BitNet
Repository: BitNet
Author: microsoft · Source status: Clear source
Score 92Clear source
Manual reviewOfficial inference framework for 1-bit LLMs
Score basis：Clear source · Risk needs review · Universal
Author unclaimed
Universal
38.2K
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
MInference
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
graphrag
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
gpt4all
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | MInference | graphrag | gpt4all |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap |
Evidence confidence | 67% | 67% | 65% |
| Source & provenance | |||
Provenance | microsoft/MInference | microsoft/graphrag | nomic-ai/gpt4all |
Freshness | 2026-04-08 | 2026-04-14 | 2025-05-28 |
| Community | |||
Stars | 1.2K | 32.2K | 77.3K |
Repository: semantic-kernel
Author: microsoft · Source status: Clear source
Integrate cutting-edge LLM technology quickly and easily into your apps
Score basis：Clear source · Risk needs review · Universal
Repository: ai-agents-for-beginners
Author: microsoft · Source status: Clear source
12 Lessons to Get Started Building AI Agents
Score basis：Clear source · Risk needs review · Universal