--- title: "instruct-eval vs Anthropic-Cybersecurity-Skills" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-mukul975-anthropic-cybersecurity-skills" tools: ["declare-lab-instruct-eval", "mukul975-anthropic-cybersecurity-skills"] --- # instruct-eval vs Anthropic-Cybersecurity-Skills *GraphCanon updated Jul 12, 2026* ## Verdict Pick instruct-eval when tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, instruction-following; pick Anthropic-Cybersecurity-Skills when pricing: Available under the Apache 2.0 license, ensuring free access and modification but without guaranteeing commercial support.. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 552 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [Anthropic-Cybersecurity-Skills](https://mahipal.engineer/Anthropic-Cybersecurity-Skills/) has 25k stars, 3.1k forks, and 35 open issues, last pushed Jun 26, 2026. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [Anthropic-Cybersecurity-Skills's repository](https://github.com/mukul975/Anthropic-Cybersecurity-Skills). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [Anthropic-Cybersecurity-Skills](/tools/mukul975-anthropic-cybersecurity-skills.md) | | --- | --- | --- | | Tagline | Code for evaluating instruction-tuned language models like Alpaca and Flan-T5 | 817 structured cybersecurity skills for AI agents | | Stars | 552 | 25,282 | | Forks | 45 | 3,060 | | Open issues | 24 | 35 | | Language | Python | Python | | 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. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [Anthropic-Cybersecurity-Skills](/tools/mukul975-anthropic-cybersecurity-skills.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 853d | 14d | | Open issues (now) | 24 | 35 | | Owner type | Organization | User | | Security scan | 83 low (83 low) | No MCP manifest | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/mukul975-anthropic-cybersecurity-skills/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. ## Choose when ### Choose instruct-eval if… - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, instruction-following. - Leaner open-issue backlog (24). ### Choose Anthropic-Cybersecurity-Skills if… - 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. - Also covers AI Agents. - - 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 NOT to use instruct-eval - Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on instruct-eval. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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. ## Common questions ### What is the difference between instruct-eval and Anthropic-Cybersecurity-Skills? instruct-eval: Code for evaluating instruction-tuned language models like Alpaca and Flan-T5. Anthropic-Cybersecurity-Skills: 817 structured cybersecurity skills for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose instruct-eval over Anthropic-Cybersecurity-Skills? Choose instruct-eval over Anthropic-Cybersecurity-Skills when Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, instruction-following; Leaner open-issue backlog (24). ### When should I choose Anthropic-Cybersecurity-Skills over instruct-eval? Choose Anthropic-Cybersecurity-Skills over instruct-eval when 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; Also covers AI Agents; - 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 avoid instruct-eval? Last GitHub push was 854 days ago (dormant maintenance, Mar 10, 2024). Validate activity before betting a new project on instruct-eval. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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. ### Is instruct-eval or Anthropic-Cybersecurity-Skills more popular on GitHub? Anthropic-Cybersecurity-Skills has more GitHub stars (25,282 vs 552). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and Anthropic-Cybersecurity-Skills open source? Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, Anthropic-Cybersecurity-Skills: Apache-2.0). ### Where can I find alternatives to instruct-eval or Anthropic-Cybersecurity-Skills? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [Anthropic-Cybersecurity-Skills alternatives](/tools/mukul975-anthropic-cybersecurity-skills/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [Anthropic-Cybersecurity-Skills markdown twin](/tools/mukul975-anthropic-cybersecurity-skills/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/declare-lab-instruct-eval-vs-mukul975-anthropic-cybersecurity-skills.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or Anthropic-Cybersecurity-Skills? instruct-eval: Dormant. Anthropic-Cybersecurity-Skills: 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 instruct-eval and Anthropic-Cybersecurity-Skills? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [Anthropic-Cybersecurity-Skills trust report](/tools/mukul975-anthropic-cybersecurity-skills/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=declare-lab-instruct-eval`](/api/graphcanon/graph?tool=declare-lab-instruct-eval) - 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/_