Contributor Recognition System With Attribution And Metrics

via “contribution system with point-based incentives for task and model additions”

Embedding model benchmark — 8 tasks, 112 languages, the standard for comparing embeddings.

Unique: Contribution system awards points based on contribution type and scope (e.g., new task type, multilingual task, large dataset). Points are tracked and displayed on contributor profiles, providing recognition and incentivizing community contributions. This design enables MTEB to scale beyond core maintainers by leveraging community contributions.

vs others: Point-based incentive system vs. purely volunteer contributions, providing recognition and motivation for community contributors. Contribution tracking enables transparency and recognition of community impact.

Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.

Unique: Implements automated contributor recognition by extracting Git history and maintaining a contributor database (.all-contributorsrc), enabling scalable community recognition without manual curation. Metrics track contribution volume and community impact.

vs others: More scalable than manual recognition because attribution is automated; more transparent than ad-hoc recognition because metrics are tracked and reported.

via “community-contribution-workflow-with-attribution”

🚀 An awesome list of curated Nano Banana pro prompts and examples. Your go-to resource for mastering prompt engineering and exploring the creative potential of the Nano banana pro(Nano banana 2) AI image model.

Unique: Treats attribution as a first-class requirement in the contribution workflow, not an afterthought — every prompt must include source credit, and the contribution template explicitly asks for creator name and platform source. This is enforced through documentation guidelines and peer review, creating a culture of intellectual honesty that's rare in prompt repositories.

vs others: More transparent and community-friendly than proprietary prompt marketplaces (which may not credit original creators or may claim ownership of community submissions), but slower and more friction-heavy than centralized platforms with dedicated editorial teams that can rapidly curate and publish new content.

via “contributor attribution and community-driven prompt curation”

| [Hugging Face Dataset](https://huggingface.co/datasets/fka/prompts.chat) |

Unique: Uses GitHub username attribution to make prompt contributions transparent and discoverable, enabling community members to identify and follow prompt engineers whose work they value. This approach leverages GitHub's social features (user profiles, contribution history) to support community curation without requiring a dedicated platform.

vs others: More transparent than proprietary prompt marketplaces because contributions are publicly visible and attributable, but less structured than formal open-source projects because it lacks contribution guidelines, code review processes, or quality assurance mechanisms.