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
Repository: LLMs-from-scratch
Author: rasbt · Source status: Clear source
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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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
awesome-gemini-prompts
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
ArcGIS-JavaScript-AI-Component
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
raptor
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | awesome-gemini-prompts | ArcGIS-JavaScript-AI-Component | raptor |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 92 · Manual review | 82 · Manual review | 82 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal |
Execution risk | High | High | High |
Threat tags | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, identity privilege abuse, data exfiltration | prompt injection, tool poisoning, unexpected code execution |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Secrets, Command | Permission review, Network, Command |
Evidence completeness | 67% | 67% | 67% |
| Source & provenance | |||
Provenance | langgptai/awesome-gemini-prompts | ralouta/ArcGIS-JavaScript-AI-Component | gadievron/raptor |
Category | Automation & Workflows | Dev & Engineering | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | needs credentials | No explicit signals |
Permission hints | repository clone | repository clone, local runtime dependencies | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 440 | 4 | 1.9K |
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: Awesome-LLM
Author: Hannibal046 · Source status: Clear source
Awesome-LLM: a curated list of Large Language Model
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: rtk
Author: rtk-ai · Source status: Clear source
CLI proxy that reduces LLM token consumption by 60-90% on common dev commands.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: llm-course
Author: mlabonne · Source status: Clear source
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
2025-12-08 |
2026-03-30 |
2026-04-08 |
75 |
55 |
40 |
