Create Adaptive Cards for MCP Plugins
Repository: awesome-copilot
Author: github · Source status: Clear source
Skill converted from mcp-create-adaptive-cards.prompt.md
Score basis:Clear source · Low risk signals · Claude
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
create-python-server
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
mcp-sequentialthinking-tools
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
UCAI
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | create-python-server | mcp-sequentialthinking-tools | UCAI |
|---|---|---|---|
| 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 | modelcontextprotocol/create-python-server | spences10/mcp-sequentialthinking-tools | nirholas/UCAI |
Freshness | 2025-01-23 | 2026-04-03 | 2026-03-30 |
| Risk & trust | |||
Trust score | 60 | 82 | 81 |
Audit signals | No explicit signals | No explicit signals | network access |
| Community | |||
Stars | 476 | 574 | 28 |
Repository: indonesia-gov-apis
Author: suryast · Source status: Clear source
🇮🇩 50+ Indonesian Government APIs & Data Sources — BPS, OJK, BPJPH, BPOM, Bank Indonesia, IDX, BMKG + MCP servers.
Score basis:Clear source · Low risk signals · Universal
Repository: UnrealGenAISupport
Author: prajwalshettydev · Source status: Clear source
Unreal Engine plugin for LLM/GenAI models & MCP UE5 server.
Score basis:Clear source · Low risk signals · Universal
Repository: precision-medicine-mcp
Author: lynnlangit · Source status: Clear source
Precision Medicine MCP Platform: A set of bioinformatics servers + tools - production multiomics/genomics + spatial transcriptomics.
Score basis:Clear source · Low risk signals · Universal