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: claude-cookbooks
Author: anthropics · Source status: Clear source
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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
claude-quickstarts
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Buddy
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
agent-memory-systems
Execution risk：High
Threat tags：data exfiltration, memory context poisoning, human approval gap
Control gaps：missing license, network without allowlist, no human approval
| Dimension | claude-quickstarts | Buddy | agent-memory-systems |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 92 · Manual review | 81 · Manual review | 79 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, memory context poisoning | data exfiltration, memory context poisoning, human approval gap |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, network without allowlist, no human approval |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Network, Command |
Evidence completeness | 65% | 67% | 65% |
| Source & provenance | |||
Provenance | anthropics/claude-quickstarts | is-leeroy-jenkins/Buddy | davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/agent-memory-systems |
Category | Automation & Workflows | Knowledge & RAG | Knowledge & RAG |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | network access, runs shell | No explicit signals |
Permission hints | repository clone | repository clone, local runtime dependencies | registry access, remote metadata pull, runtime dependencies may be required |
| Install & compatibility | |||
Supported tools | Universal | Universal | Claude, Codex, Cursor, Universal |
Install method | script-backed | script-backed | registry-install |
Install friction | |||
| Community | |||
Stars | 16.1K | 6 | 19.9K |
Repository: claude-code-router
Author: musistudio · Source status: Clear source
Use Claude Code as the foundation for coding infrastructure, allowing you to decide how to interact with the model while enjoying updates from Anthropic.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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%
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: 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: dapr
Author: dapr · Source status: Clear source
Dapr is a portable runtime for building distributed applications across cloud and edge, combining event-driven architecture with workflow orchestration.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
2026-02-06 |
2026-04-02 |
2026-02-04 |
75 |
50 |
50 |
