Gram
Repository: gram
Author: speakeasy-api · Source status: Clear source
Securely scale AI usage across your organization.
Score basis:Clear source · High execution risk · Universal · Evidence completeness 65%
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.
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
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
scalekit-sdk-python
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
inkbox
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | scalekit-sdk-python | mcp-for-beginners | inkbox |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 79 · Manual review | 85 · Manual review | 82 · Manual review |
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 completeness | 65% | 67% | 65% |
| Source & provenance | |||
Provenance | scalekit-inc/scalekit-sdk-python | microsoft/mcp-for-beginners | inkbox-ai/inkbox |
Freshness | 2026-04-03 | 2026-04-08 | 2026-04-07 |
| Community | |||
Stars | 7 | 15.8K | 8 |
Repository: agentic-ai-engineering
Author: agenticloops-ai · Source status: Clear source
Hands-on tutorials for building AI agents from scratch.
Score basis:Clear source · High execution risk · Universal · Evidence completeness 68%
Repository: NVIDIA-Nemotron-3-Super
Author: cobusgreyling · Source status: Clear source
Controllable reasoning demos for NVIDIA Nemotron 3 Super (120B/12B MoE) — chat UI, CLI, API server, tool calling, budget sweep, and adaptive routing Topics: gradio, llm, mixture-of-experts, moe, nemotron, nim, nvidia, re
Score basis:Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: ccNexus
Author: lich0821 · Source status: Clear source
Intelligent API gateway for Claude Code and Codex CLI - rotate endpoints, monitor usage, and seamlessly integrate OpenAI, Gemini, and other platforms.
Score basis:Clear source · High execution risk · Universal · Evidence completeness 65%