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
comfyui_LLM_party
Execution risk:High
Threat tags:prompt injection, tool poisoning, unexpected code execution
Control gaps:missing license, broad permissions, shell without guardrails
tool_calling_api
Execution risk:High
Threat tags:prompt injection, tool poisoning, unexpected code execution
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
| Dimension | comfyui_LLM_party | tool_calling_api | opencode-glm-quota |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Threat tags | prompt injection, tool poisoning, unexpected code execution | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, data exfiltration, human approval gap |
Evidence confidence | 67% | 67% | 65% |
| Source & provenance | |||
Provenance | heshengtao/comfyui_LLM_party | Shuyib/tool_calling_api | guyinwonder168/opencode-glm-quota |
Freshness | 2026-03-09 | 2026-03-28 | 2026-04-02 |
| Risk & trust | |||
Trust score | 76 | 82 | 82 |
| Community | |||
Stars | 2.2K | 20 | 12 |
Repository: ultimate-prompt-engineering-playbook
Author: amerob · Source status: Clear source
A collection of 114 notebooks covering modern prompt engineering techniques.
Score basis:Clear source · Low risk signals · Universal
Repository: mcp-for-beginners
Author: microsoft · Source status: Clear source
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python.
Score basis:Clear source · Low risk signals · Universal