--- title: "Anthropic-Cybersecurity-Skills vs Awesome-LLMSecOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mukul975-anthropic-cybersecurity-skills-vs-wearetyomsmnv-awesome-llmsecops" tools: ["mukul975-anthropic-cybersecurity-skills", "wearetyomsmnv-awesome-llmsecops"] --- # Anthropic-Cybersecurity-Skills vs Awesome-LLMSecOps *GraphCanon updated Aug 16, 2026* ## Verdict Pick Anthropic-Cybersecurity-Skills if anthropic-Cybersecurity-Skills is a comprehensive repository of 817 structured cybersecurity skills mapped across six industry frameworks, making it highly versatile for various AI platforms and security needs; pick Awesome-LLMSecOps if awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. [Anthropic-Cybersecurity-Skills](https://mahipal.engineer/Anthropic-Cybersecurity-Skills/) reports 28k GitHub stars, 3.4k forks, and 46 open issues, last pushed Aug 8, 2026. [Awesome-LLMSecOps](https://github.com/wearetyomsmnv/Awesome-LLMSecOps) has 150 stars, 63 forks, and 11 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [Anthropic-Cybersecurity-Skills's repository](https://github.com/mukul975/Anthropic-Cybersecurity-Skills) and [Awesome-LLMSecOps's repository](https://github.com/wearetyomsmnv/Awesome-LLMSecOps). | | [Anthropic-Cybersecurity-Skills](/tools/mukul975-anthropic-cybersecurity-skills.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Tagline | 817 structured cybersecurity skills for AI agents | Curated security resources for LLM operations | | Stars | 27,958 | 150 | | Forks | 3,401 | 63 | | Open issues | 46 | 11 | | Language | Python | HTML | | Adopt for | Anthropic-Cybersecurity-Skills is a comprehensive repository of 817 structured cybersecurity skills mapped across six industry frameworks, making it highly versatile for various AI platforms and security needs. | Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Anthropic-Cybersecurity-Skills](/tools/mukul975-anthropic-cybersecurity-skills.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 8d | 4d | | Open issues (now) | 46 | 11 | | Stars delta | +2.3k (30d) | Unknown | | Open issues delta | +6 (30d) | Unknown | | Full report | [trust report](/tools/mukul975-anthropic-cybersecurity-skills/trust.md) | [trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust.md) | ## Decision facts: Anthropic-Cybersecurity-Skills - **Pricing:** freemium - Available under the Apache 2.0 license, ensuring free access and modification but without guaranteeing commercial support. - **Requirements:** Min 4 GB RAM; Supports integration with over 20 platforms including Claude Code and GitHub Copilot; Requires basic understanding of cybersecurity frameworks for optimal use - **Adopt for:** Anthropic-Cybersecurity-Skills is a comprehensive repository of 817 structured cybersecurity skills mapped across six industry frameworks, making it highly versatile for various AI platforms and security needs. ## Decision facts: Awesome-LLMSecOps - **Adopt for:** Awesome-LLMSecOps is a curated list that emphasizes practical security implementation for the operations of large language models. ## Choose when ### Choose Anthropic-Cybersecurity-Skills if… - Anthropic-Cybersecurity-Skills is primarily Python; Awesome-LLMSecOps is HTML. - Pricing: Available under the Apache 2.0 license, ensuring free access and modification but without guaranteeing commercial support.. - Requirements: Min 4 GB RAM; Supports integration with over 20 platforms including Claude Code and GitHub Copilot; Requires basic understanding of cybersecurity frameworks for optimal use. - Tags unique to Anthropic-Cybersecurity-Skills: ai-agents, cybersecurity, mitre-attack, nist-csf. - - Use when you require integration with multiple cybersecurity frameworks like MITRE ATT&CK, NIST CSF 2.0, and others, providing a robust foundation for skill-based operations. ### Choose Awesome-LLMSecOps if… - Awesome-LLMSecOps is primarily HTML; Anthropic-Cybersecurity-Skills is Python. - Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, prompt-injection. - Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation ## When NOT to use Anthropic-Cybersecurity-Skills - - Avoid if your project specifically requires skills mapped exclusively to a single