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
new-api
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
AGiXT
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
llm-app
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | new-api | AGiXT | llm-app |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 66 · Manual review | 68 · Manual review | 65 · 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, memory context poisoning |
Evidence completeness | 65% | 67% | 67% |
| Source & provenance | |||
Provenance | QuantumNous/new-api | Josh-XT/AGiXT | pathwaycom/llm-app |
Category | Automation & Workflows | Operations & Infra | Automation & Workflows |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | runs shell | metadata-only |
Permission hints | repository clone | repository clone, local runtime dependencies | repository clone |
| Install & compatibility | |||
Install friction | 75 | 50 | 75 |
| Community | |||
Stars | 26.6K | 3.2K | 60K |
Repository: openai-cookbook
Author: openai · Source status: Clear source
Examples and guides for using the OpenAI API
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: litellm
Author: BerriAI · Source status: Clear source
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: one-api
Author: songquanpeng · Source status: Clear source
LLM API 管理 & 分发系统，支持 OpenAI、Azure、Anthropic Claude、Google Gemini、DeepSeek、字节豆包、ChatGLM、文心一言、讯飞星火、通义千问、360 智脑、腾讯混元等主流模型，统一 API 适配，可用于 key 管理与二次分发。单可执行文件，提供 Docker 镜像，一键部署，开箱即用。LLM API management & key redistribution syst…
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: FastGPT
Author: labring · Source status: Clear source
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily devel…
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: claude-task-master
Author: eyaltoledano · Source status: Clear source
An AI-powered task-management system you can drop into Cursor, Lovable, Windsurf, Roo, and others.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
2026-04-13 |
2026-04-03 |
2026-01-07 |
