Agentic Ai Engineering
Repository: agentic-ai-engineering
Author: agenticloops-ai · Source status: Clear source
Hands-on tutorials for building AI agents from scratch.
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
agentlens
Execution risk:High
Threat tags:prompt injection, tool poisoning, unexpected code execution
Control gaps:missing license, broad permissions, shell without guardrails
fossology
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:broad permissions, shell without guardrails, network without allowlist
mcpcan
Execution risk:High
Threat tags:unexpected code execution, identity privilege abuse, data exfiltration
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | agentlens | fossology | mcpcan |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Audit grade | D · Limited evidence | C · Review first | C · Review first |
Threat tags | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, identity privilege abuse, data exfiltration |
Control gaps | missing license, broad permissions, shell without guardrails | broad permissions, shell without guardrails, network without allowlist | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Secrets, Command | Permission review, Network, Command | Permission review, Network, Secrets, Command |
Evidence confidence | 69% | 67% | 67% |
| Source & provenance | |||
Provenance | agenticloops-ai/agentlens | fossology/fossology | Kymo-MCP/mcpcan |
Freshness | 2026-03-16 | 2026-04-06 | 2026-04-03 |
| Risk & trust | |||
Trust score | 77 | 81 | 81 |
Audit signals | |||
| Install & compatibility | |||
Install friction | 55 | 50 | 55 |
| Community | |||
Stars | 9 | 980 | 717 |
Repository: project-nova
Author: PradeepaRW · Source status: Clear source
A multi-agent AI architecture that connects 25+ specialized agents through n8n and MCP servers.
Score basis:Clear source · Risk needs review · Universal
Repository: mcp-context-forge
Author: IBM · Source status: Clear source
An AI Gateway, registry, and proxy that sits in front of any MCP, A2A, or REST/gRPC APIs, exposing a unified endpoint with centralized discovery, guardrails and management.
Score basis:Clear source · Low risk signals · Universal
network access, runs shell |
needs credentials, network access |