framework not among the six supported by Anthropic-Cybersecurity-Skills. - - Not suitable for projects that do not align with or benefit from the agentskills.io standard implementation, as it might limit customization options. ## When NOT to use Awesome-LLMSecOps - Looking for extensive academic references or ArXiv papers in descriptions - Require real-time interactive tools rather than curated static lists of resources ## Common questions ### What is the difference between Anthropic-Cybersecurity-Skills and Awesome-LLMSecOps? Anthropic-Cybersecurity-Skills: 817 structured cybersecurity skills for AI agents. Awesome-LLMSecOps: Curated security resources for LLM operations. See the comparison table for live GitHub stats and shared categories. ### When should I choose Anthropic-Cybersecurity-Skills over Awesome-LLMSecOps? Choose Anthropic-Cybersecurity-Skills over Awesome-LLMSecOps when Anthropic-Cybersecurity-Skills is primarily Python; Awesome-LLMSecOps is HTML; Pricing: Available under the Apache 2.0 license, ensuring free access and modification but without guaranteeing commercial support.; Requirements: Min 4 GB RAM; Supports integration with over 20 platforms including Claude Code and GitHub Copilot; Requires basic understanding of cybersecurity frameworks for optimal use; Tags unique to Anthropic-Cybersecurity-Skills: ai-agents, cybersecurity, mitre-attack, nist-csf; - Use when you require integration with multiple cybersecurity frameworks like MITRE ATT&CK, NIST CSF 2.0, and others, providing a robust foundation for skill-based operations. ### When should I choose Awesome-LLMSecOps over Anthropic-Cybersecurity-Skills? Choose Awesome-LLMSecOps over Anthropic-Cybersecurity-Skills when Awesome-LLMSecOps is primarily HTML; Anthropic-Cybersecurity-Skills is Python; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, llm-red-teaming, prompt-injection; Need a specialized focus on LLM-specific security threats like recursive pollution and prompt manipulation. ### When should I avoid Anthropic-Cybersecurity-Skills? - Avoid if your project specifically requires skills mapped exclusively to a single framework not among the six supported by Anthropic-Cybersecurity-Skills. - Not suitable for projects that do not align with or benefit from the agentskills.io standard implementation, as it might limit customization options. ### When should I avoid Awesome-LLMSecOps? Looking for extensive academic references or ArXiv papers in descriptions Require real-time interactive tools rather than curated static lists of resources ### Is Anthropic-Cybersecurity-Skills or Awesome-LLMSecOps more popular on GitHub? Anthropic-Cybersecurity-Skills has more GitHub stars (27,958 vs 150). Stars measure visibility, not whether either tool fits your constraints. ### Are Anthropic-Cybersecurity-Skills and Awesome-LLMSecOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Anthropic-Cybersecurity-Skills or Awesome-LLMSecOps? GraphCanon lists graph-backed alternatives at [Anthropic-Cybersecurity-Skills alternatives](/tools/mukul975-anthropic-cybersecurity-skills/alternatives) and [Awesome-LLMSecOps alternatives](/tools/wearetyomsmnv-awesome-llmsecops/alternatives) ([Anthropic-Cybersecurity-Skills markdown twin](/tools/mukul975-anthropic-cybersecurity-skills/alternatives.md), [Awesome-LLMSecOps markdown twin](/tools/wearetyomsmnv-awesome-llmsecops/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/mukul975-anthropic-cybersecurity-skills-vs-wearetyomsmnv-awesome-llmsecops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Anthropic-Cybersecurity-Skills or Awesome-LLMSecOps? Anthropic-Cybersecurity-Skills: Active. Awesome-LLMSecOps: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Anthropic-Cybersecurity-Skills and Awesome-LLMSecOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Anthropic-Cybersecurity-Skills trust report](/tools/mukul975-anthropic-cybersecurity-skills/trust); [Awesome-LLMSecOps trust report](/tools/wearetyomsmnv-awesome-llmsecops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mukul975-anthropic-cybersecurity-skills`](/api/graphcanon/graph?tool=mukul975-anthropic-cybersecurity-skills) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_