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
pi-mono
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
cline
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
firecrawl
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | pi-mono | cline | firecrawl |
|---|---|---|---|
| Source & provenance | |||
Provenance | badlogic/pi-mono | cline/cline | firecrawl/firecrawl |
| Community | |||
Stars | 35.3K | 60.2K | 108.8K |
Repository: awesome-design-md
Author: VoltAgent · Source status: Clear source
A collection of DESIGN.md files inspired by popular brand design systems.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: awesome-llm-apps
Author: Shubhamsaboo · Source status: Clear source
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
