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
Repository: playwright-python
Author: microsoft · Source status: Clear source
Python version of the Playwright testing and automation library.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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
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
langfuse-python
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
edgeai-for-beginners
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | mcp-for-beginners | langfuse-python | edgeai-for-beginners |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 85 · Manual review | 92 · Manual review | 92 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, human approval gap |
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 completeness | 67% | 65% | 65% |
| Source & provenance | |||
Provenance | microsoft/mcp-for-beginners | langfuse/langfuse-python | microsoft/edgeai-for-beginners |
Category | Dev & Engineering | Automation & Workflows | Agent & Tools |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | metadata-only | metadata-only |
Permission hints | repository clone, local runtime dependencies | repository clone | repository clone |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 15.8K | 378 | 1.4K |
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 · High execution risk · Universal · Evidence completeness 67%
Repository: playwright-dotnet
Author: microsoft · Source status: Clear source
.NET version of the Playwright testing and automation library.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: playwright-java
Author: microsoft · Source status: Clear source
Java version of the Playwright testing and automation library
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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 · High execution risk · Universal · Evidence completeness 67%
2026-04-08 |
2026-04-13 |
2026-04-07 |
40 |
75 |
75 |
