--- title: "awesome-claude-skills vs CoDA-Bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/composiohq-awesome-claude-skills-vs-ruc-datalab-coda-bench" tools: ["composiohq-awesome-claude-skills", "ruc-datalab-coda-bench"] --- # awesome-claude-skills vs CoDA-Bench *GraphCanon updated Jul 15, 2026* ## Verdict Pick awesome-claude-skills when pricing: The repository's license is under Apache 2.0 for the overall content, but individual skills may have varying licensing terms which should be checked individually within their respective folders.; pick CoDA-Bench when tags unique to CoDA-Bench: agent, agentic, agentic-ai, ai. [awesome-claude-skills](https://github.com/ComposioHQ/awesome-claude-skills) reports 67k GitHub stars, 7.6k forks, and 974 open issues, last pushed May 22, 2026. [CoDA-Bench](https://coda-bench.github.io/) has 39 stars, 0 forks, and 0 open issues, last pushed Jun 17, 2026. Figures are from public GitHub metadata via [awesome-claude-skills's repository](https://github.com/ComposioHQ/awesome-claude-skills) and [CoDA-Bench's repository](https://github.com/ruc-datalab/CoDA-Bench). | | [awesome-claude-skills](/tools/composiohq-awesome-claude-skills.md) | [CoDA-Bench](/tools/ruc-datalab-coda-bench.md) | | --- | --- | --- | | Tagline | A curated list of awesome Claude Skills for customizing AI workflows | CoDA-Bench is a benchmark for code agents on data-intensive tasks. 🎈代码智能体能搞定数据密集型任务吗? | | Stars | 67,447 | 39 | | Forks | 7,586 | 0 | | Open issues | 974 | 0 | | Language | Python | Python | | Adopt for | awesome-claude-skills is a curated list that provides resources and tools for customizing workflows using Claude AI. | - | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | AI Agents, Developer Tools | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-claude-skills](/tools/composiohq-awesome-claude-skills.md) | [CoDA-Bench](/tools/ruc-datalab-coda-bench.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 50d | 28d | | Open issues (now) | 974 | 0 | | Full report | [trust report](/tools/composiohq-awesome-claude-skills/trust.md) | [trust report](/tools/ruc-datalab-coda-bench/trust.md) | ## Decision facts: awesome-claude-skills - **Pricing:** unknown - The repository's license is under Apache 2.0 for the overall content, but individual skills may have varying licensing terms which should be checked individually within their respective folders. - **Adopt for:** awesome-claude-skills is a curated list that provides resources and tools for customizing workflows using Claude AI. ## Choose when ### Choose awesome-claude-skills if… - Pricing: The repository's license is under Apache 2.0 for the overall content, but individual skills may have varying licensing terms which should be checked individually within their respective folders.. - Tags unique to awesome-claude-skills: agent-skills, automation, claude code, composio. - Also covers Developer Tools. - When you are looking to customize and extend the capabilities of Claude AI through various skills and plugins. ### Choose CoDA-Bench if… - Tags unique to CoDA-Bench: agent, agentic, agentic-ai, ai. - Also covers LLM Frameworks, Vector Databases. - More recently updated (last pushed Jun 17, 2026). ## When NOT to use awesome-claude-skills - Avoid using if your primary focus is on general-purpose automation that doesn't require integration with Claude AI or its ecosystem. - Not recommended if specific automation tasks do not benefit from customization through the provided Claude Skills, or you prefer tools that operate independently without dependency on a particular AI. ## When NOT to use CoDA-Bench - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between awesome-claude-skills and CoDA-Bench? awesome-claude-skills: A curated list of awesome Claude Skills for customizing AI workflows. CoDA-Bench: CoDA-Bench is a benchmark for code agents on data-intensive tasks. 🎈代码智能体能搞定数据密集型任务吗?. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-claude-skills over CoDA-Bench? Choose awesome-claude-skills over CoDA-Bench when Pricing: The repository's license is under Apache 2.0 for the overall content, but individual skills may have varying licensing terms which should be checked individually within their respective folders.; Tags unique to awesome-claude-skills: agent-skills, automation, claude code, composio; Also covers Developer Tools; When you are looking to customize and extend the capabilities of Claude AI through various skills and plugins. ### When should I choose CoDA-Bench over awesome-claude-skills? Choose CoDA-Bench over awesome-claude-skills when Tags unique to CoDA-Bench: agent, agentic, agentic-ai, ai; Also covers LLM Frameworks, Vector Databases; More recently updated (last pushed Jun 17, 2026). ### When should I avoid awesome-claude-skills? Avoid using if your primary focus is on general-purpose automation that doesn't require integration with Claude AI or its ecosystem. Not recommended if specific automation tasks do not benefit from customization through the provided Claude Skills, or you prefer tools that operate independently without dependency on a particular AI. ### When should I avoid CoDA-Bench? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is awesome-claude-skills or CoDA-Bench more popular on GitHub? awesome-claude-skills has more GitHub stars (67,447 vs 39). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-claude-skills and CoDA-Bench open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-claude-skills or CoDA-Bench? GraphCanon lists graph-backed alternatives at [awesome-claude-skills alternatives](/tools/composiohq-awesome-claude-skills/alternatives) and [CoDA-Bench alternatives](/tools/ruc-datalab-coda-bench/alternatives) ([awesome-claude-skills markdown twin](/tools/composiohq-awesome-claude-skills/alternatives.md), [CoDA-Bench markdown twin](/tools/ruc-datalab-coda-bench/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/composiohq-awesome-claude-skills-vs-ruc-datalab-coda-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-claude-skills or CoDA-Bench? awesome-claude-skills: Steady. CoDA-Bench: 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 awesome-claude-skills and CoDA-Bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-claude-skills trust report](/tools/composiohq-awesome-claude-skills/trust); [CoDA-Bench trust report](/tools/ruc-datalab-coda-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=composiohq-awesome-claude-skills`](/api/graphcanon/graph?tool=composiohq-awesome-claude-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/_