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
mcp-for-beginners
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
opencode-glm-quota
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
DemoGPT
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | mcp-for-beginners | opencode-glm-quota | DemoGPT |
|---|---|---|---|
| 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, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | prompt injection, tool poisoning, unexpected code execution |
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 | 67% | 65% | 67% |
| Source & provenance | |||
Provenance | microsoft/mcp-for-beginners | guyinwonder168/opencode-glm-quota | melih-unsal/DemoGPT |
Category | Dev & Engineering | Dev & Engineering | Dev & Engineering |
Freshness | |||
| Risk & trust | |||
Trust score | 85 | 82 | 82 |
Audit signals | No explicit signals | No explicit signals | No explicit signals |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 15.8K | 12 | 1.9K |
Repository: RD-Agent
Author: microsoft · Source status: Clear source
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models.
Score basis：Clear source · Risk needs review · Universal
Repository: playwright-dotnet
Author: microsoft · Source status: Clear source
.NET version of the Playwright testing and automation library.
Score basis：Clear source · Risk needs review · Universal
Repository: playwright-java
Author: microsoft · Source status: Clear source
Java version of the Playwright testing and automation library
Score basis：Clear source · Risk needs review · Universal
Repository: edgeai-for-beginners
Author: microsoft · Source status: Clear source
This course is designed to guide beginners through the exciting world of Edge AI, covering fundamental concepts, popular models, inference techniques, device-specific applications, model optimization, and the developmen…
Score basis：Clear source · Risk needs review · Universal
Repository: MInference
Author: microsoft · Source status: Clear source
[NeurIPS'24 Spotlight, ICLR'25, ICML'25] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while ma…
Score basis：Clear source · Risk needs review · Universal
2026-04-08 |
2026-04-02 |
2026-04-01 |
Permission hints |
|---|
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
40 |
40 |
40 |
