Demogpt
Repository: DemoGPT
Author: melih-unsal · Source status: Clear source
🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.
Score basis:Clear source · Low risk signals · Universal
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
vscode-unify-chat-provider
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
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
ClaudeBar
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | vscode-unify-chat-provider | mcp-for-beginners | ClaudeBar |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Threat tags | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap |
Evidence confidence | 65% | 67% | 65% |
| Source & provenance | |||
Provenance | smallmain/vscode-unify-chat-provider | microsoft/mcp-for-beginners | tddworks/ClaudeBar |
Freshness | 2026-03-30 | 2026-04-08 | 2026-04-01 |
| Risk & trust | |||
Trust score | 79 | 85 | 82 |
| Community | |||
Stars | 333 | 15.8K | 893 |
Repository: raptor
Author: gadievron · Source status: Clear source
Raptor turns Claude Code into a general-purpose AI offensive/defensive security agent.
Score basis:Clear source · Low risk signals · Universal
Repository: entroly
Author: juyterman1000 · Source status: Clear source
Entroly helps AI coding tools like Cursor, Copilot, and Claude Code use the right context from your entire codebase—improving output quality while reducing token usage.
Score basis:Clear source · Risk needs review · Universal
Repository: tool_calling_api
Author: Shuyib · Source status: Clear source
This project demonstrates function-calling with Python and Ollama, utilizing the Africa's Talking API to send airtime and messages to phone numbers using natural language prompts.
Score basis:Clear source · Low risk signals · Universal