Anthropic-Cybersecurity-Skills vs llama_index
The two are close in size: 32.2k stars for Anthropic-Cybersecurity-Skills, 52.0k for llama_index. Over the days we have tracked them Anthropic-Cybersecurity-Skills moved +40.1% and llama_index +2%, so Anthropic-Cybersecurity-Skills is growing faster right now.
llama_index leads on customization and model freedom. Anthropic-Cybersecurity-Skills does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Maps 817 structured cybersecurity skills to six major industry frameworks (e.g., MITRE ATT&CK and MITRE F3), providing practitioner-grade, cross-framework workflows for AI agents.
- Stars
- 32.2k
- Tracked growth
- +40.1%
- Maturity
- ●●●●●
- Last commit
- 5d ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Free to clone; your model/API costs
A data-first RAG framework focused on document agents and agentic OCR with extensive indexing, retrieval, and 300+ integrations.
- Stars
- 52.0k
- Tracked growth
- +2%
- Maturity
- ●●●●●
- Last commit
- 6h ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API key (or optional LlamaParse cloud)
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | Anthropic-Cybersecurity-Skills | llama_index |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Indexed frontmatter + selected skills | ●●●●● Whole-data ingestion |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Matching + verification | ●●●●● Rerankers + filters |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Editable skill YAMLs | ●●●●● Highly extensible |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Local model support |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Multi-provider support | ●●●●● BYO models & keys |
Setup ease What it takes to get a first useful run out of it. | ●●●●● One-command add | ●●●●● Config + API key |
Which one to pick
Pick Anthropic-Cybersecurity-Skills if…
Comprehensive framework coverage — choose this when you need a production-ready, multi-framework cybersecurity skills library to plug directly into agents.
Pick llama_index if…
Document-agent focused — pick LlamaIndex when you need a flexible, extensible RAG/document-agent/OCR framework with strong local model and integration support.
- Customization: Highly extensible (5/5 against 3/5)
- Model freedom: BYO models & keys (5/5 against 3/5)
What people want from each one
mukul975/Anthropic-Cybersecurity-Skills
Hacker News: Anthropic-Cybersecurity-Skills:817 structured cybersecurity skills for AI agents drew 5 points and 0 comments.
run-llama/llama_index
Questions people ask
Is Anthropic-Cybersecurity-Skills better than llama_index?
llama_index leads on customization and model freedom. Anthropic-Cybersecurity-Skills does not take any axis by a clear margin. Anthropic-Cybersecurity-Skills is worth picking when comprehensive framework coverage — choose this when you need a production-ready, multi-framework cybersecurity skills library to plug directly into agents.
Which of Anthropic-Cybersecurity-Skills and llama_index keeps my code private?
Anthropic-Cybersecurity-Skills: Self-hostable (4/5). llama_index: Local model support (5/5).
What does each one cost to run?
Anthropic-Cybersecurity-Skills: Free to clone; your model/API costs. llama_index: Your API key (or optional LlamaParse cloud).
Full profiles: mukul975/Anthropic-Cybersecurity-Skills and run-llama/llama_index. Everything else in RAG & retrieval.