Today's Prompt Engineering: Fastest-Growing Projects — May 08, 2026
Today's Prompt Engineering, we've seen a surge in interest around tools that facilitate more effective and efficient interactions with large language models (LLMs). Developers are seeking ways to refine their prompts to elicit sharper responses, while also exploring industrial-grade prompt engines and template libraries. Our top growth scores reflect this trend, with repositories showcasing innovative approaches to prompt compression, genre-aware writing, and multimodal injection testing.
Yao Open Prompts, with a Growth Score of 22.18 and 1,305 stars, is a comprehensive library of Chinese AI prompts covering various scenarios such as work, study, content creation, marketing, and daily life. Its popularity stems from the growing demand for high-quality, task-specific prompts that can help LLMs better understand user intent.
WRITING.md, boasting a Growth Score of 13.57 and 242 stars, offers a set of rules to craft sharper and more genre-aware text using LLMs. By providing concrete anchors and a self-auditing workflow, this repository is attracting developers seeking to improve the quality and coherence of their AI-generated content.
Freestylefly's awesome-gpt-image-2 has garnered an impressive 4,538 stars and a Growth Score of 11.69, thanks to its extensive collection of industrial-grade prompts and templates for GPT-Image2. This repository serves as a valuable resource for developers looking to harness the power of LLMs in image generation tasks.
Leanctx, with a Growth Score of 10.65 and 60 stars, provides a Python SDK for prompt compression that can reduce token bills by 40-60% without modifying existing code. Its growing popularity reflects the need for cost-effective solutions in production LLM applications.
AI-for-engineering-leaders, featuring a Growth Score of 7.72 and 46 stars, offers practical AI playbooks for engineering leaders to enhance delivery, decision-making, and team productivity. By sharing real workflows, prompts, and systems, this repository is helping leaders navigate the intersection of AI and engineering management.
Prompt-sensei, boasting a Growth Score of 6.54 and 77 stars, acts as a local-first prompt coach for Claude Code and Codex, analyzing prompting habits and improving prompts with explicit user consent. Its growth indicates a desire for more personalized and effective LLM interactions.
Claude-comstyle, with a Growth Score of 5.25 and 27 stars, provides 13 prompts to control how Claude communicates, allowing users to choose their preferred style and reduce noise in AI-generated text. This repository's popularity highlights the importance of customizable communication styles in human-AI collaboration.
Awesome-happy-horse, featuring a Growth Score of 3.69 and 73 stars, is a community-curated resource hub for Happy Horse model prompts, news, benchmarks, and sample outputs. Its growth reflects the growing interest in this specific LLM and the need for shared knowledge and resources within the developer community.
Finally, bordair-multimodal, with a Growth Score of 3.02 and 46 stars, offers an open-source cross-modal and multimodal prompt injection test suite, featuring over 50,500 attack payloads across various modalities. This repository's growth indicates a rising concern for security and robustness in LLM applications.
Yao Open Prompts, with a Growth Score of 22.18 and 1,305 stars, is a comprehensive library of Chinese AI prompts covering various scenarios such as work, study, content creation, marketing, and daily life. Its popularity stems from the growing demand for high-quality, task-specific prompts that can help LLMs better understand user intent.
WRITING.md, boasting a Growth Score of 13.57 and 242 stars, offers a set of rules to craft sharper and more genre-aware text using LLMs. By providing concrete anchors and a self-auditing workflow, this repository is attracting developers seeking to improve the quality and coherence of their AI-generated content.
Freestylefly's awesome-gpt-image-2 has garnered an impressive 4,538 stars and a Growth Score of 11.69, thanks to its extensive collection of industrial-grade prompts and templates for GPT-Image2. This repository serves as a valuable resource for developers looking to harness the power of LLMs in image generation tasks.
Leanctx, with a Growth Score of 10.65 and 60 stars, provides a Python SDK for prompt compression that can reduce token bills by 40-60% without modifying existing code. Its growing popularity reflects the need for cost-effective solutions in production LLM applications.
AI-for-engineering-leaders, featuring a Growth Score of 7.72 and 46 stars, offers practical AI playbooks for engineering leaders to enhance delivery, decision-making, and team productivity. By sharing real workflows, prompts, and systems, this repository is helping leaders navigate the intersection of AI and engineering management.
Prompt-sensei, boasting a Growth Score of 6.54 and 77 stars, acts as a local-first prompt coach for Claude Code and Codex, analyzing prompting habits and improving prompts with explicit user consent. Its growth indicates a desire for more personalized and effective LLM interactions.
Claude-comstyle, with a Growth Score of 5.25 and 27 stars, provides 13 prompts to control how Claude communicates, allowing users to choose their preferred style and reduce noise in AI-generated text. This repository's popularity highlights the importance of customizable communication styles in human-AI collaboration.
Awesome-happy-horse, featuring a Growth Score of 3.69 and 73 stars, is a community-curated resource hub for Happy Horse model prompts, news, benchmarks, and sample outputs. Its growth reflects the growing interest in this specific LLM and the need for shared knowledge and resources within the developer community.
Finally, bordair-multimodal, with a Growth Score of 3.02 and 46 stars, offers an open-source cross-modal and multimodal prompt injection test suite, featuring over 50,500 attack payloads across various modalities. This repository's growth indicates a rising concern for security and robustness in LLM applications.