--- title: "lm-evaluation-harness vs Anthropic-Cybersecurity-Skills" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eleutherai-lm-evaluation-harness-vs-mukul975-anthropic-cybersecurity-skills" tools: ["eleutherai-lm-evaluation-harness", "mukul975-anthropic-cybersecurity-skills"] --- # lm-evaluation-harness vs Anthropic-Cybersecurity-Skills *GraphCanon updated Jul 11, 2026* ## Verdict Pick lm-evaluation-harness if lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend; 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. [lm-evaluation-harness](https://www.eleuther.ai) reports 13k GitHub stars, 3.4k forks, and 907 open issues, last pushed Jun 24, 2026. [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 [lm-evaluation-harness's repository](https://github.com/EleutherAI/lm-evaluation-harness) and [Anthropic-Cybersecurity-Skills's repository](https://github.com/mukul975/Anthropic-Cybersecurity-Skills). | | [lm-evaluation-harness](/tools/eleutherai-lm-evaluation-harness.md) | [Anthropic-Cybersecurity-Skills](/tools/mukul975-anthropic-cybersecurity-skills.md) | | --- | --- | --- | | Tagline | A framework for few-shot evaluation of language models. | 817 structured cybersecurity skills for AI agents | | Stars | 13,253 | 25,282 | | Forks | 3,404 | 3,060 | | Open issues | 907 | 35 | | Language | Python | Python | | Adopt for | lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend. | 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 | MIT | Apache-2.0 | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lm-evaluation-harness](/tools/eleutherai-lm-evaluation-harness.md) | [Anthropic-Cybersecurity-Skills](/tools/mukul975-anthropic-cybersecurity-skills.md) | | --- | --- | --- | | Days since push | 16d | 14d | | Open issues (now) | 907 | 35 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/eleutherai-lm-evaluation-harness/trust.md) | [trust report](/tools/mukul975-anthropic-cybersecurity-skills/trust.md) | ## Decision facts: lm-evaluation-harness - **Adopt for:** lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend. ## 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 lm-evaluation-harness if… - License: lm-evaluation-harness is MIT, Anthropic-Cybersecurity-Skills is Apache-2.0. - Tags unique to lm-evaluation-harness: data-parallelism, evaluation-framework, expert-parallelism, language-model. - - When you need to evaluate large language models across multiple GPUs in data or tensor parallel configurations. ### Choose Anthropic-Cybersecurity-Skills if… - License: Anthropic-Cybersecurity-Skills is Apache-2.0, lm-evaluation-harness is MIT. - 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 lm-evaluation-harness - - If your evaluation setup requires pipeline parallelism not currently supported by this framework. ## 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 lm-evaluation-harness and Anthropic-Cybersecurity-Skills? lm-evaluation-harness: A framework for few-shot evaluation of language models.. 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 lm-evaluation-harness over Anthropic-Cybersecurity-Skills? Choose lm-evaluation-harness over Anthropic-Cybersecurity-Skills when License: lm-evaluation-harness is MIT, Anthropic-Cybersecurity-Skills is Apache-2.0; Tags unique to lm-evaluation-harness: data-parallelism, evaluation-framework, expert-parallelism, language-model; - When you need to evaluate large language models across multiple GPUs in data or tensor parallel configurations. ### When should I choose Anthropic-Cybersecurity-Skills over lm-evaluation-harness? Choose Anthropic-Cybersecurity-Skills over lm-evaluation-harness when License: Anthropic-Cybersecurity-Skills is Apache-2.0, lm-evaluation-harness is MIT; 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 lm-evaluation-harness? - If your evaluation setup requires pipeline parallelism not currently supported by this framework. ### 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 lm-evaluation-harness or Anthropic-Cybersecurity-Skills more popular on GitHub? Anthropic-Cybersecurity-Skills has more GitHub stars (25,282 vs 13,253). Stars measure visibility, not whether either tool fits your constraints. ### Are lm-evaluation-harness and Anthropic-Cybersecurity-Skills open source? Yes - both are open-source projects on GitHub (lm-evaluation-harness: MIT, Anthropic-Cybersecurity-Skills: Apache-2.0). ### Where can I find alternatives to lm-evaluation-harness or Anthropic-Cybersecurity-Skills? GraphCanon lists graph-backed alternatives at [lm-evaluation-harness alternatives](/tools/eleutherai-lm-evaluation-harness/alternatives) and [Anthropic-Cybersecurity-Skills alternatives](/tools/mukul975-anthropic-cybersecurity-skills/alternatives) ([lm-evaluation-harness markdown twin](/tools/eleutherai-lm-evaluation-harness/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/eleutherai-lm-evaluation-harness-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, lm-evaluation-harness or Anthropic-Cybersecurity-Skills? lm-evaluation-harness: Active. 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 lm-evaluation-harness and Anthropic-Cybersecurity-Skills? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lm-evaluation-harness trust report](/tools/eleutherai-lm-evaluation-harness/trust); [Anthropic-Cybersecurity-Skills trust report](/tools/mukul975-anthropic-cybersecurity-skills/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eleutherai-lm-evaluation-harness`](/api/graphcanon/graph?tool=eleutherai-lm-evaluation-harness) - 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/_