Compare skills
Pick 2–4 skills and compare what really matters: fit, risk, install effort, and community signal.
Selected skills (3/4)
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: 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%
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
Multi-Agent-Custom-Automation-Engine-Solution-Accelerator
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Stream-Daemon
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Icebreaker Openers. 破冰。Rompehielos.
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | Multi-Agent-Custom-Automation-Engine-Solution-Accelerator | Stream-Daemon | Icebreaker Openers. 破冰。Rompehielos. |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 79 · Manual review | 81 · Manual review | 85 · Evidence missing |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, human approval gap | 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, Secrets, Command |
Evidence completeness | 65% | 65% | 69% |
| Source & provenance | |||
Provenance | microsoft/Multi-Agent-Custom-Automation-Engine-Solution-Accelerator | ChiefGyk3D/Stream-Daemon | openclaw/skills |
Category | Operations & Infra | Operations & Infra | Integrations & Connectors |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | No explicit signals | needs credentials, network access, runs shell, writes files |
Permission hints | repository clone, local runtime dependencies | repository clone, local runtime dependencies | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Universal | Claude, OpenClaw |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 771 | 31 | 0 |
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 · 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%
Repository: SmartKG
Author: microsoft · Source status: Clear source
This project accepts excel files as input which contains the description of a Knowledge Graph (Vertexes and Edges) and convert it into an in-memory Graph Store.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
Repository: Conversation-Knowledge-Mining-Solution-Accelerator
Author: microsoft · Source status: Clear source
This solution accelerator leverages Microsoft Foundry, Azure Content Understanding, Azure OpenAI Service, and Foundry IQ to enable organizations to derive insights from volumes of conversational data using generative AI.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: genaiops-promptflow-template
Author: microsoft · Source status: Clear source
GenAIOps with Prompt Flow is a "GenAIOps template and guidance" to help you build LLM-infused apps using Prompt Flow.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
2026-04-07 |
2026-04-06 |
2026-04-01 |
40 |
40 |
65 |
