Gram
Repository: gram
Author: speakeasy-api · Source status: Clear source
Score 82Clear source
Manual reviewSecurely scale AI usage across your organization.
Score basis：Clear source · Low risk signals · Universal
Author unclaimed
Universal
226
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
agentic-ai-apis
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
agentic-ai-engineering
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | agentic-ai-apis | mcp-for-beginners | agentic-ai-engineering |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Threat tags | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, memory context poisoning | prompt injection, tool poisoning, unexpected code execution |
Evidence confidence | 65% | 67% | 68% |
| Source & provenance | |||
Provenance | cporter202/agentic-ai-apis | microsoft/mcp-for-beginners | agenticloops-ai/agentic-ai-engineering |
Freshness | 2026-04-08 | 2026-04-08 | 2026-04-07 |
| Risk & trust | |||
Trust score | 79 | 85 | 82 |
| Community | |||
Stars | 156 | 15.8K | 49 |
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
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: OpenTokenMonitor
Author: Hitheshkaranth · Source status: Clear source
OpenTokenMonitor is a lightweight, local-first desktop widget for tracking AI CLI usage across Claude, Codex, and Gemini.
Score basis：Clear source · Risk needs review · Universal