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
Repository: AgentEval
Author: AgentEvalHQ · Source status: Clear source
AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (MAF) and Microsoft.Ex
Score basis：Clear source · Low risk signals · Universal
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
eval-tool
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
Promet
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
pythagora
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | eval-tool | Promet | pythagora |
|---|---|---|---|
| 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 | Pythagora-io/eval-tool | Tripadh/Promet | Pythagora-io/pythagora |
Category | Dev & Engineering | Dev & Engineering | Automation & Workflows |
Freshness | |||
| Risk & trust | |||
Trust score | 60 | 82 | 92 |
Audit signals | No explicit signals | No explicit signals | metadata-only |
| Install & compatibility | |||
Install friction | 40 | 40 | 75 |
| Community | |||
Stars | 12 | 4 | 1.8K |
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
2024-12-17 |
2026-04-02 |
2026-04-14 |
Permission hints |
|---|
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
repository clone |
