Community | |||
Stars | 35.6K | 136 | 5K |
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 · High execution risk · Universal · Evidence completeness 67%
Repository: playwright
Author: microsoft · Source status: Clear source
Playwright is a framework for Web Testing and Automation.
Score basis:Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: home-assistant/core
Author: home-assistant · Source status: Clear source
:house_with_garden: Open source home automation that puts local control and privacy first.
Score basis:Clear source · High execution risk · Universal · Evidence completeness 65%
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 · High execution risk · Universal · Evidence completeness 65%
Repository: vllm
Author: vllm-project · Source status: Clear source
A high-throughput and memory-efficient inference and serving engine for LLMs
Score basis:Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-14 |
2025-02-16 |
2026-04-04 |